PyNN
Contents
PyNN#
A number of ComponentType description of PyNN standard cells. All of the cells extend basePyNNCell, and the synapses extend basePynnSynapse.
Original ComponentType definitions: PyNN.xml. Schema against which NeuroML based on these should be valid: NeuroML_v2.2.xsd. Generated on 18/08/22 from this commit. Please file any issues or questions at the issue tracker here.
basePyNNCell#
extends baseCellMembPot
Base type of any PyNN standard cell model. Note: membrane potential v has dimensions voltage, but all other parameters are dimensionless. This is to facilitate translation to and from PyNN scripts in Python, where these parameters have implicit units, see http://neuralensemble.org/trac/PyNN/wiki/StandardModels.
cm 
Dimensionless 

i_offset 
Dimensionless 

tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell 
Dimensionless 
v_init 
Dimensionless 
MSEC = 1ms 

MVOLT = 1mV 

NFARAD = 1nF 
iSyn 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
Direction: in 

spike_in_I 
Direction: in 
basePyNNIaFCell#
extends basePyNNCell
Base type of any PyNN standard integrate and fire model.
cm 
(from basePyNNCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
Dimensionless 

tau_refrac 
Dimensionless 

tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
Dimensionless 

v_rest 
Dimensionless 

v_thresh 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
basePyNNIaFCondCell#
extends basePyNNIaFCell
Base type of conductance based PyNN IaF cell models.
cm 
(from basePyNNCell) 
Dimensionless 
e_rev_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell 
Dimensionless 
e_rev_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
IF_curr_alpha#
extends basePyNNIaFCell
Leaky integrate and fire model with fixed threshold and alphafunctionshaped postsynaptic current.
cm 
(from basePyNNCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
On Conditions
IF t > lastSpikeTime + (tau_refrac*MSEC) THEN
TRANSITION to REGIME integrating
 Regime: integrating (initial)
On Conditions
IF v > v_thresh * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * ((i_offset/cm) + ((v_rest  (v/MVOLT)) / tau_m))/MSEC) + (iSyn / (cm * NFARAD))
Go to the libNeuroML documentation
from neuroml import IF_curr_alpha
variable = IF_curr_alpha(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, gds_collector_=None, **kwargs_)
<IF_curr_alpha id="IF_curr_alpha" cm="1.0" i_offset="0.9" tau_m="20.0" tau_refrac="10.0" tau_syn_E="0.5" tau_syn_I="0.5" v_init="65" v_reset="62.0" v_rest="65.0" v_thresh="52.0"/>
IF_curr_exp#
extends basePyNNIaFCell
Leaky integrate and fire model with fixed threshold and decayingexponential postsynaptic current.
cm 
(from basePyNNCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
On Conditions
IF t > lastSpikeTime + (tau_refrac*MSEC) THEN
TRANSITION to REGIME integrating
 Regime: integrating (initial)
On Conditions
IF v > v_thresh * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * (((i_offset)/cm) + ((v_rest  (v/MVOLT)) / tau_m))/MSEC) + (iSyn / (cm * NFARAD))
Go to the libNeuroML documentation
from neuroml import IF_curr_exp
variable = IF_curr_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, gds_collector_=None, **kwargs_)
<IF_curr_exp id="IF_curr_exp" cm="1.0" i_offset="1.0" tau_m="20.0" tau_refrac="8.0" tau_syn_E="5.0" tau_syn_I="5.0" v_init="65" v_reset="70.0" v_rest="65.0" v_thresh="50.0"/>
IF_cond_alpha#
extends basePyNNIaFCondCell
Leaky integrate and fire model with fixed threshold and alphafunctionshaped postsynaptic conductance.
cm 
(from basePyNNCell) 
Dimensionless 
e_rev_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
e_rev_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
On Conditions
IF t > lastSpikeTime + (tau_refrac*MSEC) THEN
TRANSITION to REGIME integrating
 Regime: integrating (initial)
On Conditions
IF v > v_thresh * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * (((i_offset) / cm) + ((v_rest  (v / MVOLT)) / tau_m)) / MSEC) + (iSyn / (cm * NFARAD))
Go to the libNeuroML documentation
from neuroml import IF_cond_alpha
variable = IF_cond_alpha(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, gds_collector_=None, **kwargs_)
<IF_cond_alpha id="IF_cond_alpha" cm="1.0" e_rev_E="0.0" e_rev_I="70.0" i_offset="0.9" tau_m="20.0" tau_refrac="5.0" tau_syn_E="0.3" tau_syn_I="0.5" v_init="65" v_reset="65.0" v_rest="65.0" v_thresh="50.0"/>
<IF_cond_alpha id="silent_cell" cm="1.0" e_rev_E="0.0" e_rev_I="70.0" i_offset="0" tau_m="20.0" tau_refrac="5.0" tau_syn_E="5" tau_syn_I="10" v_init="65" v_reset="65.0" v_rest="65.0" v_thresh="50.0"/>
IF_cond_exp#
extends basePyNNIaFCondCell
Leaky integrate and fire model with fixed threshold and exponentiallydecaying postsynaptic conductance.
cm 
(from basePyNNCell) 
Dimensionless 
e_rev_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
e_rev_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
On Conditions
IF t > lastSpikeTime + (tau_refrac*MSEC) THEN
TRANSITION to REGIME integrating
 Regime: integrating (initial)
On Conditions
IF v > v_thresh * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * (((i_offset)/cm) + ((v_rest  (v / MVOLT)) / tau_m)) / MSEC) + (iSyn / (cm * NFARAD))
Go to the libNeuroML documentation
from neuroml import IF_cond_exp
variable = IF_cond_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, gds_collector_=None, **kwargs_)
<IF_cond_exp id="IF_cond_exp" cm="1.0" e_rev_E="0.0" e_rev_I="70.0" i_offset="1.0" tau_m="20.0" tau_refrac="5.0" tau_syn_E="5.0" tau_syn_I="5.0" v_init="65" v_reset="68.0" v_rest="65.0" v_thresh="52.0"/>
EIF_cond_exp_isfa_ista#
extends basePyNNIaFCondCell
Adaptive exponential integrate and fire neuron according to Brette R and Gerstner W ( 2005 ) with exponentiallydecaying postsynaptic conductance.
a 
Dimensionless 

