"""
Definition of default parameters (and hence, standard parameter names) for
standard cell models.
Plain integrate-and-fire models:
IF_curr_exp
IF_curr_alpha
IF_cond_exp
IF_cond_alpha
Integrate-and-fire with adaptation:
IF_cond_exp_gsfa_grr
EIF_cond_alpha_isfa_ista
EIF_cond_exp_isfa_ista
Integrate-and-fire model for use with the FACETS hardware
IF_facets_hardware1
Hodgkin-Huxley model
HH_cond_exp
Spike sources (input neurons)
SpikeSourcePoisson
SpikeSourceArray
SpikeSourceInhGamma
:copyright: Copyright 2006-2016 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""
from pyNN.standardmodels import StandardCellType
from pyNN.parameters import Sequence
[docs]class IF_curr_alpha(StandardCellType):
"""
Leaky integrate and fire model with fixed threshold and alpha-function-
shaped post-synaptic current.
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 0.5, # Rise time of the excitatory synaptic alpha function in ms.
'tau_syn_I': 0.5, # Rise time of the inhibitory synaptic alpha function in ms.
'i_offset': 0.0, # Offset current in nA
'v_reset': -65.0, # Reset potential after a spike in mV.
'v_thresh': -50.0, # Spike threshold in mV.
}
recordable = ['spikes', 'v']
conductance_based = False
default_initial_values = {
'v': -65.0, # 'v_rest',
'isyn_exc': 0.0,
'isyn_inh': 0.0,
}
units = {
'v': 'mV',
'isyn_exc': 'nA',
'isyn_inh': 'nA',
}
[docs]class IF_curr_exp(StandardCellType):
"""
Leaky integrate and fire model with fixed threshold and
decaying-exponential post-synaptic current. (Separate synaptic currents for
excitatory and inhibitory synapses.
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 5.0, # Decay time of excitatory synaptic current in ms.
'tau_syn_I': 5.0, # Decay time of inhibitory synaptic current in ms.
'i_offset': 0.0, # Offset current in nA
'v_reset': -65.0, # Reset potential after a spike in mV.
'v_thresh': -50.0, # Spike threshold in mV.
}
recordable = ['spikes', 'v']
conductance_based = False
default_initial_values = {
'v': -65.0, # 'v_rest',
'isyn_exc': 0.0,
'isyn_inh': 0.0,
}
units = {
'v': 'mV',
'isyn_exc': 'nA',
'isyn_inh': 'nA',
}
[docs]class IF_cond_alpha(StandardCellType):
"""
Leaky integrate and fire model with fixed threshold and alpha-function-
shaped post-synaptic conductance.
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 0.3, # Rise time of the excitatory synaptic alpha function in ms.
'tau_syn_I': 0.5, # Rise time of the inhibitory synaptic alpha function in ms.
'e_rev_E': 0.0, # Reversal potential for excitatory input in mV
'e_rev_I': -70.0, # Reversal potential for inhibitory input in mV
'v_thresh': -50.0, # Spike threshold in mV.
'v_reset': -65.0, # Reset potential after a spike in mV.
'i_offset': 0.0, # Offset current in nA
}
recordable = ['spikes', 'v', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -65.0, # 'v_rest',
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
[docs]class IF_cond_exp(StandardCellType):
"""
Leaky integrate and fire model with fixed threshold and
exponentially-decaying post-synaptic conductance.
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 5.0, # Decay time of the excitatory synaptic conductance in ms.
'tau_syn_I': 5.0, # Decay time of the inhibitory synaptic conductance in ms.
'e_rev_E': 0.0, # Reversal potential for excitatory input in mV
'e_rev_I': -70.0, # Reversal potential for inhibitory input in mV
'v_thresh': -50.0, # Spike threshold in mV.
'v_reset': -65.0, # Reset potential after a spike in mV.
'i_offset': 0.0, # Offset current in nA
}
recordable = ['spikes', 'v', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -65.0, # 'v_rest',
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
[docs]class IF_cond_exp_gsfa_grr(StandardCellType):
"""
Linear leaky integrate and fire model with fixed threshold,
decaying-exponential post-synaptic conductance, conductance based
spike-frequency adaptation, and a conductance-based relative refractory
mechanism.
See: Muller et al (2007) Spike-frequency adapting neural ensembles: Beyond
mean-adaptation and renewal theories. Neural Computation 19: 2958-3010.
