PyNN

Dynamics

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))

Schema

<xs:complexType name="IF_curr_alpha">
  <xs:complexContent>
    <xs:extension base="basePyNNIaFCell">
            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import IF_curr_alpha
from neuroml.utils import component_factory

variable = component_factory(
    IF_curr_alpha,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
)

Usage: XML

<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 decaying-exponential post-synaptic current.

Parameters

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

Exposures

iSyn	(from basePyNNCell)	current
v	Membrane potential (from baseCellMembPot)	voltage

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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))

Schema

<xs:complexType name="IF_curr_exp">
  <xs:complexContent>
    <xs:extension base="basePyNNIaFCell">
            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import IF_curr_exp
from neuroml.utils import component_factory

variable = component_factory(
    IF_curr_exp,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
)

Usage: XML

<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 alpha-function-shaped post-synaptic conductance.

Parameters

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

Exposures

iSyn	(from basePyNNCell)	current
v	Membrane potential (from baseCellMembPot)	voltage

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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))

Schema

<xs:complexType name="IF_cond_alpha">
  <xs:complexContent>
    <xs:extension base="basePyNNIaFCondCell">
            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import IF_cond_alpha
from neuroml.utils import component_factory

variable = component_factory(
    IF_cond_alpha,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
    e_rev_E: 'a float (required)' = None,
    e_rev_I: 'a float (required)' = None,
)

Usage: XML

<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 exponentially-decaying post-synaptic conductance.

Parameters

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

Exposures

iSyn	(from basePyNNCell)	current
v	Membrane potential (from baseCellMembPot)	voltage

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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))

Schema

<xs:complexType name="IF_cond_exp">
  <xs:complexContent>
    <xs:extension base="basePyNNIaFCondCell">
            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import IF_cond_exp
from neuroml.utils import component_factory

variable = component_factory(
    IF_cond_exp,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
    e_rev_E: 'a float (required)' = None,
    e_rev_I: 'a float (required)' = None,
)

Usage: XML

<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 exponentially-decaying post-synaptic conductance.

Parameters

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

Derived parameters

eif_threshold

Dimensionless

eif_threshold = v_spike * H(delta_T-1e-12) + v_thresh * H(-1*delta_T+1e-9)

Exposures

iSyn	(from basePyNNCell)	current
v	Membrane potential (from baseCellMembPot)	voltage
w		Dimensionless

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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

Schema

<xs:complexType name="EIF_cond_exp_isfa_ista">
  <xs:complexContent>
    <xs:extension base="basePyNNIaFCondCell">
      <xs:attribute name="a" type="xs:float" use="required"/>
      <xs:attribute name="b" type="xs:float" use="required"/>
      <xs:attribute name="delta_T" type="xs:float" use="required"/>
      <xs:attribute name="tau_w" type="xs:float" use="required"/>
      <xs:attribute name="v_spike" type="xs:float" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import EIF_cond_exp_isfa_ista
from neuroml.utils import component_factory

variable = component_factory(
    EIF_cond_exp_isfa_ista,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
    e_rev_E: 'a float (required)' = None,
    e_rev_I: 'a float (required)' = None,
    a: 'a float (required)' = None,
    b: 'a float (required)' = None,
    delta_T: 'a float (required)' = None,
    tau_w: 'a float (required)' = None,
    v_spike: 'a float (required)' = None,
    extensiontype_=None,
)

Usage: XML

<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 alpha-function-shaped post-synaptic conductance.

Parameters

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

Derived parameters

eif_threshold

Dimensionless

eif_threshold = v_spike * H(delta_T-1e-12) + v_thresh * H(-1*delta_T+1e-9)

Exposures

iSyn	(from basePyNNCell)	current
v	Membrane potential (from baseCellMembPot)	voltage
w		Dimensionless

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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