b 
Dimensionless 

cm 
(from basePyNNCell) 
Dimensionless 
delta_T 
Dimensionless 

e_rev_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
e_rev_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_w 
Dimensionless 

v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_spike 
Dimensionless 

v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
eif_threshold 
Dimensionless 
eif_threshold = v_spike * H(delta_T1e12) + v_thresh * H(1*delta_T+1e9)
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 

w 
Dimensionless 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
w: Dimensionless (exposed as w)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
w = 0
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Conditional Derived Variables
IF delta_T > 0 THEN
delta_I = delta_T * exp(((v / MVOLT)  v_thresh) / delta_T)
IF delta_T = 0 THEN
delta_I = 0
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
w = w+b
On Conditions
IF t > lastSpikeTime + (tau_refrac*MSEC) THEN
TRANSITION to REGIME integrating
Time Derivatives
d w /dt = (1 / tau_w) * (a * ((v / MVOLT)  v_rest)  w) / MSEC
 Regime: integrating (initial)
On Conditions
IF v > eif_threshold * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * ((1 * ((v / MVOLT)  v_rest) + delta_I) / tau_m + (i_offset  w) / cm) / MSEC) + (iSyn / (cm * NFARAD))
d w /dt = (1 / tau_w) * (a * ((v / MVOLT)  v_rest)  w) / MSEC
Go to the libNeuroML documentation
from neuroml import EIF_cond_exp_isfa_ista
variable = EIF_cond_exp_isfa_ista(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, a=None, b=None, delta_T=None, tau_w=None, v_spike=None, extensiontype_=None, gds_collector_=None, **kwargs_)
<EIF_cond_exp_isfa_ista id="EIF_cond_exp_isfa_ista" a="0.0" b="0.0805" cm="0.281" delta_T="2.0" e_rev_E="0.0" e_rev_I="80.0" i_offset="0.6" tau_m="9.3667" tau_refrac="5" tau_syn_E="5.0" tau_syn_I="5.0" tau_w="144.0" v_init="65" v_reset="68.0" v_rest="70.6" v_spike="40.0" v_thresh="52.0"/>
EIF_cond_alpha_isfa_ista#
extends basePyNNIaFCondCell
Adaptive exponential integrate and fire neuron according to Brette R and Gerstner W ( 2005 ) with alphafunctionshaped postsynaptic conductance.
a 
Dimensionless 

