diff --git a/PySONIC/core/pneuron.py b/PySONIC/core/pneuron.py
index 23585d4..3f08fc8 100644
--- a/PySONIC/core/pneuron.py
+++ b/PySONIC/core/pneuron.py
@@ -1,596 +1,596 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2017-08-03 11:53:04
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2020-08-28 17:14:49
+# @Last Modified time: 2021-03-02 13:27:17
 
 import abc
 import inspect
 import numpy as np
 
 from .protocols import *
 from .model import Model
 from .lookups import EffectiveVariablesLookup
 from .solvers import EventDrivenSolver
 from .drives import Drive, ElectricDrive
 from ..postpro import detectSpikes, computeFRProfile
 from ..constants import *
 from ..utils import *
 
 
 class PointNeuron(Model):
     ''' Generic point-neuron model interface. '''
 
     tscale = 'ms'     # relevant temporal scale of the model
     simkey = 'ESTIM'  # keyword used to characterize simulations made with this model
     celsius = 36.0    # Temperature (Celsius)
     T = celsius + CELSIUS_2_KELVIN
 
     def __repr__(self):
         return self.__class__.__name__
 
     def copy(self):
         return self.__class__()
 
     def __eq__(self, other):
         if not isinstance(other, PointNeuron):
             return False
         return self.name == other.name
 
     @property
     @classmethod
     @abc.abstractmethod
     def name(cls):
         ''' Neuron name. '''
         raise NotImplementedError
 
     @property
     @classmethod
     @abc.abstractmethod
     def Cm0(cls):
         ''' Neuron's resting capacitance (F/m2). '''
         raise NotImplementedError
 
     @property
     @classmethod
     @abc.abstractmethod
     def Vm0(cls):
         ''' Neuron's resting membrane potential(mV). '''
         raise NotImplementedError
 
     @property
     def Qm0(self):
         return self.Cm0 * self.Vm0 * 1e-3  # C/m2
 
     @property
     def tau_pas(self):
         ''' Passive membrane time constant (s). '''
         return self.Cm0 / self.gLeak
 
     @property
     def meta(self):
         return {'neuron': self.name}
 
     @staticmethod
     def inputs():
         return ElectricDrive.inputs()
 
     @classmethod
     def filecodes(cls, drive, pp):
         return {
             'simkey': cls.simkey,
             'neuron': cls.name,
             'nature': pp.nature,
             **drive.filecodes,
             **pp.filecodes
         }
 
     @classmethod
     def normalizedQm(cls, Qm):
         ''' Compute membrane charge density normalized by resting capacitance.
 
             :param Qm: membrane charge density (Q/m2)
             :return: normalized charge density (mV)
 
          '''
         return Qm / cls.Cm0 * 1e3
 
     @classmethod
     def getPltVars(cls, wl='df["', wr='"]'):
         pltvars = {
             'Qm': {
                 'desc': 'membrane charge density',
                 'label': 'Q_m',
                 'unit': 'nC/cm^2',
                 'factor': 1e5,
                 'bounds': ((cls.Vm0 - 20.0) * cls.Cm0 * 1e2, 60)
             },
 
             'Qm/Cm0': {
                 'desc': 'membrane charge density over resting capacitance',
                 'label': 'Q_m / C_{m0}',
                 'unit': 'mV',
                 'bounds': (-150, 70),
                 'func': f"normalizedQm({wl}Qm{wr})"
             },
 
             'Vm': {
                 'desc': 'membrane potential',
                 'label': 'V_m',
                 'unit': 'mV',
                 'bounds': (-150, 70)
             },
 
             'ELeak': {
                 'constant': 'obj.ELeak',
                 'desc': 'non-specific leakage current resting potential',
                 'label': 'V_{leak}',
                 'unit': 'mV',
                 'ls': '--',
                 'color': 'k'
             }
         }
 
         for cname in cls.getCurrentsNames():
             cfunc = getattr(cls, cname)
             cargs = inspect.getargspec(cfunc)[0][1:]
             pltvars[cname] = {
                 'desc': inspect.getdoc(cfunc).splitlines()[0],
                 'label': f'I_{{{cname[1:]}}}',
                 'unit': 'A/m^2',
                 'factor': 1e-3,
                 'func': f"{cname}({', '.join([f'{wl}{a}{wr}' for a in cargs])})"
             }
             for var in cargs:
                 if var != 'Vm':
                     pltvars[var] = {
                         'desc': cls.states[var],
                         'label': var,
                         'bounds': (-0.1, 1.1)
                     }
 
         pltvars['iNet'] = {
             'desc': inspect.getdoc(getattr(cls, 'iNet')).splitlines()[0],
             'label': 'I_{net}',
             'unit': 'A/m^2',
             'factor': 1e-3,
             'func': f'iNet({wl}Vm{wr}, {wl[:-1]}{cls.statesNames()}{wr[1:]})',
             'ls': '--',
             'color': 'black'
         }
 
         pltvars['dQdt'] = {
             'desc': inspect.getdoc(getattr(cls, 'dQdt')).splitlines()[0],
             'label': 'dQ_m/dt',
             'unit': 'A/m^2',
             'factor': 1e-3,
             'func': f'dQdt({wl}t{wr}, {wl}Qm{wr})',
             'ls': '--',
             'color': 'black'
         }
 
         pltvars['iax'] = {
             'desc': inspect.getdoc(getattr(cls, 'iax')).splitlines()[0],
             'label': 'i_{ax}',
             'unit': 'A/m^2',
             'factor': 1e-3,
             # 'func': f'iax({wl}t{wr}, {wl}Qm{wr}, {wl}Vm{wr}, {wl[:-1]}{cls.statesNames()}{wr[1:]})',
             'ls': '--',
             'color': 'black',
             # 'bounds': (-1e2, 1e2)
         }
 
         pltvars['iCap'] = {
             'desc': inspect.getdoc(getattr(cls, 'iCap')).splitlines()[0],
             'label': 'I_{cap}',
             'unit': 'A/m^2',
             'factor': 1e-3,
             'func': f'iCap({wl}t{wr}, {wl}Vm{wr})'
         }
 
         for rate in cls.rates:
             if 'alpha' in rate:
                 prefix, suffix = 'alpha', rate[5:]
             else:
                 prefix, suffix = 'beta', rate[4:]
             pltvars[rate] = {
                 'label': '\\{}_{{{}}}'.format(prefix, suffix),
                 'unit': 'ms^{-1}',
                 'factor': 1e-3
             }
 
         pltvars['FR'] = {
             'desc': 'riring rate',
             'label': 'FR',
             'unit': 'Hz',
             'factor': 1e0,
             # 'bounds': (0, 1e3),
             'func': f'firingRateProfile({wl[:-2]})'
         }
 
         return pltvars
 
     @classmethod
     def iCap(cls, t, Vm):
         ''' Capacitive current. '''
         dVdt = np.insert(np.diff(Vm) / np.diff(t), 0, 0.)
         return cls.Cm0 * dVdt
 
     @property
     def pltScheme(self):
         pltscheme = {
             'Q_m': ['Qm'],
             'V_m': ['Vm']
         }
         pltscheme['I'] = self.getCurrentsNames() + ['iNet']
         for cname in self.getCurrentsNames():
             if 'Leak' not in cname:
                 key = f'i_{{{cname[1:]}}}\ kin.'
                 cargs = inspect.getargspec(getattr(self, cname))[0][1:]
                 pltscheme[key] = [var for var in cargs if var not in ['Vm', 'Cai']]
 
         return pltscheme
 
     @classmethod
     def statesNames(cls):
         ''' Return a list of names of all state variables of the model. '''
         return list(cls.states.keys())
 
     @classmethod
     @abc.abstractmethod
     def derStates(cls):
         ''' Dictionary of states derivatives functions '''
         raise NotImplementedError
 
     @classmethod
     def getDerStates(cls, Vm, states):
         ''' Compute states derivatives array given a membrane potential and states dictionary '''
         return np.array([cls.derStates()[k](Vm, states) for k in cls.statesNames()])
 
     @classmethod
     @abc.abstractmethod
     def steadyStates(cls):
         ''' Return a dictionary of steady-states functions '''
         raise NotImplementedError
 
     @classmethod
     def getSteadyStates(cls, Vm):
         ''' Compute array of steady-states for a given membrane potential '''
         return np.array([cls.steadyStates()[k](Vm) for k in cls.statesNames()])
 
     @classmethod
     def getDerEffStates(cls, lkp, states):
         ''' Compute effective states derivatives array given lookups and states dictionaries. '''
         return np.array([cls.derEffStates()[k](lkp, states) for k in cls.statesNames()])
 
     @classmethod
     def getEffRates(cls, Vm):
         ''' Compute array of effective rate constants for a given membrane potential vector. '''
         return {k: np.mean(np.vectorize(v)(Vm)) for k, v in cls.effRates().items()}
 
     def getLookup(self):
         ''' Get lookup of membrane potential rate constants interpolated along the neuron's
             charge physiological range. '''
         logger.debug(f'generating {self} baseline lookup')
         Qmin, Qmax = expandRange(*self.Qbounds, exp_factor=10.)
         Qref = np.arange(Qmin, Qmax, 1e-5)  # C/m2
         Vref = Qref / self.Cm0 * 1e3  # mV
         tables = {k: np.vectorize(v)(Vref) for k, v in self.effRates().items()}
         return EffectiveVariablesLookup({'Q': Qref}, {'V': Vref, **tables})
 
     @classmethod
     @abc.abstractmethod
     def currents(cls):
         ''' Dictionary of ionic currents functions (returning current densities in mA/m2) '''
 
     @classmethod
     def iNet(cls, Vm, states):
         ''' net membrane current
 
             :param Vm: membrane potential (mV)
             :param states: states of ion channels gating and related variables
             :return: current per unit area (mA/m2)
         '''
         return sum([cfunc(Vm, states) for cfunc in cls.currents().values()])
 
     @classmethod
     def dQdt(cls, t, Qm, pad='right'):
         ''' membrane charge density variation rate
 
             :param t: time vector (s)
             :param Qm: membrane charge density vector (C/m2)
             :return: variation rate vector (mA/m2)
         '''
         dQdt = np.diff(Qm) / np.diff(t) * 1e3  # mA/m2
         return {'left': padleft, 'right': padright}[pad](dQdt)
 
     @classmethod
     def iax(cls, t, Qm, Vm, states):
         ''' axial current density
 
             (computed as sum of charge variation and net membrane ionic current)
 
             :param t: time vector (s)
             :param Qm: membrane charge density vector (C/m2)
             :param Vm: membrane potential (mV)
             :param states: states of ion channels gating and related variables
             :return: axial current density (mA/m2)
         '''
         return cls.iNet(Vm, states) + cls.dQdt(t, Qm)
 
     @classmethod
     def titrationFunc(cls, *args, **kwargs):
         ''' Default titration function. '''
         return cls.isExcited(*args, **kwargs)
 
     @staticmethod
     def currentToConcentrationRate(z_ion, depth):
         ''' Compute the conversion factor from a specific ionic current (in mA/m2)
             into a variation rate of submembrane ion concentration (in M/s).
 