Schema

<xs:complexType name="EIF_cond_alpha_isfa_ista">
  <xs:complexContent>
    <xs:extension base="EIF_cond_exp_isfa_ista">
            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import EIF_cond_alpha_isfa_ista
from neuroml.utils import component_factory

variable = component_factory(
    EIF_cond_alpha_isfa_ista,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    tau_m: 'a float (required)' = None,
    tau_refrac: 'a float (required)' = None,
    v_reset: 'a float (required)' = None,
    v_rest: 'a float (required)' = None,
    v_thresh: 'a float (required)' = None,
    e_rev_E: 'a float (required)' = None,
    e_rev_I: 'a float (required)' = None,
    a: 'a float (required)' = None,
    b: 'a float (required)' = None,
    delta_T: 'a float (required)' = None,
    tau_w: 'a float (required)' = None,
    v_spike: 'a float (required)' = None,
)

Usage: XML

<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

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

Parameters

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

Exposures

h		Dimensionless
iSyn	(from basePyNNCell)	current
m		Dimensionless
n		Dimensionless
v	Membrane potential (from baseCellMembPot)	voltage

Event Ports

spike	Spike event (from baseSpikingCell)	Direction: out
spike_in_E	(from basePyNNCell)	Direction: in
spike_in_I	(from basePyNNCell)	Direction: in

Attachments

synapses

Dynamics

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

Schema

<xs:complexType name="HH_cond_exp">
  <xs:complexContent>
    <xs:extension base="basePyNNCell">
      <xs:attribute name="v_offset" type="xs:float" use="required"/>
      <xs:attribute name="e_rev_E" type="xs:float" use="required"/>
      <xs:attribute name="e_rev_I" type="xs:float" use="required"/>
      <xs:attribute name="e_rev_K" type="xs:float" use="required"/>
      <xs:attribute name="e_rev_Na" type="xs:float" use="required"/>
      <xs:attribute name="e_rev_leak" type="xs:float" use="required"/>
      <xs:attribute name="g_leak" type="xs:float" use="required"/>
      <xs:attribute name="gbar_K" type="xs:float" use="required"/>
      <xs:attribute name="gbar_Na" type="xs:float" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import HH_cond_exp
from neuroml.utils import component_factory

variable = component_factory(
    HH_cond_exp,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    cm: 'a float (required)' = None,
    i_offset: 'a float (required)' = None,
    tau_syn_E: 'a float (required)' = None,
    tau_syn_I: 'a float (required)' = None,
    v_init: 'a float (required)' = None,
    v_offset: 'a float (required)' = None,
    e_rev_E: 'a float (required)' = None,
    e_rev_I: 'a float (required)' = None,
    e_rev_K: 'a float (required)' = None,
    e_rev_Na: 'a float (required)' = None,
    e_rev_leak: 'a float (required)' = None,
    g_leak: 'a float (required)' = None,
    gbar_K: 'a float (required)' = None,
    gbar_Na: 'a float (required)' = None,
)

Usage: XML

<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.

Parameters

tau_syn

Dimensionless

Constants

MSEC = 1ms		time
MVOLT = 1mV		voltage
NAMP = 1nA		current

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse)

Event Ports

in

(from baseSynapse)

Direction: in

Schema

<xs:complexType name="BasePynnSynapse">
  <xs:complexContent>
    <xs:extension base="BaseSynapse">
      <xs:attribute name="tau_syn" type="xs:float" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import BasePynnSynapse
from neuroml.utils import component_factory

variable = component_factory(
    BasePynnSynapse,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    tau_syn: 'a float (required)' = None,
    extensiontype_=None,
)

expCondSynapse#

extends basePynnSynapse

Conductance based synapse with instantaneous rise and single exponential decay ( with time constant tau_syn ).

Parameters

e_rev		Dimensionless
tau_syn	(from basePynnSynapse)	Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

g		Dimensionless
i	The total (usually time varying) current produced by this ComponentType (from basePointCurrent)	current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse)

Event Ports

in

(from baseSynapse)

Direction: in

Dynamics

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)

Schema

<xs:complexType name="ExpCondSynapse">
  <xs:complexContent>
    <xs:extension base="BasePynnSynapse">
      <xs:attribute name="e_rev" type="xs:float" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import ExpCondSynapse
from neuroml.utils import component_factory

variable = component_factory(
    ExpCondSynapse,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    tau_syn: 'a float (required)' = None,
    e_rev: 'a float (required)' = None,
)

Usage: XML

<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 ).

