Standard models¶

Standard models are neuron models that are available in at least two of the simulation engines supported by PyNN. PyNN performs automatic translation of parameter names, types and units. Only a handful of models are currently available, but the list will be expanded in future releases. To obtain a list of all the standard models available in a given simulator, use the list_standard_models() function, e.g.:

>>> from pyNN import neuron
>>> neuron.list_standard_models()
['IF_cond_alpha', 'IF_curr_exp', 'IF_cond_exp', 'EIF_cond_exp_isfa_ista',
 'SpikeSourceArray', 'HH_cond_exp', 'IF_cond_exp_gsfa_grr',
 'IF_facets_hardware1', 'SpikeSourcePoisson', 'EIF_cond_alpha_isfa_ista',
 'IF_curr_alpha']

Neurons¶

IF_curr_alpha¶

Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic current.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`v_rest`	-65.0	mV	Resting membrane potential
`cm`	1.0	nF	Capacity of the membrane
`tau_m`	20.0	ms	Membrane time constant
`tau_refrac`	0.0	ms	Duration of refractory period
`tau_syn_E`	5.0	ms	Rise time of the excitatory synaptic alpha function
`tau_syn_I`	5.0	ms	Rise time of the inhibitory synaptic alpha function
`i_offset`	0.0	nA	Offset current
`v_reset`	-65.0	mV	Reset potential after a spike
`v_thresh`	-50.0	mV	Spike threshold

IF_curr_exp¶

Leaky integrate and fire model with fixed threshold and decaying-exponential post-synaptic current. (Separate synaptic currents for excitatory and inhibitory synapses.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`v_rest`	-65.0	mV	Resting membrane potential
`cm`	1.0	nF	Capacity of the membrane
`tau_m`	20.0	ms	Membrane time constant
`tau_refrac`	0.0	ms	Duration of refractory period
`tau_syn_E`	5.0	ms	Decay time of excitatory synaptic current
`tau_syn_I`	5.0	ms	Decay time of inhibitory synaptic current
`i_offset`	0.0	nA	Offset current
`v_reset`	-65.0	mV	Reset potential after a spike
`v_thresh`	-50.0	mV	Spike threshold

IF_cond_alpha¶

Leaky integrate and fire model with fixed threshold and alpha-function-shaped post-synaptic conductance.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`v_rest`	-65.0	mV	Resting membrane potential
`cm`	1.0	nF	Capacity of the membrane
`tau_m`	20.0	ms	Membrane time constant
`tau_refrac`	0.0	ms	Duration of refractory period
`tau_syn_E`	5.0	ms	Rise time of the excitatory synaptic alpha function
`tau_syn_I`	5.0	ms	Rise time of the inhibitory synaptic alpha function
`e_rev_E`	0.0	mV	Reversal potential for excitatory input
`e_rev_I`	-70.0	mV	Reversal potential for inhibitory input
`v_thresh`	-50.0	mV	Spike threshold
`v_reset`	-65.0	mV	Reset potential after a spike
`i_offset`	0.0	nA	Offset current

IF_cond_exp¶

Leaky integrate and fire model with fixed threshold and decaying-exponential post-synaptic conductance.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`v_rest`	-65.0	mV	Resting membrane potential
`cm`	1.0	nF	Capacity of the membrane
`tau_m`	20.0	ms	Membrane time constant
`tau_refrac`	0.0	ms	Duration of refractory period
`tau_syn_E`	5.0	ms	Decay time of the excitatory synaptic conductance
`tau_syn_I`	5.0	ms	Decay time of the inhibitory synaptic conductance
`e_rev_E`	0.0	mV	Reversal potential for excitatory input
`e_rev_I`	-70.0	mV	Reversal potential for inhibitory input
`v_thresh`	-50.0	mV	Spike threshold
`v_reset`	-65.0	mV	Reset potential after a spike
`i_offset`	0.0	nA	Offset current

HH_cond_exp¶

Single-compartment Hodgkin-Huxley-type neuron with transient sodium and delayed-rectifier potassium currents using the ion channel models from Traub.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`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	mV
`e_rev_K`	-90.0	mV
`e_rev_leak`	-65.0	mV
`e_rev_E`	0.0	mV
`e_rev_I`	-80.0	mV
`tau_syn_E`	0.2	ms
`tau_syn_I`	2.0	ms
`i_offset`	0.0	nA

EIF_cond_alpha_isfa_ista¶

Adaptive exponential integrate and fire neuron 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

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`cm`	0.281	nF	Capacity of the membrane
`tau_refrac`	0.0	ms	Duration of refractory period
`v_spike`	0.0	mV	Spike detection threshold
`v_reset`	-70.6	mV	Reset value for membrane potential after a spike
`v_rest`	-70.6	mV	Resting membrane potential (Leak reversal potential)
`tau_m`	9.3667	ms	Membrane time constant
`i_offset`	0.0	nA	Offset current
`a`	4.0	nS	Subthreshold adaptation conductance
`b`	0.0805	nA	Spike-triggered adaptation
`delta_T`	2.0	mV	Slope factor
`tau_w`	144.0	ms	Adaptation time constant
`v_thresh`	-50.4	mV	Spike initiation threshold
`e_rev_E`	0.0	mV	Excitatory reversal potential
`tau_syn_E`	5.0	ms	Rise time of excitatory synaptic conductance (alpha function)
`e_rev_I`	-80.0	mV	Inhibitory reversal potential
`tau_syn_I`	5.0	ms	Rise time of the inhibitory synaptic conductance (alpha function)

Spike sources¶

SpikeSourcePoisson¶

Spike source, generating spikes according to a Poisson process.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`rate`	0.0	s^`-1`	Mean spike frequency
`start`	0.0	ms	Start time
`duration`	10^9	ms	Duration of spike sequence

SpikeSourceArray¶

Spike source generating spikes at the times given in the spike_times array.

Availability: NEST, NEURON, Brian

Name	Default value	Units	Description
`spike_times`	`[]`	ms	list or numpy array containing spike times