             :param: z_ion: ion valence
             :param depth: submembrane depth (m)
             :return: conversion factor (Mmol.m-1.C-1)
         '''
         return 1e-6 / (z_ion * depth * FARADAY)
 
     @staticmethod
     def nernst(z_ion, Cion_in, Cion_out, T):
         ''' Nernst potential of a specific ion given its intra and extracellular concentrations.
 
             :param z_ion: ion valence
             :param Cion_in: intracellular ion concentration
             :param Cion_out: extracellular ion concentration
             :param T: temperature (K)
             :return: ion Nernst potential (mV)
         '''
         return (Rg * T) / (z_ion * FARADAY) * np.log(Cion_out / Cion_in) * 1e3
 
     @staticmethod
     def vtrap(x, y):
         ''' Generic function used to compute rate constants. '''
         return x / (np.exp(x / y) - 1)
 
     @staticmethod
     def efun(x):
         ''' Generic function used to compute rate constants. '''
         return x / (np.exp(x) - 1)
 
     @classmethod
     def ghkDrive(cls, Vm, Z_ion, Cion_in, Cion_out, T):
         ''' Use the Goldman-Hodgkin-Katz equation to compute the electrochemical driving force
             of a specific ion species for a given membrane potential.
 
             :param Vm: membrane potential (mV)
             :param Cin: intracellular ion concentration (M)
             :param Cout: extracellular ion concentration (M)
             :param T: temperature (K)
             :return: electrochemical driving force of a single ion particle (mC.m-3)
         '''
         x = Z_ion * FARADAY * Vm / (Rg * T) * 1e-3   # [-]
         eCin = Cion_in * cls.efun(-x)  # M
         eCout = Cion_out * cls.efun(x)  # M
         return FARADAY * (eCin - eCout) * 1e6  # mC/m3
 
     @classmethod
     def xBG(cls, Vref, Vm):
         ''' Compute dimensionless Borg-Graham ratio for a given voltage.
 
             :param Vref: reference voltage membrane (mV)
             :param Vm: membrane potential (mV)
             :return: dimensionless ratio
         '''
         return (Vm - Vref) * FARADAY / (Rg * cls.T) * 1e-3  # [-]
 
     @classmethod
     def alphaBG(cls, alpha0, zeta, gamma, Vref,  Vm):
         ''' Compute the activation rate constant for a given voltage and temperature, using
             a Borg-Graham formalism.
 
             :param alpha0: pre-exponential multiplying factor
             :param zeta: effective valence of the gating particle
             :param gamma: normalized position of the transition state within the membrane
             :param Vref: membrane voltage at which alpha = alpha0 (mV)
             :param Vm: membrane potential (mV)
             :return: rate constant (in alpha0 units)
         '''
         return alpha0 * np.exp(-zeta * gamma * cls.xBG(Vref, Vm))
 
     @classmethod
     def betaBG(cls, beta0, zeta, gamma, Vref, Vm):
         ''' Compute the inactivation rate constant for a given voltage and temperature, using
             a Borg-Graham formalism.
 
             :param beta0: pre-exponential multiplying factor
             :param zeta: effective valence of the gating particle
             :param gamma: normalized position of the transition state within the membrane
             :param Vref: membrane voltage at which beta = beta0 (mV)
             :param Vm: membrane potential (mV)
             :return: rate constant (in beta0 units)
         '''
         return beta0 * np.exp(zeta * (1 - gamma) * cls.xBG(Vref, Vm))
 
     @classmethod
     def getCurrentsNames(cls):
         return list(cls.currents().keys())
 
     @staticmethod
     def firingRateProfile(*args, **kwargs):
         return computeFRProfile(*args, **kwargs)
 
     @property
     def Qbounds(self):
         ''' Determine bounds of membrane charge physiological range for a given neuron. '''
         return np.array([np.round(self.Vm0 - 25.0), 50.0]) * self.Cm0 * 1e-3  # C/m2
 
     @classmethod
     def isVoltageGated(cls, state):
         ''' Determine whether a given state is purely voltage-gated or not.'''
         return f'alpha{state.lower()}' in cls.rates
 
     @classmethod
     @Model.checkOutputDir
     def simQueue(cls, amps, durations, offsets, PRFs, DCs, **kwargs):
         ''' Create a serialized 2D array of all parameter combinations for a series of individual
             parameter sweeps.
 
             :param amps: list (or 1D-array) of acoustic amplitudes
             :param durations: list (or 1D-array) of stimulus durations
             :param offsets: list (or 1D-array) of stimulus offsets (paired with durations array)
             :param PRFs: list (or 1D-array) of pulse-repetition frequencies
             :param DCs: list (or 1D-array) of duty cycle values
             :return: list of parameters (list) for each simulation
         '''
         if amps is None:
             amps = [None]
         drives = ElectricDrive.createQueue(amps)
         protocols = PulsedProtocol.createQueue(durations, offsets, PRFs, DCs)
         queue = []
         for drive in drives:
             for pp in protocols:
                 queue.append([drive, pp])
         return queue
 
     @classmethod
     @Model.checkOutputDir
     def simQueueBurst(cls, amps, durations, PRFs, DCs, BRFs, nbursts, **kwargs):
         if amps is None:
             amps = [None]
         drives = ElectricDrive.createQueue(amps)
         protocols = BurstProtocol.createQueue(durations, PRFs, DCs, BRFs, nbursts)
         queue = []
         for drive in drives:
             for pp in protocols:
                 queue.append([drive, pp])
         return queue
 
     @staticmethod
     def checkInputs(drive, pp):
         ''' Check validity of electrical stimulation parameters.
 
             :param drive: electric drive object
             :param pp: pulse protocol object
         '''
         if not isinstance(drive, Drive):
             raise TypeError(f'Invalid "drive" parameter (must be an "Drive" object)')
         if not isinstance(pp, TimeProtocol):
             raise TypeError('Invalid time protocol (must be "TimeProtocol" instance)')
 
     def chooseTimeStep(self):
         ''' Determine integration time step based on intrinsic temporal properties. '''
         return DT_EFFECTIVE
 
     @classmethod
     def derivatives(cls, t, y, Cm=None, drive=None):
         ''' Compute system derivatives for a given membrane capacitance and injected current.
 
             :param t: specific instant in time (s)
             :param y: vector of HH system variables at time t
             :param Cm: membrane capacitance (F/m2)
             :param Iinj: injected current (mA/m2)
             :return: vector of system derivatives at time t
         '''
         if Cm is None:
             Cm = cls.Cm0
         Qm, *states = y
         Vm = Qm / Cm * 1e3  # mV
         states_dict = dict(zip(cls.statesNames(), states))
         dQmdt = - cls.iNet(Vm, states_dict)  # mA/m2
         if drive is not None:
             dQmdt += drive.compute(t)
         dQmdt *= 1e-3  # A/m2
         # dQmdt = (Iinj - cls.iNet(Vm, states_dict)) * 1e-3  # A/m2
         return [dQmdt, *cls.getDerStates(Vm, states_dict)]
 
     @Model.logNSpikes
     @Model.checkTitrate
     @Model.addMeta
     @Model.logDesc
     @Model.checkSimParams
     def simulate(self, drive, pp):
         ''' Simulate a specific neuron model for a set of simulation parameters,
             and return output data in a dataframe.
 
             :param drive: electric drive object
             :param pp: pulse protocol object
             :return: output DataFrame
         '''
         # Set initial conditions
         y0 = {
             'Qm': self.Qm0,
             **{k: self.steadyStates()[k](self.Vm0) for k in self.statesNames()}
         }
 
         # Initialize solver and compute solution
         solver = EventDrivenSolver(
             lambda x: setattr(solver.drive, 'xvar', drive.xvar * x),  # eventfunc
             y0.keys(),                                                # variables
             lambda t, y: self.derivatives(t, y, drive=solver.drive),  # dfunc
             event_params={'drive': drive.copy().updatedX(0.)},        # event parameters
             dt=self.chooseTimeStep())                                 # time step
         data = solver(y0, pp.stimEvents(), pp.tstop)
 
         # Add Vm timeries to solution
         data = addColumn(data, 'Vm', data['Qm'].values / self.Cm0 * 1e3, preceding_key='Qm')
 
         # Return solution dataframe
         return data
 
     def desc(self, meta):
         return f'{self}: simulation @ {meta["drive"].desc}, {meta["pp"].desc}'
 
     @staticmethod
     def getNSpikes(data):
         ''' Compute number of spikes in charge profile of simulation output.
 
             :param data: dataframe containing output time series
             :return: number of detected spikes
         '''
         return detectSpikes(data)[0].size
 
     @staticmethod
     def getStabilizationValue(data):
         ''' Determine stabilization value from the charge profile of a simulation output.
 
             :param data: dataframe containing output time series
             :return: charge stabilization value (or np.nan if no stabilization detected)
         '''
 
         # Extract charge signal posterior to observation window
         t, Qm = [data[key].values for key in ['t', 'Qm']]
         if t.max() <= TMIN_STABILIZATION:
             raise ValueError('solution length is too short to assess stabilization')
         Qm = Qm[t > TMIN_STABILIZATION]
 
         # Compute variation range
         Qm_range = np.ptp(Qm)
         logger.debug('%.2f nC/cm2 variation range over the last %.0f ms, Qmf = %.2f nC/cm2',
                      Qm_range * 1e5, TMIN_STABILIZATION * 1e3, Qm[-1] * 1e5)
 
         # Return final value only if stabilization is detected
         if np.ptp(Qm) < QSS_Q_DIV_THR:
             return Qm[-1]
         else:
             return np.nan
 
     @classmethod
     def isExcited(cls, data):
         ''' Determine if neuron is excited from simulation output.
 
             :param data: dataframe containing output time series
             :return: boolean stating whether neuron is excited or not
         '''
         return cls.getNSpikes(data) > 0
 
     @classmethod
     def isSilenced(cls, data):
         ''' Determine if neuron is silenced from simulation output.
 
