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