Parameters

tau_syn

(from basePynnSynapse)

Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

i

The total (usually time varying) current produced by this ComponentType (from basePointCurrent)

current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse)

Event Ports

in

(from baseSynapse)

Direction: in

Dynamics

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)

Schema

<xs:complexType name="ExpCurrSynapse">
  <xs:complexContent>
    <xs:extension base="BasePynnSynapse">

            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import ExpCurrSynapse
from neuroml.utils import component_factory

variable = component_factory(
    ExpCurrSynapse,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    tau_syn: 'a float (required)' = None,
)

Usage: XML

<expCurrSynapse id="syn3" tau_syn="5"/>

alphaCondSynapse#

extends basePynnSynapse

Alpha synapse: rise time and decay time are both tau_syn. Conductance based synapse.

Parameters

e_rev		Dimensionless
tau_syn	(from basePynnSynapse)	Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

A		Dimensionless
g		Dimensionless
i	The total (usually time varying) current produced by this ComponentType (from basePointCurrent)	current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse)

Event Ports

in

(from baseSynapse)

Direction: in

Dynamics

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)

Schema

<xs:complexType name="AlphaCondSynapse">
  <xs:complexContent>
    <xs:extension base="BasePynnSynapse">
      <xs:attribute name="e_rev" type="xs:float" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import AlphaCondSynapse
from neuroml.utils import component_factory

variable = component_factory(
    AlphaCondSynapse,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    tau_syn: 'a float (required)' = None,
    e_rev: 'a float (required)' = None,
)

Usage: XML

<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.

Parameters

tau_syn

(from basePynnSynapse)

Dimensionless

Properties

weight (default: 1)

Dimensionless

Exposures

A		current
i	The total (usually time varying) current produced by this ComponentType (from basePointCurrent)	current

Requirements

v

The current may vary with the voltage exposed by the ComponentType on which this is placed (from baseVoltageDepSynapse)

Event Ports

in

(from baseSynapse)

Direction: in

Dynamics

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)

Schema

<xs:complexType name="AlphaCurrSynapse">
  <xs:complexContent>
    <xs:extension base="BasePynnSynapse">

            </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python

from neuroml import AlphaCurrSynapse
from neuroml.utils import component_factory

variable = component_factory(
    AlphaCurrSynapse,
    id: 'a NmlId (required)' = None,
    metaid: 'a MetaId (optional)' = None,
    notes: 'a string (optional)' = None,
    properties: 'list of Property(s) (optional)' = None,
    annotation: 'a Annotation (optional)' = None,
    neuro_lex_id: 'a NeuroLexId (optional)' = None,
    tau_syn: 'a float (required)' = None,
)

Usage: XML

<alphaCurrSynapse id="syn4" tau_syn="5"/>

SpikeSourcePoisson#

extends baseSpikeSource

Spike source, generating spikes according to a Poisson process.

Parameters

duration		time
rate		per_time
start		time

Constants

LONG_TIME = 1e9hour		time
SMALL_TIME = 1e-9ms		time

Derived parameters

end

time

end = start + duration

Exposures

isi		time
tnextIdeal		time
tnextUsed		time
tsince	Time since the last spike was emitted (from baseSpikeSource)	time

Event Ports

in		Direction: in
spike	Port on which spikes are emitted (from baseSpikeSource)	Direction: out

Dynamics

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( (tnextIdeal-t)/t ) + (t+SMALL_TIME)*H( (t-tnextIdeal)/t ); tsince = 0; EVENT OUT on port: spike
Time Derivatives: d tsince /dt = 1; d tnextUsed /dt = 0; d tnextIdeal /dt = 0

Schema

<xs:complexType name="SpikeSourcePoisson">
  <xs:complexContent>
    <xs:extension base="Standalone">
      <xs:attribute name="start" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="duration" type="Nml2Quantity_time" use="required"/>
      <xs:attribute name="rate" type="Nml2Quantity_pertime" use="required"/>
    </xs:extension>
  </xs:complexContent>
</xs:complexType>

Usage: Python