             :param data: dataframe containing output time series
             :return: boolean stating whether neuron is silenced or not
         '''
         return not np.isnan(cls.getStabilizationValue(data))
 
     def getArange(self, drive):
         return drive.xvar_range
diff --git a/PySONIC/postpro.py b/PySONIC/postpro.py
index 04f0bf6..18af23f 100644
--- a/PySONIC/postpro.py
+++ b/PySONIC/postpro.py
@@ -1,423 +1,426 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2017-08-22 14:33:04
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2020-08-08 15:45:47
+# @Last Modified time: 2021-03-02 13:26:27
 
 ''' Utility functions to detect spikes on signals and compute spiking metrics. '''
 
 import numpy as np
 import pandas as pd
 from scipy.interpolate import interp1d
 from scipy.optimize import brentq
 from scipy.signal import find_peaks, peak_prominences, butter, sosfiltfilt
 
 from .constants import *
 from .utils import logger, isIterable, loadData
 
 
 def detectCrossings(x, thr=0.0, edge='both'):
     '''
         Detect crossings of a threshold value in a 1D signal.
 
         :param x: 1D array_like data.
         :param edge: 'rising', 'falling', or 'both'
         :return: 1D array with the indices preceding the crossings
     '''
     ine, ire, ife = np.array([[], [], []], dtype=int)
     x_padright = np.hstack((x, x[-1]))
     x_padleft = np.hstack((x[0], x))
     if edge.lower() in ['falling', 'both']:
         ire = np.where((x_padright <= thr) & (x_padleft > thr))[0]
     if edge.lower() in ['rising', 'both']:
         ife = np.where((x_padright >= thr) & (x_padleft < thr))[0]
     ind = np.unique(np.hstack((ine, ire, ife))) - 1
     return ind
 
 
 def getFixedPoints(x, dx, filter='stable', der_func=None):
     ''' Find fixed points in a 1D plane phase profile.
 
         :param x: variable (1D array)
         :param dx: derivative (1D array)
         :param filter: string indicating whether to consider only stable/unstable
             fixed points or both
         :param: der_func: derivative function
         :return: array of fixed points values (or None if none is found)
     '''
     fps = []
     edge = {'stable': 'falling', 'unstable': 'rising', 'both': 'both'}[filter]
     izc = detectCrossings(dx, edge=edge)
     if izc.size > 0:
         for i in izc:
             # If derivative function is provided, find root using iterative Brent method
             if der_func is not None:
                 fps.append(brentq(der_func, x[i], x[i + 1], xtol=1e-16))
 
             # Otherwise, approximate root by linear interpolation
             else:
                 fps.append(x[i] - dx[i] * (x[i + 1] - x[i]) / (dx[i + 1] - dx[i]))
         return np.array(fps)
     else:
         return np.array([])
 
 
 def getEqPoint1D(x, dx, x0):
     ''' Determine equilibrium point in a 1D plane phase profile, for a given starting point.
 
         :param x: variable (1D array)
         :param dx: derivative (1D array)
         :param x0: abscissa of starting point (float)
         :return: abscissa of equilibrium point (or np.nan if none is found)
     '''
 
     # Find stable fixed points in 1D plane phase profile
     x_SFPs = getFixedPoints(x, dx, filter='stable')
     if x_SFPs.size == 0:
         return np.nan
 
     # Determine relevant stable fixed point from y0 sign
     y0 = np.interp(x0, x, dx, left=np.nan, right=np.nan)
     inds_subset = x_SFPs >= x0
     ind_SFP = 0
     if y0 < 0:
         inds_subset = ~inds_subset
         ind_SFP = -1
     x_SFPs = x_SFPs[inds_subset]
 
     if len(x_SFPs) == 0:
         return np.nan
 
     return x_SFPs[ind_SFP]
 
 
 def convertTime2SampleCriterion(x, dt, nsamples):
     if isIterable(x) and len(x) == 2:
         return (convertTime2Sample(x[0], dt, nsamples), convertTime2Sample(x[1], dt, nsamples))
     else:
         if isIterable(x) and len(x) == nsamples:
             return np.array([convertTime2Sample(item, dt, nsamples) for item in x])
         elif x is None:
             return None
         else:
             return int(np.ceil(x / dt))
 
 
 def computeTimeStep(t):
     ''' Compute time step based on time vector.
 
         :param t: time vector (s)
         :return: average time step (s)
     '''
     # Compute time step vector
     dt = np.diff(t)  # s
 
+    # Remove zero time increments from potential transition times.
+    dt = dt[dt != 0]
+
     # Raise error if time step vector is not uniform
     rel_dt_var = (dt.max() - dt.min()) / dt.min()
     if rel_dt_var > DT_MAX_REL_TOL:
         raise ValueError(f'irregular time step (rel. variance = {rel_dt_var:.2e})')
 
     # Return average dt value
     return np.mean(dt)  # s
 
 
 def resample(t, y, dt):
     ''' Resample a dataframe at regular time step. '''
     n = int(np.ptp(t) / dt) + 1
     ts = np.linspace(t.min(), t.max(), n)
     ys = np.interp(ts, t, y)
     return ts, ys
 
 
 def resolveIndexes(indexes, y, choice='max'):
     if indexes.size == 0:
         return indexes
     icomp = np.array([np.floor(indexes), np.ceil(indexes)]).astype(int).T
     ycomp = np.array([y[i] for i in icomp])
     method = {'min': np.argmin, 'max': np.argmax}[choice]
     ichoice = method(ycomp, axis=1)
     return np.array([x[ichoice[i]] for i, x in enumerate(icomp)])
 
 
 def resampleDataFrame(data, dt):
     ''' Resample a dataframe at regular time step. '''
     t = data['t'].values
     n = int(np.ptp(t) / dt) + 1
     tnew = np.linspace(t.min(), t.max(), n)
     new_data = {}
     for key in data:
         kind = 'nearest' if key == 'stimstate' else 'linear'
         new_data[key] = interp1d(t, data[key].values, kind=kind)(tnew)
     return pd.DataFrame(new_data)
 
 
 def prependDataFrame(data, tonset=0.):
     ''' Add an initial value (for t = 0) to all columns of a dataframe. '''
     # Repeat first row
     data = pd.concat([pd.DataFrame([data.iloc[0]]), data], ignore_index=True)
     data['t'][0] = tonset
     data['stimstate'][0] = 0
     return data
 
 
 def boundDataFrame(data, tbounds):
     ''' Restrict all columns of a dataframe to indexes corresponding to
         time values within specific bounds. '''
     tmin, tmax = tbounds
     return data[np.logical_and(data.t >= tmin, data.t <= tmax)].reset_index(drop=True)
 
 
 def find_tpeaks(t, y, **kwargs):
     ''' Wrapper around the scipy.signal.find_peaks function that provides a time vector
         associated to the signal, and translates time-based selection criteria into
         index-based criteria before calling the function.
 
         :param t: time vector
         :param y: signal vector
         :return: 2-tuple with peaks timings and properties dictionary
     '''
     # Remove initial samples from vectors if time values are redundant
     ipad = 0
     while t[ipad + 1] == t[ipad]:
         ipad += 1
     if ipad > 0:
         ss = 'from vectors (redundant time values)'
         if ipad == 1:
             logger.debug(f'Removing index 0 {ss}')
         else:
             logger.debug(f'Removing indexes 0-{ipad - 1} {ss}')
         t = t[ipad:]
         y = y[ipad:]
 
     # If time step is irregular, resample vectors at a uniform time step
     try:
         dt = computeTimeStep(t)  # s
         t_raw, y_raw = None, None
         indexes_raw = None
     except ValueError:
         new_dt = max(np.diff(t).min(), 1e-7)
         logger.debug(f'Resampling vector at regular time step (dt = {new_dt:.2e}s)')
         t_raw, y_raw = t.copy(), y.copy()
         indexes_raw = np.arange(t_raw.size)
         t, y = resample(t, y, new_dt)
         dt = computeTimeStep(t)  # s
 
     # Convert provided time-based input criteria into samples-based criteria
     for key in ['distance', 'width', 'wlen', 'plateau_size']:
         if key in kwargs:
             kwargs[key] = convertTime2SampleCriterion(kwargs[key], dt, t.size)
     if 'width' not in kwargs:
         kwargs['width'] = 1
 
     # Find peaks in the regularly sampled signal
     ipeaks, pps = find_peaks(y, **kwargs)
 
     # Adjust peak prominences and bases with restricted analysis window length
     # based on smallest peak width
     if len(ipeaks) > 0:
         wlen = 5 * min(pps['widths'])
         pps['prominences'], pps['left_bases'], pps['right_bases'] = peak_prominences(
             y, ipeaks, wlen=wlen)
 
     # If needed, re-project index-based outputs onto original sampling
     if t_raw is not None:
         logger.debug(f're-projecting index-based outputs onto original sampling')
         # Interpolate peak indexes and round to neighbor integer with max y value
         ipeaks_raw = np.interp(t[ipeaks], t_raw, indexes_raw, left=np.nan, right=np.nan)
         ipeaks = resolveIndexes(ipeaks_raw, y_raw, choice='max')
 
         # Interpolate peak base indexes and round to neighbor integer with min y value
         for key in ['left_bases', 'right_bases']:
             if key in pps:
                 ibase_raw = np.interp(
                     t[pps[key]], t_raw, indexes_raw, left=np.nan, right=np.nan)
                 pps[key] = resolveIndexes(ibase_raw, y_raw, choice='min')
 
         # Interpolate peak half-width interpolated positions
         for key in ['left_ips', 'right_ips']:
             if key in pps:
                 pps[key] = np.interp(
                     dt * pps[key], t_raw, indexes_raw, left=np.nan, right=np.nan)
 
     # If original vectors were cropped, correct offset in index-based outputs
     if ipad > 0:
         logger.debug(f'offseting index-based outputs by {ipad} to compensate initial cropping')
         ipeaks += ipad
         for key in ['left_bases', 'right_bases', 'left_ips', 'right_ips']:
             if key in pps:
                 pps[key] += ipad
 
     # Convert index-based peak widths into time-based widths
     if 'widths' in pps:
         pps['widths'] = np.array(pps['widths']) * dt
 
     # Return updated properties
     return ipeaks, pps
 
 
 def detectSpikes(data, key='Qm', mpt=SPIKE_MIN_DT, mph=SPIKE_MIN_QAMP, mpp=SPIKE_MIN_QPROM):
     ''' Detect spikes in simulation output data, by detecting peaks with specific height, prominence
         and distance properties on a given signal.
 
         :param data: simulation output dataframe
         :param key: key of signal on which to detect peaks
         :param mpt: minimal time interval between two peaks (s)
         :param mph: minimal peak height (in signal units)
         :param mpp: minimal peak prominence (in signal units)
         :return: indexes and properties of detected spikes
     '''
     if key not in data:
         raise ValueError(f'{key} vector not available in dataframe')
 
     # Detect peaks
     return find_tpeaks(
         data['t'].values,
         data[key].values,
         height=mph,
         distance=mpt,
         prominence=mpp
     )
 
 
 def convertPeaksProperties(t, properties):
     ''' Convert index-based peaks properties into time-based properties.
 
