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add eprop-iaf-adapt NEST model #808

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be4e239
add eprop-iaf-adapt NEST model
HarshKhilawala Jul 24, 2024
f85a71d
add eprop-iaf NEST model
HarshKhilawala Jul 24, 2024
6663435
add eprop-iaf-readout NEST model
HarshKhilawala Jul 24, 2024
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71 changes: 71 additions & 0 deletions pyNN/nest/standardmodels/cells.py
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Original file line number Diff line number Diff line change
Expand Up @@ -397,6 +397,77 @@ class EIF_cond_exp_isfa_ista(cells.EIF_cond_exp_isfa_ista):
standard_receptor_type = True


class IF_eprop_adaptive(cells.IF_eprop_adaptive):

__doc__ = cells.IF_eprop_adaptive.__doc__

translations = build_translations(
('adapt_beta', 'adapt_beta'),
('adapt_tau', 'adapt_tau'),
('cm', 'C_m', 1000.0), # nF -> pF
('c_reg', 'c_reg'),
('v_rest', 'E_L'),
('f_target', 'f_target'),
('gamma', 'gamma'),
('i_offset', 'I_e', 1000.0), # nA -> pA
('reg_spike_arr', 'regular_spike_arrival'),
('surrogate_gradient_function', 'surrogate_gradient_function'),
('tau_refrac', 't_ref'),
('tau_m', 'tau_m'),
('v_min', 'V_min'),
('v_thresh', 'V_th'),
)
variable_map = {'v': 'V_m', 'v_th_adapt': 'V_th_adapt', 'adaptation': 'adaptation', 'learning_signal': 'learning_signal', 'surrogate_gradient': 'surrogate_gradient'}
scale_factors = {'v': 1, 'v_th_adapt': 1, 'adaptation': 1, 'learning_signal': 1, 'surrogate_gradient': 1}
nest_name = {"on_grid": "eprop_iaf_adapt_bsshslm_2020",
"off_grid": "eprop_iaf_adapt_bsshslm_2020"}
standard_receptor_type = True


class IF_eprop(cells.IF_eprop):

__doc__ = cells.IF_eprop.__doc__

translations = build_translations(
('cm', 'C_m', 1000.0), # nF -> pF
('c_reg', 'c_reg'),
('v_rest', 'E_L'),
('f_target', 'f_target'),
('gamma', 'gamma'),
('i_offset', 'I_e', 1000.0), # nA -> pA
('reg_spike_arr', 'regular_spike_arrival'),
('surrogate_gradient_function', 'surrogate_gradient_function'),
('tau_refrac', 't_ref'),
('tau_m', 'tau_m'),
('v_min', 'V_min'),
('v_thresh', 'V_th'),
)
variable_map = {'v': 'V_m', 'learning_signal': 'learning_signal', 'surrogate_gradient': 'surrogate_gradient'}
scale_factors = {'v': 1, 'learning_signal': 0.001, 'surrogate_gradient': 1}
nest_name = {"on_grid": "eprop_iaf_bsshslm_2020",
"off_grid": "eprop_iaf_bsshslm_2020"}
standard_receptor_type = True


class IF_eprop_readout(cells.IF_eprop_readout):

__doc__ = cells.IF_eprop_readout.__doc__

translations = build_translations(
('cm', 'C_m', 1000.0), # nF -> pF
('v_rest', 'E_L'),
('i_offset', 'I_e', 1000.0), # nA -> pA
('reg_spike_arr', 'regular_spike_arrival'),
('tau_m', 'tau_m'),
('v_min', 'V_min'),
)
variable_map = {'v': 'V_m', 'error signal': 'error signal', 'readout_signal': 'readout_signal', 'readout_signal_unnorm': 'readout_signal_unnorm', 'target_signal': 'target_signal' }
scale_factors = {'v': 1, 'error signal': 1, 'readout_signal': 1, 'readout_signal_unnorm': 1, 'target_signal': 1}
nest_name = {"on_grid": "eprop_iaf_readout_bsshslm_2020",
"off_grid": "eprop_iaf_readout_bsshslm_2020"}
standard_receptor_type = True


class Izhikevich(cells.Izhikevich):
__doc__ = cells.Izhikevich.__doc__

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146 changes: 146 additions & 0 deletions pyNN/standardmodels/cells.py
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Original file line number Diff line number Diff line change
Expand Up @@ -685,6 +685,152 @@ def computed_parameters_include(self, parameter_names):
)


class IF_eprop_adaptive(StandardCellType):
"""
Implementation of a leaky integrate-and-fire neuron model with delta-shaped
postsynaptic currents and threshold adaptation used for
eligibility propagation (e-prop) plasticity.

Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R, Maass W (2020).
A solution to the learning dilemma for recurrent networks of spiking neurons.
Nature Communications, 11:3625. https://doi.org/10.1038/s41467-020-17236-y
"""

default_parameters = {
'adapt_beta': 1.0, # Prefactor of threshold adaptation
'adapt_tau': 10.0, # Time constant of threshold adaptation in ms
'cm': 0.250, # Membrane capacitance in nF
'c_reg': 0.0, # Regularization factor
'v_rest': -70.0, # Resting membrane potential in mV
'f_target': 10.0, # Target firing rate of regularization factor in Hz
'gamma': 0.3, # Scaling of surrogate gradient
'i_offset': 0.0, # Offset current in nA
'reg_spike_arr': True, # Regularize spike arrival times
'surrogate_gradient_function': 'piecewise_linear', # Surrogate gradient function
'tau_refrac': 2.0, # Refractory period in ms
'tau_m': 10.0, # Membrane time constant in ms
'v_min': -1.79e308, # Minimum membrane potential in mV
'v_thresh': -55.0, # Spike threshold in mV
}

recordable = ['v', 'spikes', 'adaptation', 'v_th_adapt', 'learning_signal', 'surrogate_gradient']

default_initial_values = {
'adaptation': 0.0, # Adaptation variable
'learning_signal': 0.0, # Learning signal
'surrogate_gradient': 0.0, # Surrogate gradient
'v': -70.0, # Membrane potential
'v_th_adapt': -55.0, # Threshold adaptation variable
}

units = {
'adapt_tau': 'ms',
'cm': 'nF',
'v_rest': 'mV',
'f_target': 'Hz',
'i_offset': 'nA',
't_ref': 'ms',
'tau_m': 'ms',
'v_min': 'mV',
'v_thresh': 'mV',
'v': 'mV',
'v_th_adapt': 'mV',
}


class IF_eprop(StandardCellType):
"""
Implementation of a leaky integrate-and-fire neuron model with delta-shaped
postsynaptic currents used for eligibility propagation (e-prop) plasticity.

Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R, Maass W (2020).
A solution to the learning dilemma for recurrent networks of spiking neurons.
Nature Communications, 11:3625. https://doi.org/10.1038/s41467-020-17236-y
"""

default_parameters = {
'cm': 0.250, # Membrane capacitance in nF
'c_reg': 0.0, # Regularization factor
'v_rest': -70.0, # Resting membrane potential in mV
'f_target': 10.0, # Target firing rate of regularization factor in Hz
'gamma': 0.3, # Scaling of surrogate gradient
'i_offset': 0.0, # Offset current in nA
'reg_spike_arr': True, # Regularize spike arrival times
'surrogate_gradient_function': 'piecewise_linear', # Surrogate gradient function
'tau_refrac': 2.0, # Refractory period in ms
'tau_m': 10.0, # Membrane time constant in ms
'v_min': -1.79e308, # Minimum membrane potential in mV
'v_thresh': -55.0, # Spike threshold in mV
}

recordable = ['v', 'spikes', 'learning_signal', 'surrogate_gradient']

default_initial_values = {
'learning_signal': 0.0, # Learning signal
'surrogate_gradient': 0.0, # Surrogate gradient
'v': -70.0, # Membrane potential
}

units = {
'cm': 'nF',
'v_rest': 'mV',
'f_target': 'Hz',
'i_offset': 'nA',
't_ref': 'ms',
'tau_m': 'ms',
'v_min': 'mV',
'v_thresh': 'mV',
'v': 'mV',
'learning_signal': 'nA',
}


class IF_eprop_readout(StandardCellType):
"""
Implementation of a integrate-and-fire neuron model with delta-shaped postsynaptic currents
used as readout neuron for eligibility propagation (e-prop) plasticity.

Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R, Maass W (2020).
A solution to the learning dilemma for recurrent networks of spiking neurons.
Nature Communications, 11:3625. https://doi.org/10.1038/s41467-020-17236-y
"""

default_parameters = {
'cm': 0.250, # Membrane capacitance in nF
'v_rest': -70.0, # Resting membrane potential in mV
'i_offset': 0.0, # Offset current in nA
'loss': 'mean_squared_error', # Loss function
'reg_spike_arr': True, # Regularize spike arrival times
'surrogate_gradient_function': 'piecewise_linear', # Surrogate gradient function
'tau_m': 10.0, # Membrane time constant in ms
'v_min': -1.79e308, # Minimum membrane potential in mV
}

recordable = ['v', 'spikes', 'error_signal', 'readout_signal', 'readout_signal_unnorm', 'target_signal']

default_initial_values = {
'error_signal': 0.0, # Error signal
'readout_signal': 0.0, # Readout signal
'readout_signal_unnorm': 0.0, # Unnormalized readout signal
'target_signal': 0.0, # Target signal
'v': -70.0, # Membrane potential
}

units = {
'cm': 'nF',
'v_rest': 'mV',
'i_offset': 'nA',
'tau_m': 'ms',
'v_min': 'mV',
'v_thresh': 'mV',
'v': 'mV',
'error_signal': 'mV',
'readout_signal': 'mV',
'readout_signal_unnorm': 'mV',
'target_signal': 'mV',
}


class Izhikevich(StandardCellType):
"""
Izhikevich spiking model with a quadratic non-linearity according to:
Expand Down