See also: EIF_cond_alpha_isfa_ista
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 5.0, # Decay time of the excitatory synaptic conductance in ms.
'tau_syn_I': 5.0, # Decay time of the inhibitory synaptic conductance in ms.
'e_rev_E': 0.0, # Reversal potential for excitatory input in mV
'e_rev_I': -70.0, # Reversal potential for inhibitory input in mV
'v_thresh': -50.0, # Spike threshold in mV.
'v_reset': -65.0, # Reset potential after a spike in mV.
'i_offset': 0.0, # Offset current in nA
'tau_sfa': 100.0, # Time constant of spike-frequency adaptation in ms
'e_rev_sfa': -75.0, # spike-frequency adaptation conductance reversal potential in mV
'q_sfa': 15.0, # Quantal spike-frequency adaptation conductance increase in nS
'tau_rr': 2.0, # Time constant of the relative refractory mechanism in ms
'e_rev_rr': -75.0, # relative refractory mechanism conductance reversal potential in mV
'q_rr': 3000.0 # Quantal relative refractory conductance increase in nS
}
recordable = ['spikes', 'v', 'g_r', 'g_s', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -65.0, # 'v_rest',
'g_r': 0.0,
'g_s': 0.0,
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'g_r': 'nS',
'g_s': 'nS',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
class IF_facets_hardware1(StandardCellType):
"""
Leaky integrate and fire model with conductance-based synapses and fixed
threshold as it is resembled by the FACETS Hardware Stage 1.
The following parameters can be assumed for a corresponding software
simulation: cm = 0.2 nF, tau_refrac = 1.0 ms, e_rev_E = 0.0 mV.
For further details regarding the hardware model see the FACETS-internal Wiki:
https://facets.kip.uni-heidelberg.de/private/wiki/index.php/WP7_NNM
"""
default_parameters = {
'g_leak': 40.0, # nS
'tau_syn_E': 30.0, # ms
'tau_syn_I': 30.0, # ms
'v_reset': -80.0, # mV
'e_rev_I': -80.0, # mV,
'v_rest': -65.0, # mV
'v_thresh': -55.0 # mV
}
recordable = ['spikes', 'v', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -65.0, # 'v_rest',
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
class HH_cond_exp(StandardCellType):
"""Single-compartment Hodgkin-Huxley model.
Reference:
Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991.
"""
default_parameters = {
'gbar_Na': 20.0, # uS
'gbar_K': 6.0, # uS
'g_leak': 0.01, # uS
'cm': 0.2, # nF
'v_offset': -63.0, # mV
'e_rev_Na': 50.0,
'e_rev_K': -90.0,
'e_rev_leak': -65.0,
'e_rev_E': 0.0,
'e_rev_I': -80.0,
'tau_syn_E': 0.2, # ms
'tau_syn_I': 2.0,
'i_offset': 0.0, # nA
}
recordable = ['spikes', 'v', 'gsyn_exc', 'gsyn_inh']
receptor_types = ('excitatory', 'inhibitory', 'source_section.gap')
default_initial_values = {
'v': -65.0, # 'v_rest',
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
[docs]class EIF_cond_alpha_isfa_ista(StandardCellType):
"""
Exponential integrate and fire neuron with spike triggered and
sub-threshold adaptation currents (isfa, ista reps.) according to:
Brette R and Gerstner W (2005) Adaptive Exponential Integrate-and-Fire Model
as an Effective Description of Neuronal Activity. J Neurophysiol 94:3637-3642
See also: IF_cond_exp_gsfa_grr, EIF_cond_exp_isfa_ista
"""
default_parameters = {
'cm': 0.281, # Capacitance of the membrane in nF
'tau_refrac': 0.1, # Duration of refractory period in ms.
'v_spike': -40.0, # Spike detection threshold in mV.
'v_reset': -70.6, # Reset value for V_m after a spike. In mV.
'v_rest': -70.6, # Resting membrane potential (Leak reversal potential) in mV.
'tau_m': 9.3667, # Membrane time constant in ms
'i_offset': 0.0, # Offset current in nA
'a': 4.0, # Subthreshold adaptation conductance in nS.