         :param t: time vector (s)
         :param properties: properties dictionary (with index-based information)
         :return: properties dictionary (with time-based information)
     '''
     indexes = np.arange(t.size)
     for key in ['left_bases', 'right_bases', 'left_ips', 'right_ips']:
         if key in properties:
             properties[key] = np.interp(properties[key], indexes, t, left=np.nan, right=np.nan)
     return properties
 
 
 def computeFRProfile(data):
     ''' Compute temporal profile of firing rate from simulaton output.
 
         :param data: simulation output dataframe
         :return: firing rate profile interpolated along time vector
     '''
     # Detect spikes in data
     ispikes, _ = detectSpikes(data)
     if len(ispikes) == 0:
         return np.ones(len(data)) * np.nan
 
     # Compute firing rate as function of spike time
     t = data['t'].values
     tspikes = t[ispikes][:-1]
     sr = 1 / np.diff(t[ispikes])
     if len(sr) == 0:
         return np.ones(t.size) * np.nan
 
     # Interpolate firing rate vector along time vector
     return np.interp(t, tspikes, sr, left=np.nan, right=np.nan)
 
 
 def computeSpikingMetrics(outputs):
     ''' Analyze the charge density profile from a list of files and compute for each one of them
         the following spiking metrics:
         - latency (ms)
         - firing rate mean and standard deviation (Hz)
         - spike amplitude mean and standard deviation (nC/cm2)
         - spike width mean and standard deviation (ms)
 
         :param outputs: list / generator of simulation outputs
         :return: a dataframe with the computed metrics
     '''
 
     # Initialize metrics dictionaries
     keys = [
         'latencies (ms)',
         'mean firing rates (Hz)',
         'std firing rates (Hz)',
         'mean spike amplitudes (nC/cm2)',
         'std spike amplitudes (nC/cm2)',
         'mean spike widths (ms)',
         'std spike widths (ms)'
     ]
     metrics = {k: [] for k in keys}
 
     # Compute spiking metrics
     for output in outputs:
 
         # Load data
         if isinstance(output, str):
             data, meta = loadData(output)
         else:
             data, meta = output
         tstim = meta['pp'].tstim
         t = data['t'].values
 
         # Detect spikes in data and extract features
         ispikes, properties = detectSpikes(data)
         widths = properties['widths']
         prominences = properties['prominences']
 
         if ispikes.size > 0:
             # Compute latency
             latency = t[ispikes[0]]
 
             # Select prior-offset spikes
             ispikes_prior = ispikes[t[ispikes] < tstim]
         else:
             latency = np.nan
             ispikes_prior = np.array([])
 
         # Compute spikes widths and amplitude
         if ispikes_prior.size > 0:
             widths_prior = widths[:ispikes_prior.size]
             prominences_prior = prominences[:ispikes_prior.size]
         else:
             widths_prior = np.array([np.nan])
             prominences_prior = np.array([np.nan])
 
         # Compute inter-spike intervals and firing rates
         if ispikes_prior.size > 1:
             ISIs_prior = np.diff(t[ispikes_prior])
             FRs_prior = 1 / ISIs_prior
         else:
             ISIs_prior = np.array([np.nan])
             FRs_prior = np.array([np.nan])
 
         # Log spiking metrics
         logger.debug('%u spikes detected (%u prior to offset)', ispikes.size, ispikes_prior.size)
         logger.debug('latency: %.2f ms', latency * 1e3)
         logger.debug('average spike width within stimulus: %.2f +/- %.2f ms',
                      np.nanmean(widths_prior) * 1e3, np.nanstd(widths_prior) * 1e3)
         logger.debug('average spike amplitude within stimulus: %.2f +/- %.2f nC/cm2',
                      np.nanmean(prominences_prior) * 1e5, np.nanstd(prominences_prior) * 1e5)
         logger.debug('average ISI within stimulus: %.2f +/- %.2f ms',
                      np.nanmean(ISIs_prior) * 1e3, np.nanstd(ISIs_prior) * 1e3)
         logger.debug('average FR within stimulus: %.2f +/- %.2f Hz',
                      np.nanmean(FRs_prior), np.nanstd(FRs_prior))
 
         # Complete metrics dictionaries
         metrics['latencies (ms)'].append(latency * 1e3)
         metrics['mean firing rates (Hz)'].append(np.mean(FRs_prior))
         metrics['std firing rates (Hz)'].append(np.std(FRs_prior))
         metrics['mean spike amplitudes (nC/cm2)'].append(np.mean(prominences_prior) * 1e5)
         metrics['std spike amplitudes (nC/cm2)'].append(np.std(prominences_prior) * 1e5)
         metrics['mean spike widths (ms)'].append(np.mean(widths_prior) * 1e3)
         metrics['std spike widths (ms)'].append(np.std(widths_prior) * 1e3)
 
     # Return dataframe with metrics
     return pd.DataFrame(metrics, columns=metrics.keys())
 
 
 def filtfilt(y, fs, fc, order):
     ''' Apply a bi-directional low-pass filter of specific order and cutoff frequency to a signal.
 
         :param y: signal vector
         :param fs: sampling frequency
         :param fc: cutoff frequency
         :param order: filter order (must be even)
         :return: filtered signal vector
 
         ..note: the filter order is divided by 2 since filtering is applied twice.
     '''
     assert order % 2 == 0, 'filter order must be an even integer'
     sos = butter(order // 2, fc, 'low', fs=fs, output='sos')
     return sosfiltfilt(sos, y)
diff --git a/README.md b/README.md
index 9ebcd70..d0f86a4 100644
--- a/README.md
+++ b/README.md
@@ -1,243 +1,203 @@
 # Description
 
 `PySONIC` is a Python implementation of the **multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model [1]**, a computationally efficient and interpretable model of neuronal intramembrane cavitation. It allows to simulate the responses of various neuron types to ultrasonic (and electrical) stimuli.
 
 ## Content of repository
 
 ### Models
 
 The package contains four model classes:
 - `Model` defines the generic interface of a model, including mandatory attributes and methods for simulating it.
 - `BilayerSonophore` defines the underlying **biomechanical model of intramembrane cavitation**.
 - `PointNeuron` defines an abstract generic interface to **conductance-based point-neuron electrical models**. It is inherited by classes defining the different neuron types with specific membrane dynamics.
 - `NeuronalBilayerSonophore` defines the **full electromechanical model for any given neuron type**. To do so, it inherits from `BilayerSonophore` and receives a specific `PointNeuron` object at initialization.
 
 All model classes contain a `simulate` method to simulate the underlying model's behavior for a given set of stimulation and physiological parameters. The `NeuronalBilayerSonophore.simulate` method contains an additional `method` argument defining whether to perform a detailed (`full`), coarse-grained (`sonic`) or hybrid (`hybrid`) integration of the differential system.
 
 ### Solvers
 
 Numerical integration routines are implemented outside the models, in a separate `solvers` module:
 - `PeriodicSolver` integrates a differential system periodically until a stable periodic behavior is detected.
 - `EventDrivenSolver` integrates a differential system across a specific set of "events" that modulate the stimuluation drive over time.
 - `HybridSolver` inherits from both `PeriodicSolver`and `EventDrivenSolver`. It integrates a differential system using a hybrid scheme inside each "ON" or "OFF" period:
   1. The full ODE system is integrated for a few cycles with a dense time granularity until a periodic stabilization detection
   2. The profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate
   3. A subset of the ODE system is integrated with a sparse time granularity, while the remaining variables are periodically expanded from their last cycle profile, until the end of the period or that of an predefined update interval.
   4. The process is repeated from step 1
 
 ### Neurons
 
 Several conductance-based point-neuron models are implemented that inherit from the `PointNeuron` generic interface.
 
 Among them, several CNS models:
 - `CorticalRS`: cortical regular spiking (`RS`) neuron
 - `CorticalFS`: cortical fast spiking (`FS`) neuron
 - `CorticalLTS`: cortical low-threshold spiking (`LTS`) neuron
 - `CorticalIB`: cortical intrinsically bursting (`IB`) neuron
 - `ThalamicRE`: thalamic reticular (`RE`) neuron
 - `ThalamoCortical`: thalamo-cortical (`TC`) neuron
 - `OstukaSTN`: subthalamic nucleus (`STN`) neuron
 
 But also some valuable models used in peripheral axon models:
 - `FrankenhaeuserHuxleyNode`: Amphibian (Xenopus) myelinated fiber node (`FHnode`)
 - `SweeneyNode`: Mammalian (rabbit) myelinated motor fiber node (`SWnode`)
 - `MRGNode`: Mammalian (human / cat) myelinated fiber node (`MRGnode`)
 - `SundtSegment`: Mammalian unmyelinated C-fiber segment (`SUseg`)
 
 ### Other modules
 
 - `batches`: a generic interface to run simulation batches with or without multiprocessing
 - `parsers`: command line parsing utilities
 - `plt`: graphing utilities
 - `postpro`: post-processing utilities (mostly signal features detection)
 - `constants`: algorithmic constants used across modules and classes
 - `utils`: generic utilities
 
 # Requirements
 
 - Python 3.6+
 - Package dependencies (numpy, scipy, ...) are installed automatically upon installation of the package.
 
 # Installation
 
 - Open a terminal.
 