b 
Dimensionless 

cm 
(from basePyNNCell) 
Dimensionless 
delta_T 
Dimensionless 

e_rev_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
e_rev_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNIaFCondCell) 
Dimensionless 
i_offset 
(from basePyNNCell) 
Dimensionless 
tau_m 
(from basePyNNIaFCell) 
Dimensionless 
tau_refrac 
(from basePyNNIaFCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_w 
Dimensionless 

v_init 
(from basePyNNCell) 
Dimensionless 
v_reset 
(from basePyNNIaFCell) 
Dimensionless 
v_rest 
(from basePyNNIaFCell) 
Dimensionless 
v_spike 
Dimensionless 

v_thresh 
(from basePyNNIaFCell) 
Dimensionless 
eif_threshold 
Dimensionless 
eif_threshold = v_spike * H(delta_T1e12) + v_thresh * H(1*delta_T+1e9)
iSyn 
(from basePyNNCell) 

v 
Membrane potential (from baseCellMembPot) 

w 
Dimensionless 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
w: Dimensionless (exposed as w)
lastSpikeTime: time
 On Start
v = v_init * MVOLT
w = 0
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
 Conditional Derived Variables
IF delta_T > 0 THEN
delta_I = delta_T * exp(((v / MVOLT)  v_thresh) / delta_T)
IF delta_T = 0 THEN
delta_I = 0
 Regime: refractory (initial)
On Entry
lastSpikeTime = t
v = v_reset * MVOLT
w = w + b
On Conditions
IF t > lastSpikeTime + (tau_refrac * MSEC) THEN
TRANSITION to REGIME integrating
Time Derivatives
d w /dt = (1 / tau_w) * (a * ((v / MVOLT)  v_rest)  w) / MSEC
 Regime: integrating (initial)
On Conditions
IF v > eif_threshold * MVOLT THEN
EVENT OUT on port: spike
TRANSITION to REGIME refractory
Time Derivatives
d v /dt = (MVOLT * ((1 * ( (v / MVOLT)  v_rest) + delta_I) / tau_m + (i_offset  w) / cm) / MSEC) + (iSyn / (cm * NFARAD))
d w /dt = (1/ tau_w) * (a*((v/MVOLT)v_rest)  w) /MSEC
Go to the libNeuroML documentation
from neuroml import EIF_cond_alpha_isfa_ista
variable = EIF_cond_alpha_isfa_ista(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, tau_m=None, tau_refrac=None, v_reset=None, v_rest=None, v_thresh=None, e_rev_E=None, e_rev_I=None, a=None, b=None, delta_T=None, tau_w=None, v_spike=None, gds_collector_=None, **kwargs_)
<EIF_cond_alpha_isfa_ista id="EIF_cond_alpha_isfa_ista" a="0.0" b="0.0805" cm="0.281" delta_T="0" e_rev_E="0.0" e_rev_I="80.0" i_offset="0.6" tau_m="9.3667" tau_refrac="5" tau_syn_E="5.0" tau_syn_I="5.0" tau_w="144.0" v_init="65" v_reset="68.0" v_rest="70.6" v_spike="40.0" v_thresh="52.0"/>
HH_cond_exp#
extends basePyNNCell
Singlecompartment HodgkinHuxleytype neuron with transient sodium and delayedrectifier potassium currents using the ion channel models from Traub.
cm 
(from basePyNNCell) 
Dimensionless 
e_rev_E 
Dimensionless 

e_rev_I 
Dimensionless 

e_rev_K 
Dimensionless 

e_rev_Na 
Dimensionless 

e_rev_leak 
Dimensionless 

g_leak 
Dimensionless 

gbar_K 
Dimensionless 

gbar_Na 
Dimensionless 

i_offset 
(from basePyNNCell) 
Dimensionless 
tau_syn_E 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
tau_syn_I 
This parameter is never used in the NeuroML2 description of this cell! Any synapse producing a current can be placed on this cell (from basePyNNCell) 
Dimensionless 
v_init 
(from basePyNNCell) 
Dimensionless 
v_offset 
Dimensionless 
h 
Dimensionless 

iSyn 
(from basePyNNCell) 

