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	0.1	ms	Duration of refractory period
tau_syn_E	0.5	ms	Rise time of the excitatory synaptic alpha function
tau_syn_I	0.5	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	0.1	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	0.1	ms	Duration of refractory period
tau_syn_E	0.3	ms	Rise time of the excitatory synaptic alpha function
tau_syn_I	0.5	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	0.1	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	0.1	ms	Duration of refractory period
v_spike	-40.0	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)

Composed models

The models listed above all have two fixed post-synaptic mechanism types, “excitatory” and “inhibitory”. If you need more than two mechanisms, e.g. AMPA, NMDA and GABA_A, you can build such models by combining a “neuron-only” component with one or more “post-synaptic mechanism” components, for example:

celltype = sim.PointNeuron(
             sim.AdExp(tau_m=10.0, v_rest=-60.0),
             AMPA=sim.ExpPSR(tau_syn=1.0, e_syn=0.0),
             NMDA=sim.AlphaPSR(tau_syn=20.0, e_syn=0.0),
             GABAA=sim.ExpPSR(tau_syn=1.5, e_syn=-70.0),
             GABAB=sim.AlphaPSR(tau_syn=15.0, e_syn=-90.0))

Not all simulators can handle all combinations of components, and in general it is not possible to mix conductance-based and current-based synapses within a single cell type. PyNN will emit an error message if this is the case.

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