 - Install [git-lfs](https://github.com/git-lfs/git-lfs/wiki/Installation) if not already done
 
 - Activate a Python3 environment if needed, e.g. on the tnesrv5 machine:
 
 ```source /opt/apps/anaconda3/bin activate```
 
 - Check that the appropriate version of pip is activated:
 
 ```pip --version```
 
 - Clone the repository and install the python package:
 
 ```git clone https://c4science.ch/diffusion/4670/pysonic.git```
 
 ```cd pysonic```
 
 ```pip install -e .```
 
 # Usage
 
-## Python scripts
+## Python code
 
-You can easily run simulations of any implemented point-neuron model under both electrical and ultrasonic stimuli with various temporal protocols, and visualize the simulation results, in just a few lines of code:
+You can easily run simulations of any implemented point-neuron model under both electrical and ultrasonic stimuli with various temporal protocols, and visualize the simulation results, in just a few lines of code. Here's an example:
 
 ```python
-# -*- coding: utf-8 -*-
-# @Author: Theo Lemaire
-# @Email: theo.lemaire@epfl.ch
-# @Date:   2020-04-17 16:09:42
-# @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2020-04-24 10:35:33
-
-''' Example script showing how to simulate a point-neuron model upon application
-    of both electrical and ultrasonic stimuli, with various temporal protocols.
-'''
-
 import logging
 import matplotlib.pyplot as plt
-
 from PySONIC.utils import logger
 from PySONIC.neurons import getPointNeuron
-from PySONIC.core import NeuronalBilayerSonophore, ElectricDrive, AcousticDrive
+from PySONIC.core import NeuronalBilayerSonophore, AcousticDrive
 from PySONIC.core.protocols import *
 from PySONIC.plt import GroupedTimeSeries
-
-# Set logging level
 logger.setLevel(logging.INFO)
 
-# Define point-neuron model and corresponding neuronal bilayer sonophore model
-pneuron = getPointNeuron('RS')
-a = 32e-9  # sonophore radius (m)
-nbls = NeuronalBilayerSonophore(a, pneuron)
-
-# Define electric and ultrasonic drives
-ELdrive = ElectricDrive(20.)  # mA/m2
-USdrive = AcousticDrive(
-    500e3,  # Hz
-    100e3)  # Pa
-
-# Pulsing parameters
-tstart = 10e-3   # s
-tburst = 100e-3  # s
-PRF = 100.       # Hz
-DC = 0.5         # -
-BRF = 1.0        # Hz
-nbursts = 3      # -
-
-# Protocols
-protocols = [
-    CustomProtocol([10e-3, 30e-3, 50e-3], [1., 2., 0.], 100e-3),
-    PulsedProtocol(tburst, 1 / BRF - tburst, PRF=PRF, DC=DC, tstart=tstart),
-    BurstProtocol(tburst, PRF=PRF, DC=DC, BRF=BRF, nbursts=nbursts, tstart=tstart)
-]
-
-# For each protocol
-for p in protocols:
-    # Run simulation upon electrical stimulation, and plot results
-    data, meta = pneuron.simulate(ELdrive, p)
-    GroupedTimeSeries([(data, meta)]).render()
-
-    # Run simulation upon ultrasonic stimulation, and plot results
-    data, meta = nbls.simulate(USdrive, p)
-    GroupedTimeSeries([(data, meta)]).render()
-
-# Show figures
-plt.show()
+# Define point-neuron model and corresponding neuronal bilayer sonophore object
+nbls = NeuronalBilayerSonophore(32e-9, getPointNeuron('RS'))
+
+# Define stimulus drive and time protocol
+drive = AcousticDrive(500e3, 100e3)
+pp = PulsedProtocol(100e-3, 100e-3, tstart=10e-3)
 
+# Run simulation and plot results
+data, meta = nbls.simulate(drive, pp)
+GroupedTimeSeries([(data, meta)]).render()
+plt.show()
 ```
 
+Have a look at the `demo.ipynb` notebook located in the `notebooks` folder for more details.
+
 ## From the command line
 
 ### Running simulations and batches
 
 You can easily run simulations of all 3 model types using the dedicated command line scripts. To do so, open a terminal in the `scripts` directory.
 
 - Use `run_mech.py` for simulations of the **mechanical model** upon **ultrasonic stimulation**. For instance, for a 32 nm radius bilayer sonophore sonicated at 500 kHz and 100 kPa:
 
 ```python run_mech.py -a 32 -f 500 -A 100 -p Z```
 
 - Use `run_estim.py` for simulations of **point-neuron models** upon **intracellular electrical stimulation**. For instance, a regular-spiking (RS) neuron injected with 10 mA/m2 intracellular current for 30 ms:
 
 ```python run_estim.py -n RS -A 10 --tstim 30 -p Vm```
 
 - Use `run_astim.py` for simulations of **point-neuron models** upon **ultrasonic stimulation**. For instance, for a coarse-grained simulation of a 32 nm radius bilayer sonophore within a regular-spiking (RS) neuron membrane, sonicated at 500 kHz and 100 kPa for 150 ms:
 
 ```python run_astim.py -n RS -a 32 -f 500 -A 100 --tstim 150 --method sonic -p Qm```
 
 Additionally, you can run batches of simulations by specifying more than one value for any given stimulation parameter (e.g. `-A 100 200` for sonication with 100 and 200 kPa respectively). These batches can be parallelized using multiprocessing to optimize performance, with the extra argument `--mpi`.
 
 ### Saving and visualizing results
 
 By default, simulation results are neither shown, nor saved.
 
 To view results directly upon simulation completion, you can use the `-p [xxx]` option, where `[xxx]` can be `all` (to plot all resulting variables) or a given variable name (e.g. `Z` for membrane deflection, `Vm` for membrane potential, `Qm` for membrane charge density).
 
 To save simulation results in binary `.pkl` files, you can use the `-s` option. You will be prompted to choose an output directory, unless you also specify it with the `-o <output_directory>` option. Output files are automatically named from model and simulation parameters to avoid ambiguity.
 
 When running simulation batches, it is highly advised to specify the `-s` option in order to save results of each simulation. You can then visualize results at a later stage.
 
 To visualize results, use the `plot_timeseries.py` script. You will be prompted to select the output files containing the simulation(s) results. By default, separate figures will be created for each simulation, showing the time profiles of all resulting variables. Here again, you can choose to show only a subset of variables using the `-p [xxx]` option. Moreover, if you select a subset of variables, you can visualize resulting profiles across simulations in comparative figures wih the `--compare` option.
 
 Several more options are available. To view them, type in:
 
 ```python <script_name> -h```
 
 
 # Extend the package
 
 ## Add other neuron types
 
 You can easily add other neuron types into the package, providing their ion channel populations and underlying voltage-gated dynamics equations are known.
 
 To add a new point-neuron model, follow this procedure:
 
 1. Create a new file, and save it in the `neurons` sub-folder, with an explicit name (e.g. `my_neuron.py`).
 2. Copy-paste the content of the `template.py` file (also located in the `neurons` sub-folder) into your file.
 3. In your file, change the **class name** from `TemplateNeuron` to something more explicit (e.g. `MyNeuron`), and change the **neuron name** accordingly (e.g. `myneuron`). This name is a keyword used to refer to the model from outside the class. Also, comment out the `addSonicFeatures` decorator temporarily.
 4. Modify/add **biophysical parameters** of your model (resting parameters, reversal potentials, channel conductances, ionic concentrations, temperatures, diffusion constants, etc...) as class attributes. If some parameters are not fixed and must be computed, assign them to the class inside a  `__new__` method, taking the class (`cls`) as sole attribute.
 5. Specify a **dictionary of names:descriptions of your different differential states** (i.e. all the differential variables of your model, except for the membrane potential).
 6. Modify/add **gating states kinetics** (`alphax` and `betax` methods) that define the voltage-dependent activation and inactivation rates of the different ion channnels gates of your model. Those methods take the membrane potential `Vm` as input and return a rate in `s-1`. Alternatively, your can use steady-state open-probabilties (`xinf`) and adaptation time constants (`taux`) methods.
 7. Modify the `derStates` method that defines the **derivatives of your different state variables**. These derivatives are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential `Vm` and a states vector `x` as inputs, and returns the associated state derivative (in `<state_unit>/s`).
 8. Modify the `steadyStates` method that defines the **steady-state values of your different state variables**. These steady-states are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential `Vm` as only input, and returns the associated steady-state value (in `<state_unit>`). If some steady-states depend on the values of other-steady states, you can proceed as follows:
    - define all independent steady-states functions in a dictionary called `lambda_dict`
    - add dependent steady-state functions to the dictionary, calling `lambda_dict[k](Vm)` for each state `k` whose value is required.
 9. Modify/add **membrane currents** (`iXX` methods) of your model. Those methods take relevant gating states and the membrane potential `Vm` as inputs, and must return a current density in `mA/m2`. **You also need to modify the docstring accordingly, as this information is used by the package**.
 10. Modify the `currents` method that defines the **membrane currents of your model**. These currents are defined inside a dictionary, where each current key is paired to a lambda function that takes the membrane potential `Vm` and a states vector `x` as inputs, and returns the associated current (in `mA/m2`).
 11. Add the neuron class to the package, by importing it in the `__init__.py` file of the `neurons` sub-folder:
 
 ```python
 from .my_neuron import MyNeuron
 ```
 
 12. Verify your point-neuron model by running simulations under various electrical stimuli and comparing the output to the neurons's expected behavior. Implement required corrections if any.
 13. Uncomment the `addSonicFeatures` decorator on top of your class. **This decorator will automatically parse the `derStates`, `steadyStates` and `currents` methods in order to adapt your neuron model to US stimulation. For this to work, you need to make sure to**:
    - **keep them as class methods**
    - **check that all calls to functions that depend solely on `Vm` appear directly in the methods' lambda expressions and are not hidden inside nested function calls.**
 14. Pre-compute lookup tables required to run coarse-grained  simulations of the neuron model upon ultrasonic stimulation. To do so, go to the `scripts` directory and run the `run_lookups.py` script with the neuron's name as command line argument, e.g.:
 
 ```python run_lookups.py -n myneuron --mpi```
 
 If possible, use the `--mpi` argument to enable multiprocessing, as lookups pre-computation greatly benefits from parallelization.
 
 That's it! You can now run simulations of your point-neuron model upon ultrasonic stimulation.
 
 # Authors
 
 Code written and maintained by Theo Lemaire (theo.lemaire@epfl.ch).
 
 # License & citation
 
 This project is licensed under the MIT License - see the LICENSE file for details.
 