m 
Dimensionless 

n 
Dimensionless 

v 
Membrane potential (from baseCellMembPot) 
spike 
Spike event (from baseSpikingCell) 
Direction: out 
spike_in_E 
(from basePyNNCell) 
Direction: in 
spike_in_I 
(from basePyNNCell) 
Direction: in 
synapses 
 State Variables
v: voltage (exposed as v)
m: Dimensionless (exposed as m)
h: Dimensionless (exposed as h)
n: Dimensionless (exposed as n)
 On Start
v = v_init * MVOLT
 Derived Variables
iSyn = synapses[*]>i(reduce method: add) (exposed as iSyn)
iLeak = g_leak * (e_rev_leak  (v / MVOLT))
iNa = gbar_Na * (m * m * m) * h * (e_rev_Na  (v / MVOLT))
iK = gbar_K * (n * n * n * n) * (e_rev_K  (v / MVOLT))
iMemb = iLeak + iNa + iK + i_offset
alpham = 0.32 * (13  (v / MVOLT) + v_offset) / (exp((13  (v / MVOLT) + v_offset) / 4.0)  1)
betam = 0.28 * ((v / MVOLT)  v_offset  40) / (exp(((v / MVOLT)  v_offset  40) / 5.0)  1)
alphah = 0.128 * exp((17  (v / MVOLT) + v_offset) / 18.0)
betah = 4.0 / (1 + exp((40  (v / MVOLT) + v_offset) / 5))
alphan = 0.032 * (15  (v / MVOLT) + v_offset) / (exp((15  (v / MVOLT) + v_offset) / 5)  1)
betan = 0.5 * exp((10  (v / MVOLT) + v_offset) / 40)
 Time Derivatives
d v /dt = (MVOLT * (iMemb / cm) / MSEC) + (iSyn / (cm * NFARAD))
d m /dt = (alpham * (1  m)  betam * m) / MSEC
d h /dt = (alphah * (1  h)  betah * h) / MSEC
d n /dt = (alphan * (1  n)  betan * n) / MSEC
Go to the libNeuroML documentation
from neuroml import HH_cond_exp
variable = HH_cond_exp(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, cm=None, i_offset=None, tau_syn_E=None, tau_syn_I=None, v_init=None, v_offset=None, e_rev_E=None, e_rev_I=None, e_rev_K=None, e_rev_Na=None, e_rev_leak=None, g_leak=None, gbar_K=None, gbar_Na=None, gds_collector_=None, **kwargs_)
<HH_cond_exp id="HH_cond_exp" cm="0.2" e_rev_E="0.0" e_rev_I="80.0" e_rev_K="90.0" e_rev_Na="50.0" e_rev_leak="65.0" g_leak="0.01" gbar_K="6.0" gbar_Na="20.0" i_offset="0.2" tau_syn_E="0.2" tau_syn_I="2.0" v_init="65" v_offset="63.0"/>
basePynnSynapse#
extends baseVoltageDepSynapse
Base type for all PyNN synapses. Note, the current I produced is dimensionless, but it requires a membrane potential v with dimension voltage.
tau_syn 
Dimensionless 
i 
The total (usually time varying) current produced by this ComponentType (from basePointCurrent) 
v 
The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse) 
in 
(from baseSynapse) 
Direction: in 
Go to the libNeuroML documentation
from neuroml import BasePynnSynapse
variable = BasePynnSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, extensiontype_=None, gds_collector_=None, **kwargs_)
expCondSynapse#
extends basePynnSynapse
Conductance based synapse with instantaneous rise and single exponential decay ( with time constant tau_syn ).
e_rev 
Dimensionless 

tau_syn 
(from basePynnSynapse) 
Dimensionless 
weight (default: 1) 
Dimensionless 
g 
Dimensionless 

i 
The total (usually time varying) current produced by this ComponentType (from basePointCurrent) 
v 
The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse) 
in 
(from baseSynapse) 
Direction: in 
 State Variables
g: Dimensionless (exposed as g)
 On Events
EVENT IN on port: in
g = g+weight
 Derived Variables
i = g * (e_rev  (v/MVOLT)) * NAMP (exposed as i)
 Time Derivatives
d g /dt = g / (tau_syn*MSEC)
Go to the libNeuroML documentation
from neuroml import ExpCondSynapse
variable = ExpCondSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, e_rev=None, gds_collector_=None, **kwargs_)
<expCondSynapse id="syn1" tau_syn="5" e_rev="0"/>
expCurrSynapse#
extends basePynnSynapse
Current based synapse with instantaneous rise and single exponential decay ( with time constant tau_syn ).
tau_syn 
(from basePynnSynapse) 
Dimensionless 
weight (default: 1) 
Dimensionless 
i 
The total (usually time varying) current produced by this ComponentType (from basePointCurrent) 
v 
The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse) 
in 
(from baseSynapse) 
Direction: in 
 State Variables
I: Dimensionless
 On Events
EVENT IN on port: in
I = I + weight
 Derived Variables
i = I * NAMP (exposed as i)
 Time Derivatives
d I /dt = I / (tau_syn*MSEC)
Go to the libNeuroML documentation
from neuroml import ExpCurrSynapse
variable = ExpCurrSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, gds_collector_=None, **kwargs_)
<expCurrSynapse id="syn3" tau_syn="5"/>
alphaCondSynapse#
extends basePynnSynapse
Alpha synapse: rise time and decay time are both tau_syn. Conductance based synapse.
e_rev 
Dimensionless 