'b': 0.0805, # Spike-triggered adaptation in nA
'delta_T': 2.0, # Slope factor in mV
'tau_w': 144.0, # Adaptation time constant in ms
'v_thresh': -50.4, # Spike initiation threshold in mV
'e_rev_E': 0.0, # Excitatory reversal potential in mV.
'tau_syn_E': 5.0, # Rise time of excitatory synaptic conductance in ms (alpha function).
'e_rev_I': -80.0, # Inhibitory reversal potential in mV.
'tau_syn_I': 5.0, # Rise time of the inhibitory synaptic conductance in ms (alpha function).
}
recordable = ['spikes', 'v', 'w', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -70.6, # 'v_rest',
'w': 0.0,
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'w': 'nA',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
[docs]class EIF_cond_exp_isfa_ista(StandardCellType):
"""
Exponential integrate and fire neuron with spike triggered and
sub-threshold adaptation currents (isfa, ista reps.) according to:
Brette R and Gerstner W (2005) Adaptive Exponential Integrate-and-Fire Model
as an Effective Description of Neuronal Activity. J Neurophysiol 94:3637-3642
See also: IF_cond_exp_gsfa_grr, EIF_cond_alpha_isfa_ista
"""
default_parameters = {
'cm': 0.281, # Capacitance of the membrane in nF
'tau_refrac': 0.1, # Duration of refractory period in ms.
'v_spike': -40.0, # Spike detection threshold in mV.
'v_reset': -70.6, # Reset value for V_m after a spike. In mV.
'v_rest': -70.6, # Resting membrane potential (Leak reversal potential) in mV.
'tau_m': 9.3667, # Membrane time constant in ms
'i_offset': 0.0, # Offset current in nA
'a': 4.0, # Subthreshold adaptation conductance in nS.
'b': 0.0805, # Spike-triggered adaptation in nA
'delta_T': 2.0, # Slope factor in mV
'tau_w': 144.0, # Adaptation time constant in ms
'v_thresh': -50.4, # Spike initiation threshold in mV
'e_rev_E': 0.0, # Excitatory reversal potential in mV.
'tau_syn_E': 5.0, # Decay time constant of excitatory synaptic conductance in ms.
'e_rev_I': -80.0, # Inhibitory reversal potential in mV.
'tau_syn_I': 5.0, # Decay time constant of the inhibitory synaptic conductance in ms.
}
recordable = ['spikes', 'v', 'w', 'gsyn_exc', 'gsyn_inh']
default_initial_values = {
'v': -70.6, # 'v_rest',
'w': 0.0,
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'w': 'nA',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
}
[docs]class Izhikevich(StandardCellType):
"""
Izhikevich spiking model with a quadratic non-linearity according to:
E. Izhikevich (2003), IEEE transactions on neural networks, 14(6)
dv/dt = 0.04*v^2 + 5*v + 140 - u + I
du/dt = a*(b*v - u)
Synapses are modeled as Dirac delta currents (voltage step), as in the original model
NOTE: name should probably be changed to match standard nomenclature,
e.g. QIF_cond_delta_etc_etc, although keeping "Izhikevich" as an alias would be good
"""
default_parameters = {
'a': 0.02, # (/ms)
'b': 0.2, # (/ms)
'c': -65.0, # (mV) aka 'v_reset'
'd': 2.0, # (mV/ms) Reset value for u after a spike.
'i_offset': 0.0 # (nA)
}
recordable = ['spikes', 'v', 'u']
conductance_based = False
voltage_based_synapses = True
default_initial_values = {
'v': -70.0, # mV
'u': -14.0 # mV/ms
}
units = {
'v': 'mV',
'u': 'mV/ms',
}
class GIF_cond_exp(StandardCellType):
"""
The GIF model is a leaky integrate-and-fire model including a spike-triggered current eta(t),
a moving threshold gamma(t) and stochastic spike emission.
References:
[1] Mensi, S., Naud, R., Pozzorini, C., Avermann, M., Petersen, C. C., &
Gerstner, W. (2012). Parameter extraction and classification of three cortical
neuron types reveals two distinct adaptation mechanisms.
Journal of Neurophysiology, 107(6), 1756-1775.
[2] Pozzorini, C., Mensi, S., Hagens, O., Naud, R., Koch, C., & Gerstner, W.
(2015). Automated High-Throughput Characterization of Single Neurons by Means of
Simplified Spiking Models. PLoS Comput Biol, 11(6), e1004275.