 If PySONIC contributes to a project that leads to a scientific publication, please acknowledge this fact by citing [Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.](https://iopscience.iop.org/article/10.1088/1741-2552/ab1685)
 
 DOI: 10.1088/1741-2552/ab1685
 
 # References
 
 [1] Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.
diff --git a/README.mdpp b/README.mdpp
deleted file mode 100644
index 730f3dc..0000000
--- a/README.mdpp
+++ /dev/null
@@ -1,180 +0,0 @@
-# Description
-
-`PySONIC` is a Python implementation of the **multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model [1]**, a computationally efficient and interpretable model of neuronal intramembrane cavitation. It allows to simulate the responses of various neuron types to ultrasonic (and electrical) stimuli.
-
-## Content of repository
-
-### Models
-
-The package contains four model classes:
-- `Model` defines the generic interface of a model, including mandatory attributes and methods for simulating it.
-- `BilayerSonophore` defines the underlying **biomechanical model of intramembrane cavitation**.
-- `PointNeuron` defines an abstract generic interface to **conductance-based point-neuron electrical models**. It is inherited by classes defining the different neuron types with specific membrane dynamics.
-- `NeuronalBilayerSonophore` defines the **full electromechanical model for any given neuron type**. To do so, it inherits from `BilayerSonophore` and receives a specific `PointNeuron` object at initialization.
-
-All model classes contain a `simulate` method to simulate the underlying model's behavior for a given set of stimulation and physiological parameters. The `NeuronalBilayerSonophore.simulate` method contains an additional `method` argument defining whether to perform a detailed (`full`), coarse-grained (`sonic`) or hybrid (`hybrid`) integration of the differential system.
-
-### Solvers
-
-Numerical integration routines are implemented outside the models, in a separate `solvers` module:
-- `PeriodicSolver` integrates a differential system periodically until a stable periodic behavior is detected.
-- `EventDrivenSolver` integrates a differential system across a specific set of "events" that modulate the stimuluation drive over time.
-- `HybridSolver` inherits from both `PeriodicSolver`and `EventDrivenSolver`. It integrates a differential system using a hybrid scheme inside each "ON" or "OFF" period:
-  1. The full ODE system is integrated for a few cycles with a dense time granularity until a periodic stabilization detection
-  2. The profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate
-  3. A subset of the ODE system is integrated with a sparse time granularity, while the remaining variables are periodically expanded from their last cycle profile, until the end of the period or that of an predefined update interval.
-  4. The process is repeated from step 1
-
-### Neurons
-
-Several conductance-based point-neuron models are implemented that inherit from the `PointNeuron` generic interface.
-
-Among them, several CNS models:
-- `CorticalRS`: cortical regular spiking (`RS`) neuron
-- `CorticalFS`: cortical fast spiking (`FS`) neuron
-- `CorticalLTS`: cortical low-threshold spiking (`LTS`) neuron
-- `CorticalIB`: cortical intrinsically bursting (`IB`) neuron
-- `ThalamicRE`: thalamic reticular (`RE`) neuron
-- `ThalamoCortical`: thalamo-cortical (`TC`) neuron
-- `OstukaSTN`: subthalamic nucleus (`STN`) neuron
-
-But also some valuable models used in peripheral axon models:
-- `FrankenhaeuserHuxleyNode`: Amphibian (Xenopus) myelinated fiber node (`FHnode`)
-- `SweeneyNode`: Mammalian (rabbit) myelinated motor fiber node (`SWnode`)
-- `MRGNode`: Mammalian (human / cat) myelinated fiber node (`MRGnode`)
-- `SundtSegment`: Mammalian unmyelinated C-fiber segment (`SUseg`)
-
-### Other modules
-
-- `batches`: a generic interface to run simulation batches with or without multiprocessing
-- `parsers`: command line parsing utilities
-- `plt`: graphing utilities
-- `postpro`: post-processing utilities (mostly signal features detection)
-- `constants`: algorithmic constants used across modules and classes
-- `utils`: generic utilities
-
-# Requirements
-
-- Python 3.6+
-- Package dependencies (numpy, scipy, ...) are installed automatically upon installation of the package.
-
-# Installation
-
-- Open a terminal.
-
-- Install [git-lfs](https://github.com/git-lfs/git-lfs/wiki/Installation) if not already done
-
-- Activate a Python3 environment if needed, e.g. on the tnesrv5 machine:
-
-```source /opt/apps/anaconda3/bin activate```
-
-- Check that the appropriate version of pip is activated:
-
-```pip --version```
-
-- Clone the repository and install the python package:
-
-```git clone https://c4science.ch/diffusion/4670/pysonic.git```
-
-```cd pysonic```
-
-```pip install -e .```
-
-# Usage
-
-## Python scripts
-
-You can easily run simulations of any implemented point-neuron model under both electrical and ultrasonic stimuli with various temporal protocols, and visualize the simulation results, in just a few lines of code:
-
-!INCLUDECODE "examples/sims.py" (python)
-
-## From the command line
-
-### Running simulations and batches
-
-You can easily run simulations of all 3 model types using the dedicated command line scripts. To do so, open a terminal in the `scripts` directory.
-
-- Use `run_mech.py` for simulations of the **mechanical model** upon **ultrasonic stimulation**. For instance, for a 32 nm radius bilayer sonophore sonicated at 500 kHz and 100 kPa:
-
-```python run_mech.py -a 32 -f 500 -A 100 -p Z```
-
-- Use `run_estim.py` for simulations of **point-neuron models** upon **intracellular electrical stimulation**. For instance, a regular-spiking (RS) neuron injected with 10 mA/m2 intracellular current for 30 ms:
-
-```python run_estim.py -n RS -A 10 --tstim 30 -p Vm```
-
-- Use `run_astim.py` for simulations of **point-neuron models** upon **ultrasonic stimulation**. For instance, for a coarse-grained simulation of a 32 nm radius bilayer sonophore within a regular-spiking (RS) neuron membrane, sonicated at 500 kHz and 100 kPa for 150 ms:
-
-```python run_astim.py -n RS -a 32 -f 500 -A 100 --tstim 150 --method sonic -p Qm```
-
-Additionally, you can run batches of simulations by specifying more than one value for any given stimulation parameter (e.g. `-A 100 200` for sonication with 100 and 200 kPa respectively). These batches can be parallelized using multiprocessing to optimize performance, with the extra argument `--mpi`.
-
-### Saving and visualizing results
-
-By default, simulation results are neither shown, nor saved.
-
-To view results directly upon simulation completion, you can use the `-p [xxx]` option, where `[xxx]` can be `all` (to plot all resulting variables) or a given variable name (e.g. `Z` for membrane deflection, `Vm` for membrane potential, `Qm` for membrane charge density).
-
-To save simulation results in binary `.pkl` files, you can use the `-s` option. You will be prompted to choose an output directory, unless you also specify it with the `-o <output_directory>` option. Output files are automatically named from model and simulation parameters to avoid ambiguity.
-
-When running simulation batches, it is highly advised to specify the `-s` option in order to save results of each simulation. You can then visualize results at a later stage.
-
-To visualize results, use the `plot_timeseries.py` script. You will be prompted to select the output files containing the simulation(s) results. By default, separate figures will be created for each simulation, showing the time profiles of all resulting variables. Here again, you can choose to show only a subset of variables using the `-p [xxx]` option. Moreover, if you select a subset of variables, you can visualize resulting profiles across simulations in comparative figures wih the `--compare` option.
-
-Several more options are available. To view them, type in:
-
-```python <script_name> -h```
-
-
-# Extend the package
-
-## Add other neuron types
-
-You can easily add other neuron types into the package, providing their ion channel populations and underlying voltage-gated dynamics equations are known.
-
-To add a new point-neuron model, follow this procedure:
-
-1. Create a new file, and save it in the `neurons` sub-folder, with an explicit name (e.g. `my_neuron.py`).
-2. Copy-paste the content of the `template.py` file (also located in the `neurons` sub-folder) into your file.
-3. In your file, change the **class name** from `TemplateNeuron` to something more explicit (e.g. `MyNeuron`), and change the **neuron name** accordingly (e.g. `myneuron`). This name is a keyword used to refer to the model from outside the class. Also, comment out the `addSonicFeatures` decorator temporarily.
-4. Modify/add **biophysical parameters** of your model (resting parameters, reversal potentials, channel conductances, ionic concentrations, temperatures, diffusion constants, etc...) as class attributes. If some parameters are not fixed and must be computed, assign them to the class inside a  `__new__` method, taking the class (`cls`) as sole attribute.
-5. Specify a **dictionary of names:descriptions of your different differential states** (i.e. all the differential variables of your model, except for the membrane potential).
-6. Modify/add **gating states kinetics** (`alphax` and `betax` methods) that define the voltage-dependent activation and inactivation rates of the different ion channnels gates of your model. Those methods take the membrane potential `Vm` as input and return a rate in `s-1`. Alternatively, your can use steady-state open-probabilties (`xinf`) and adaptation time constants (`taux`) methods.
-7. Modify the `derStates` method that defines the **derivatives of your different state variables**. These derivatives are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential `Vm` and a states vector `x` as inputs, and returns the associated state derivative (in `<state_unit>/s`).
-8. Modify the `steadyStates` method that defines the **steady-state values of your different state variables**. These steady-states are defined inside a dictionary, where each state key is paired to a lambda function that takes the membrane potential `Vm` as only input, and returns the associated steady-state value (in `<state_unit>`). If some steady-states depend on the values of other-steady states, you can proceed as follows:
-   - define all independent steady-states functions in a dictionary called `lambda_dict`
-   - add dependent steady-state functions to the dictionary, calling `lambda_dict[k](Vm)` for each state `k` whose value is required.
-9. Modify/add **membrane currents** (`iXX` methods) of your model. Those methods take relevant gating states and the membrane potential `Vm` as inputs, and must return a current density in `mA/m2`. **You also need to modify the docstring accordingly, as this information is used by the package**.
-10. Modify the `currents` method that defines the **membrane currents of your model**. These currents are defined inside a dictionary, where each current key is paired to a lambda function that takes the membrane potential `Vm` and a states vector `x` as inputs, and returns the associated current (in `mA/m2`).
-11. Add the neuron class to the package, by importing it in the `__init__.py` file of the `neurons` sub-folder:
-
-```python
-from .my_neuron import MyNeuron
-```
-
-12. Verify your point-neuron model by running simulations under various electrical stimuli and comparing the output to the neurons's expected behavior. Implement required corrections if any.