tau_syn 
(from basePynnSynapse) 
Dimensionless 
weight (default: 1) 
Dimensionless 
A 
Dimensionless 

g 
Dimensionless 

i 
The total (usually time varying) current produced by this ComponentType (from basePointCurrent) 
v 
The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse) 
in 
(from baseSynapse) 
Direction: in 
 State Variables
g: Dimensionless (exposed as g)
A: Dimensionless (exposed as A)
 On Events
EVENT IN on port: in
A = A + weight
 Derived Variables
i = g * (e_rev  (v/MVOLT)) * NAMP (exposed as i)
 Time Derivatives
d g /dt = (2.7182818A  g)/(tau_synMSEC)
d A /dt = A /(tau_syn*MSEC)
Go to the libNeuroML documentation
from neuroml import AlphaCondSynapse
variable = AlphaCondSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, e_rev=None, gds_collector_=None, **kwargs_)
<alphaCondSynapse id="syn2" tau_syn="5" e_rev="0"/>
alphaCurrSynapse#
extends basePynnSynapse
Alpha synapse: rise time and decay time are both tau_syn. Current based synapse.
tau_syn 
(from basePynnSynapse) 
Dimensionless 
weight (default: 1) 
Dimensionless 
A 

i 
The total (usually time varying) current produced by this ComponentType (from basePointCurrent) 
v 
The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse) 
in 
(from baseSynapse) 
Direction: in 
 State Variables
I: Dimensionless
A: Dimensionless (exposed as A)
 On Events
EVENT IN on port: in
A = A + weight
 Derived Variables
i = I * NAMP (exposed as i)
 Time Derivatives
d I /dt = (2.7182818A  I)/(tau_synMSEC)
d A /dt = A /(tau_syn*MSEC)
Go to the libNeuroML documentation
from neuroml import AlphaCurrSynapse
variable = AlphaCurrSynapse(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, tau_syn=None, gds_collector_=None, **kwargs_)
<alphaCurrSynapse id="syn4" tau_syn="5"/>
SpikeSourcePoisson#
extends baseSpikeSource
Spike source, generating spikes according to a Poisson process.
end 
end = start + duration
isi 

tnextIdeal 

tnextUsed 

tsince 
Time since the last spike was emitted (from baseSpikeSource) 
in 
Direction: in 

spike 
Port on which spikes are emitted (from baseSpikeSource) 
Direction: out 
 State Variables
tsince: time (exposed as tsince)
tnextIdeal: time (exposed as tnextIdeal)
tnextUsed: time (exposed as tnextUsed)
isi: time (exposed as isi)
 On Start
isi = start  log(random(1))/rate
tsince = 0
tnextIdeal = isi + H(((isi)  (start+duration))/duration)*LONG_TIME
tnextUsed = tnextIdeal
 On Conditions
IF t > tnextUsed THEN
isi = 1 * log(random(1))/rate
tnextIdeal = (tnextIdeal+isi) + H(((tnextIdeal+isi)  (start+duration))/duration)*LONG_TIME
tnextUsed = tnextIdeal*H( (tnextIdealt)/t ) + (t+SMALL_TIME)*H( (ttnextIdeal)/t )
tsince = 0
EVENT OUT on port: spike
 Time Derivatives
d tsince /dt = 1
d tnextUsed /dt = 0
d tnextIdeal /dt = 0
Go to the libNeuroML documentation
from neuroml import SpikeSourcePoisson
variable = SpikeSourcePoisson(neuro_lex_id=None, id=None, metaid=None, notes=None, properties=None, annotation=None, start=None, duration=None, rate=None, gds_collector_=None, **kwargs_)
<SpikeSourcePoisson id="spikes1" start="50ms" duration="400ms" rate="50Hz"/>
<SpikeSourcePoisson id="spikes2" start="50ms" duration="300ms" rate="80Hz"/>