"""
default_parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 4.0, # Duration of refractory period in ms.
'tau_syn_E': 5.0, # Decay time of the excitatory synaptic conductance in ms.
'tau_syn_I': 5.0, # Decay time of the inhibitory synaptic conductance in ms.
'e_rev_E': 0.0, # Reversal potential for excitatory input in mV
'e_rev_I': -70.0, # Reversal potential for inhibitory input in mV
'v_reset': -65.0, # Reset potential after a spike in mV.
'i_offset': 0.0, # Offset current in nA
'delta_v': 0.5, # Threshold sharpness in mV.
'v_t_star': -48.0, # Threshold baseline in mV.
'lambda0': 1.0, # Firing intensity at threshold in Hz.
'tau_eta1': 1.0, # }
'tau_eta2': 10.0, # } Time constants for spike-triggered current in ms.
'tau_eta3': 100.0, # }
'tau_gamma1': 1.0, # }
'tau_gamma2': 10.0, # } Time constants for spike-frequency adaptation in ms.
'tau_gamma3': 100.0, # }
'a_eta1': 1.0, # }
'a_eta2': 1.0, # } Post-spike increments for spike-triggered current in nA
'a_eta3': 1.0, # }
'a_gamma1': 1.0, # }
'a_gamma2': 1.0, # } Post-spike increments for moving threshold in mV
'a_gamma3': 1.0, # }
}
recordable = ['spikes', 'v', 'gsyn_exc', 'gsyn_inh', 'i_eta', 'v_t']
default_initial_values = {
'v': -65.0,
'v_t': -48.0,
'i_eta': 0.0,
'gsyn_exc': 0.0,
'gsyn_inh': 0.0,
}
units = {
'v': 'mV',
'gsyn_exc': 'uS',
'gsyn_inh': 'uS',
'i_eta': 'nA',
'v_t': 'mV',
}
[docs]class SpikeSourcePoisson(StandardCellType):
"""Spike source, generating spikes according to a Poisson process."""
default_parameters = {
'rate': 1.0, # Mean spike frequency (Hz)
'start': 0.0, # Start time (ms)
'duration': 1e10 # Duration of spike sequence (ms)
}
recordable = ['spikes']
injectable = False
receptor_types = ()
class SpikeSourcePoissonRefractory(StandardCellType):
"""Spike source, generating spikes according to a Poisson process with dead time"""
default_parameters = {
'rate': 1.0, # Mean spike frequency (Hz)
'tau_refrac': 0.0, # Minimum time between spikes (ms)
'start': 0.0, # Start time (ms)
'duration': 1e10 # Duration of spike sequence (ms)
}
recordable = ['spikes']
injectable = False
receptor_types = ()
class SpikeSourceGamma(StandardCellType):
"""Spike source, generating spikes according to a gamma process.
The mean inter-spike interval is given by alpha/beta
"""
default_parameters = {
'alpha': 2, # shape (order) parameter of the gamma distribution
'beta': 1.0, # rate parameter of the gamma distribution (Hz)
'start': 0.0, # Start time (ms)
'duration': 1e10, # Duration of spike sequence (ms)
}
recordable = ['spikes']
injectable = False
receptor_types = ()
[docs]class SpikeSourceInhGamma(StandardCellType):
"""
Spike source, generating realizations of an inhomogeneous gamma process,
employing the thinning method.
See: Muller et al (2007) Spike-frequency adapting neural ensembles: Beyond
mean-adaptation and renewal theories. Neural Computation 19: 2958-3010.
"""
default_parameters = {
'a': Sequence([1.0]), # time histogram of parameter a of a gamma distribution (dimensionless)
'b': Sequence([1.0]), # time histogram of parameter b of a gamma distribution (seconds)
'tbins': Sequence([0.0]), # time bins of the time histogram of a,b in units of ms
'start': 0.0, # Start time (ms)
'duration': 1e10 # Duration of spike sequence (ms)
}
recordable = ['spikes']
injectable = False
receptor_types = ()
[docs]class SpikeSourceArray(StandardCellType):
"""Spike source generating spikes at the times given in the spike_times array."""
default_parameters = {'spike_times': Sequence([])} # list or numpy array containing spike times in milliseconds.
recordable = ['spikes']
injectable = False
receptor_types = ()