-13. Uncomment the `addSonicFeatures` decorator on top of your class. **This decorator will automatically parse the `derStates`, `steadyStates` and `currents` methods in order to adapt your neuron model to US stimulation. For this to work, you need to make sure to**:
-   - **keep them as class methods**
-   - **check that all calls to functions that depend solely on `Vm` appear directly in the methods' lambda expressions and are not hidden inside nested function calls.**
-14. Pre-compute lookup tables required to run coarse-grained  simulations of the neuron model upon ultrasonic stimulation. To do so, go to the `scripts` directory and run the `run_lookups.py` script with the neuron's name as command line argument, e.g.:
-
-```python run_lookups.py -n myneuron --mpi```
-
-If possible, use the `--mpi` argument to enable multiprocessing, as lookups pre-computation greatly benefits from parallelization.
-
-That's it! You can now run simulations of your point-neuron model upon ultrasonic stimulation.
-
-# Authors
-
-Code written and maintained by Theo Lemaire (theo.lemaire@epfl.ch).
-
-# License & citation
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-If PySONIC contributes to a project that leads to a scientific publication, please acknowledge this fact by citing [Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.](https://iopscience.iop.org/article/10.1088/1741-2552/ab1685)
-
-DOI: 10.1088/1741-2552/ab1685
-
-# References
-
-[1] Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.
diff --git a/examples/baseline_modulation.py b/examples/baseline_modulation.py
deleted file mode 100644
index 2851f6e..0000000
--- a/examples/baseline_modulation.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author: Theo Lemaire
-# @Email: theo.lemaire@epfl.ch
-# @Date:   2020-04-24 10:22:56
-# @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2020-04-24 11:14:47
-
-import logging
-import matplotlib.pyplot as plt
-
-from PySONIC.utils import logger
-from PySONIC.neurons import getPointNeuron
-from PySONIC.core import DrivenNeuronalBilayerSonophore, AcousticDrive
-from PySONIC.core.protocols import *
-from PySONIC.plt import CompTimeSeries
-
-# Set logging level
-logger.setLevel(logging.INFO)
-
-# Point-neuron model and sonophore radius
-pneuron = getPointNeuron('TC')
-a = 32e-9  # sonophore radius (m)
-
-# Ultrasonic drive
-USdrive = AcousticDrive(
-    500e3,  # Hz
-    100e3)  # Pa
-
-# Bursting protocol
-tstart = 500e-3  # s
-tburst = 100e-3  # s
-PRF = 100.       # Hz
-DC = 0.5         # -
-BRF = 1.0        # Hz
-nbursts = 3      # -
-p = BurstProtocol(tburst, PRF=PRF, DC=DC, BRF=BRF, nbursts=nbursts, tstart=tstart)
-
-# Simulate for a bunch of driving currents
-Idrives = [-3.0, -1.5, 0., 4.0, 5.0]  # mA/m2
-outputs = []
-for Idrive in Idrives:
-    nbls = DrivenNeuronalBilayerSonophore(Idrive, a, pneuron)
-    outputs.append(nbls.simulate(USdrive, p))
-
-# Plot comparative profiles
-labels = [f'Idrive = {x:.1f} mA/m2' for x in Idrives]
-CompTimeSeries(outputs, 'Qm').render(labels=labels)
-
-plt.show()
diff --git a/examples/sims.py b/examples/sims.py
deleted file mode 100644
index 85854ef..0000000
--- a/examples/sims.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author: Theo Lemaire
-# @Email: theo.lemaire@epfl.ch
-# @Date:   2020-04-17 16:09:42
-# @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2020-04-24 10:35:33
-
-''' Example script showing how to simulate a point-neuron model upon application
-    of both electrical and ultrasonic stimuli, with various temporal protocols.
-'''
-
-import logging
-import matplotlib.pyplot as plt
-
-from PySONIC.utils import logger
-from PySONIC.neurons import getPointNeuron
-from PySONIC.core import NeuronalBilayerSonophore, ElectricDrive, AcousticDrive
-from PySONIC.core.protocols import *
-from PySONIC.plt import GroupedTimeSeries
-
-# Set logging level
-logger.setLevel(logging.INFO)
-
-# Define point-neuron model and corresponding neuronal bilayer sonophore model
-pneuron = getPointNeuron('RS')
-a = 32e-9  # sonophore radius (m)
-nbls = NeuronalBilayerSonophore(a, pneuron)
-
-# Define electric and ultrasonic drives
-ELdrive = ElectricDrive(20.)  # mA/m2
-USdrive = AcousticDrive(
-    500e3,  # Hz
-    100e3)  # Pa
-
-# Pulsing parameters
-tstart = 10e-3   # s
-tburst = 100e-3  # s
-PRF = 100.       # Hz
-DC = 0.5         # -
-BRF = 1.0        # Hz
-nbursts = 3      # -
-
-# Protocols
-protocols = [
-    CustomProtocol([10e-3, 30e-3, 50e-3], [1., 2., 0.], 100e-3),
-    PulsedProtocol(tburst, 1 / BRF - tburst, PRF=PRF, DC=DC, tstart=tstart),
-    BurstProtocol(tburst, PRF=PRF, DC=DC, BRF=BRF, nbursts=nbursts, tstart=tstart)
-]
-
-# For each protocol
-for p in protocols:
-    # Run simulation upon electrical stimulation, and plot results
-    data, meta = pneuron.simulate(ELdrive, p)
-    GroupedTimeSeries([(data, meta)]).render()
-
-    # Run simulation upon ultrasonic stimulation, and plot results
-    data, meta = nbls.simulate(USdrive, p)
-    GroupedTimeSeries([(data, meta)]).render()
-
-# Show figures
-plt.show()
diff --git a/notebooks/BLS model - balance deflection.ipynb b/notebooks/BLS model/BLS model - balance deflection.ipynb
similarity index 100%
rename from notebooks/BLS model - balance deflection.ipynb
rename to notebooks/BLS model/BLS model - balance deflection.ipynb
diff --git a/notebooks/BLS model - intermolecular pressure.ipynb b/notebooks/BLS model/BLS model - intermolecular pressure.ipynb
similarity index 100%
rename from notebooks/BLS model - intermolecular pressure.ipynb
rename to notebooks/BLS model/BLS model - intermolecular pressure.ipynb
diff --git a/notebooks/BLS model - static forces.ipynb b/notebooks/BLS model/BLS model - static forces.ipynb
similarity index 100%
rename from notebooks/BLS model - static forces.ipynb
rename to notebooks/BLS model/BLS model - static forces.ipynb
diff --git a/notebooks/STN neuron.ipynb b/notebooks/STN neuron/STN neuron.ipynb
similarity index 100%
rename from notebooks/STN neuron.ipynb
rename to notebooks/STN neuron/STN neuron.ipynb
diff --git a/notebooks/TC neuron - iH kinetics.ipynb b/notebooks/TC neuron/TC neuron - iH kinetics.ipynb
similarity index 100%
rename from notebooks/TC neuron - iH kinetics.ipynb
rename to notebooks/TC neuron/TC neuron - iH kinetics.ipynb
diff --git a/notebooks/TI US predictions.ipynb b/notebooks/TI US/TI US predictions.ipynb
similarity index 100%
rename from notebooks/TI US predictions.ipynb
rename to notebooks/TI US/TI US predictions.ipynb
diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb
new file mode 100644
index 0000000..9e7ca3a
--- /dev/null
+++ b/notebooks/demo.ipynb
@@ -0,0 +1,817 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running simulations with PySONIC\n",
+    "\n",
+    "This notebook illustrates how to simulate a point-neuron model upon application of both electrical and ultrasonic stimuli, with various temporal protocols."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import PySONIC modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PySONIC.utils import logger\n",
+    "from PySONIC.neurons import getPointNeuron, getNeuronsDict\n",
+    "from PySONIC.core import NeuronalBilayerSonophore, DrivenNeuronalBilayerSonophore, ElectricDrive, AcousticDrive\n",
+    "from PySONIC.core.protocols import *\n",
+    "from PySONIC.plt import GroupedTimeSeries, CompTimeSeries\n",
+    "\n",
+    "# Set logging level\n",
+    "logger.setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instanciate a point-neuron model\n",
+    "\n",
+    "We start by instanciating a point-neuron model of a particular neuron type. To list the available point-neuron models, type in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- FS: <class 'PySONIC.neurons.cortical.CorticalFS'>\n",
+      "- IB: <class 'PySONIC.neurons.cortical.CorticalIB'>\n",
+      "- LTS: <class 'PySONIC.neurons.cortical.CorticalLTS'>\n",
+      "- RS: <class 'PySONIC.neurons.cortical.CorticalRS'>\n",
+      "- FHnode: <class 'PySONIC.neurons.fh.FrankenhaeuserHuxleyNode'>\n",
+      "- HHseg: <class 'PySONIC.neurons.hh.HodgkinHuxleySegment'>\n",
+      "- LeechP: <class 'PySONIC.neurons.leech.LeechPressure'>\n",
+      "- LeechR: <class 'PySONIC.neurons.leech.LeechRetzius'>\n",
+      "- LeechT: <class 'PySONIC.neurons.leech.LeechTouch'>\n",
+      "- MRGnode: <class 'PySONIC.neurons.mrg.MRGNode'>\n",
+      "- STN: <class 'PySONIC.neurons.stn.OtsukaSTN'>\n",
+      "- SUseg: <class 'PySONIC.neurons.sundt.SundtSegment'>\n",
+      "- SWnode: <class 'PySONIC.neurons.sweeney.SweeneyNode'>\n",
+      "- template: <class 'PySONIC.neurons.template.TemplateNeuron'>\n",
+      "- RE: <class 'PySONIC.neurons.thalamic.ThalamicRE'>\n",
+      "- TC: <class 'PySONIC.neurons.thalamic.ThalamoCortical'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k, v in getNeuronsDict().items():\n",
+    "    print(f'- {k}: {v}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will choose here a cortical regular spiking (RS) neuron, by providing its key to the `getPointNeuron` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pneuron = getPointNeuron('RS')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define a stimulus\n",
+    "\n",
+    "In PySONIC, a stimulus is defined by the combination of 2 objects:\n",
+    "\n",
+    "#### 1. Stimulus drive\n",
+    "\n",
+    "The \"drive\" object specifies the fundamental properties of the stimulus (e.g. nominal amplitude, carrier frequency). \n",
+    "\n",
+    "Let's start by simulating our point-neuron model upon electrical stimulation (using a standard Hodgkin-Huxley paradigm). To do so, we define an `ElectricDrive` object that specifies a current density (in mA/m2) injected intracellularly into our point neuron model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 20.  # mA/m2\n",
+    "ELdrive = ElectricDrive(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Time protocol\n",
+    "\n",
+    "The \"time protocol\" object specfying how the stimulus drive amplitude is modulated over time (e.g. continous stimulation, pulsed stimulation, ...).\n",
+    "\n",
+    "We define here a standard `PulsedProtocol` consisting of a unique pulse of 100 ms, starting with a delay of 10 ms, and followed by a 100 ms offset where the stimulus is turned off:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tstart = 0e-3    # s\n",
+    "tpulse = 100e-3   # s\n",
+    "toffset = 100e-3  # s\n",
+    "pp = PulsedProtocol(tpulse, toffset, tstart=tstart)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run a simulation\n",
+    "\n",
+    "Let's simulate our point-neuron model upon application of this pulsed electrical stimulus, by calling the model's `simulate` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:08: CorticalRS: simulation @ I = 20.0mA/m2, tstim = 100ms, toffset = 100ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "data, meta = pneuron.simulate(ELdrive, pp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, this method returns a `(data, meta)` tuple.\n",
+    "\n",
+    "The `data` field is a dataframe containing the resulting timeseries of all the differential variables of the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>t</th>\n",
+       "      <th>stimstate</th>\n",
+       "      <th>Qm</th>\n",
+       "      <th>Vm</th>\n",
+       "      <th>m</th>\n",
+       "      <th>h</th>\n",
+       "      <th>n</th>\n",
+       "      <th>p</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000719</td>\n",
+       "      <td>-71.900000</td>\n",
+       "      <td>0.000451</td>\n",
+       "      <td>0.999926</td>\n",
+       "      <td>0.002227</td>\n",
+       "      <td>0.024364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000719</td>\n",
+       "      <td>-71.900000</td>\n",
+       "      <td>0.000451</td>\n",
+       "      <td>0.999926</td>\n",
+       "      <td>0.002227</td>\n",
+       "      <td>0.024364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000719</td>\n",
+       "      <td>-71.900000</td>\n",
+       "      <td>0.000451</td>\n",
+       "      <td>0.999926</td>\n",
+       "      <td>0.002227</td>\n",
+       "      <td>0.024364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.000719</td>\n",
+       "      <td>-71.900000</td>\n",
+       "      <td>0.000451</td>\n",
+       "      <td>0.999926</td>\n",
+       "      <td>0.002227</td>\n",
+       "      <td>0.024364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.00005</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.000718</td>\n",
+       "      <td>-71.800019</td>\n",
+       "      <td>0.000454</td>\n",
+       "      <td>0.999926</td>\n",
+       "      <td>0.002228</td>\n",
+       "      <td>0.024364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         t  stimstate        Qm         Vm         m         h         n  \\\n",
+       "0  0.00000        0.0 -0.000719 -71.900000  0.000451  0.999926  0.002227   \n",
+       "1  0.00000        0.0 -0.000719 -71.900000  0.000451  0.999926  0.002227   \n",
+       "2  0.00000        0.0 -0.000719 -71.900000  0.000451  0.999926  0.002227   \n",
+       "3  0.00000        1.0 -0.000719 -71.900000  0.000451  0.999926  0.002227   \n",
+       "4  0.00005        1.0 -0.000718 -71.800019  0.000454  0.999926  0.002228   \n",
+       "\n",
+       "          p  \n",
+       "0  0.024364  \n",
+       "1  0.024364  \n",
+       "2  0.024364  \n",
+       "3  0.024364  \n",
+       "4  0.024364  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `meta` dictionary stores information about the simulation (model type, stimulus, pulsed protocol) that can be used to recreate an identical simulation if needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- simkey: ESTIM\n",
+      "- model: {'neuron': 'RS'}\n",
+      "- drive: ElectricDrive(I=20.0mA/m2)\n",
+      "- pp: PulsedProtocol(tstim=100ms, toffset=100ms)\n",
+      "- tcomp: 0.18283270000000051\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k, v in meta.items():\n",
+    "    print(f'- {k}: {v}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize results\n",
+    "\n",
+    "#### Plot all timeseries of a simulation\n",
+    "\n",
+    "Simulation results can be easily visualized by feeding the simulation output (the `(data, meta)` tuple) into a `GroupedTimeSeries` object. Note that this object can accept outputs from multiple simulations, hence the output must be wrapped in a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see here the evolution of the neuron's membrane charge density, membrane potential and membrane currents, as well as that of the gating states of its constituent ion channels. The gray stripe indicates when the stimulus is turned on by the pulsed protocol."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare timeseries from different simulations\n",
+    "\n",
+    "Alternatively, you can compare results of 2 or more simulations different only by a single parameter within a single graph, using a `CompTimeSeries` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:09: CorticalRS: simulation @ I = 30.0mA/m2, tstim = 100ms, toffset = 100ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "out = pneuron.simulate(ElectricDrive(30.), pp)  # Run simulation with different current amplitude\n",
+    "outputs = [(data, meta), out]  # wrap the two simulation outputs into a list\n",
+    "fig = CompTimeSeries(outputs, 'Vm').render()  # compare membrane potential profiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trying different protocols\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A more complex pulsed protocol\n",
+    "\n",
+    "First, let's try a more complex `PulsedProtocol` consisting of multiple pulses repeated at a given frequency and with a given duty cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:10: CorticalRS: simulation @ I = 20.0mA/m2, tstim = 100ms, toffset = 100ms, PRF = 100.00Hz, DC = 50.0%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tburst = 100e-3  # ms\n",
+    "PRF = 100.       # Hz\n",
+    "DC = 0.5         # -\n",
+    "pp2 = PulsedProtocol(tburst, toffset, PRF=PRF, DC=DC, tstart=tstart)\n",
+    "data, meta = pneuron.simulate(ELdrive, pp2)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Burst protocol\n",
+    "\n",
+    "Second, let's try a `BurstProtocol` that combines 2 \"pulsing layers\" by repeating bursts of pulses at a given frequency:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:11: CorticalRS: simulation @ I = 20.0mA/m2, tburst = 100ms, PRF = 100.00Hz, DC = 50.0%, BRF = 2.0Hz, nbursts = 3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "BRF = 2.0    # Hz\n",
+    "nbursts = 3  # -\n",
+    "bp = BurstProtocol(tburst, PRF=PRF, DC=DC, BRF=BRF, nbursts=nbursts, tstart=tstart)\n",
+    "data, meta = pneuron.simulate(ELdrive, bp)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Custom protocol\n",
+    "\n",
+    "Finally, let's try a `CustomProtocol` defined by a list of time instants and modulation amplitudes describing how the drive is modulated over time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:13: CorticalRS: simulation @ I = 20.0mA/m2, tevents = (10.00m, 30.00m, 50.00m)s, xevents = (1.0, 2.0, 0.0), tstop = 100ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mod_times = [10e-3, 30e-3, 50e-3]\n",
+    "mod_amps = [1., 2., 0.]\n",
+    "cp = CustomProtocol(mod_times, mod_amps, toffset)\n",
+    "data, meta = pneuron.simulate(ELdrive, cp)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The different shades of gray indicate here the relative modulation amplitudes of the stimulus over time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ultrasonic stimulation\n",
+    "\n",
+    "Let's now move on to the main purpose of the PySONIC framework: to simulate neural dynamics upon ultrasonic stimulation. For this purpose, PySONIC uses the **SONIC model**, a computationally efficient paradigm of the *Neuronal Intramembrane Cavitation Excitation (NICE)* model that represents the mechano-electrical dynamics of a nanoscale membrane structure resonating to ultrasound, so-called *bilayer sonophore*. \n",
+    "\n",
+    "#### Neuronal Bilayer Sonophore\n",
+    "\n",
+    "To simulate the behavior of a poin-neuron model upon ultrasonic stimulation, one must first define a `NeuronalBilayerSonophore` object. This is done by specifying a sonophore radius and an associated point-neuron object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 32e-9  # sonophore radius (m)\n",
+    "nbls = NeuronalBilayerSonophore(a, pneuron)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Acoustic drive\n",
+    "\n",
+    "Then, one must define an `AcousticDrive` object specifying the carrier frequency (in Hz) and peak pressure amplitude (in Pa) of the ultrasound stimulus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fdrive = 500e3  # Hz\n",
+    "Adrive = 100e3  # Pa\n",
+    "USdrive = AcousticDrive(Fdrive, Adrive)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulations\n",
+    "\n",
+    "Similarly as for electrical stimulation, acoustic stimulation can be simulated by calling the `simulate` method of the instantiated `NeuronalBilayerSonophore` object, with the appropriate acoustic drive and time protocol.\n",
+    "\n",
+    "#### Single-pulse protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:14: NeuronalBilayerSonophore(32.0 nm, CorticalRS): sonic simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 100ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data, meta = nbls.simulate(USdrive, pp)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pulsed protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:20: NeuronalBilayerSonophore(32.0 nm, CorticalRS): sonic simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 100ms, PRF = 100.00Hz, DC = 50.0%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data, meta = nbls.simulate(USdrive, pp2)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Burst protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:21: NeuronalBilayerSonophore(32.0 nm, CorticalRS): sonic simulation @ f = 500kHz, A = 100.00kPa, tburst = 100ms, PRF = 100.00Hz, DC = 50.0%, BRF = 2.0Hz, nbursts = 3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data, meta = nbls.simulate(USdrive, bp)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Custom protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:25: NeuronalBilayerSonophore(32.0 nm, CorticalRS): sonic simulation @ f = 500kHz, A = 100.00kPa, tevents = (10.00m, 30.00m, 50.00m)s, xevents = (1.0, 2.0, 0.0), tstop = 100ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x648 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data, meta = nbls.simulate(USdrive, cp)\n",
+    "fig = GroupedTimeSeries([(data, meta)]).render()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combining electrical and ultrasonic stimulation\n",
+    "\n",
+    "The PySONIC framework also allows to simulate neural dynamics upon combined electrical and ultrasonic stimulation.\n",
+    "\n",
+    "This feature is still in development. At the moment, the electrical stimulus can only be defined as a constant injected current, while the acoustic stimulus is a full-fledged stimulus. \n",
+    "\n",
+    "To perform this combined simulation, you must define a `DrivenNeuronalBilayerSonophore` object by specifying a driving current amplitude, a sonophore radius and a point-neuron model. The simulation call is unchanged.  \n",
+    "\n",
+    "For instance, we compare compare here the behavior of the regular spiking neuron upon ultrasonic stimulation, while driven by different electrical current densities:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 02/03/2021 14:59:27: DrivenNeuronalBilayerSonophore(32.0 nm, CorticalRS, Idrive = -5.00 mA/m2): sonic simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 100ms\n",
+      " 02/03/2021 14:59:29: DrivenNeuronalBilayerSonophore(32.0 nm, CorticalRS, Idrive = 0.00 mA/m2): sonic simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 100ms\n",
+      " 02/03/2021 14:59:34: DrivenNeuronalBilayerSonophore(32.0 nm, CorticalRS, Idrive = 5.00 mA/m2): sonic simulation @ f = 500kHz, A = 100.00kPa, tstim = 100ms, toffset = 100ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Idrives = [-5.0, 0., 5.0]  # mA/m2\n",
+    "outputs = []\n",
+    "for Idrive in Idrives:\n",
+    "    dnbls = DrivenNeuronalBilayerSonophore(Idrive, a, pneuron)\n",
+    "    outputs.append(dnbls.simulate(USdrive, pp))\n",
+    "fig = CompTimeSeries(outputs, 'Qm').render(labels=[f'Idrive = {x:.1f} mA/m2' for x in Idrives])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/fiber nodes analysis.ipynb b/notebooks/fibers/fiber nodes analysis.ipynb
similarity index 100%
rename from notebooks/fiber nodes analysis.ipynb
rename to notebooks/fibers/fiber nodes analysis.ipynb
diff --git a/notebooks/threshold search procedure.ipynb b/notebooks/threshold/threshold search procedure.ipynb
similarity index 100%
rename from notebooks/threshold search procedure.ipynb
rename to notebooks/threshold/threshold search procedure.ipynb
diff --git a/examples/actmaps.py b/scripts/run_actmaps.py
similarity index 100%
rename from examples/actmaps.py
rename to scripts/run_actmaps.py