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Merge pull request #1 from jessica-mitchell/doxygen_refactor
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refactoring of comment blocks in models/o-z and precise
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@clinssen
clinssen authored Oct 25, 2018
2 parents 2eaf9e8 + f990f14 commit c78f0ee
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45 changes: 23 additions & 22 deletions models/ac_generator.h
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Expand Up @@ -38,40 +38,41 @@ namespace nest
{

/** @BeginDocumentation
Name: ac_generator - provides AC input current
Name: ac_generator - provides AC input current
Description:
Description:
This device produce an ac-current which are sent by a CurrentEvent. The
current is given by
This device produce an ac-current which are sent by a CurrentEvent. The
current is given by
I(t) = offset + amplitude * sin ( om * t + phi )
I(t) = offset + amplitude * sin ( om * t + phi )
where
where
om = 2 * pi * frequency
phi = phase / 180 * pi
om = 2 * pi * frequency
phi = phase / 180 * pi
The parameters are
Parameters:
amplitude double - Amplitude of sine current in pA
offset double - Constant amplitude offset in pA
frequency double - Frequency in Hz
phase double - Phase of sine current (0-360 deg)
amplitude double - Amplitude of sine current in pA
offset double - Constant amplitude offset in pA
frequency double - Frequency in Hz
phase double - Phase of sine current (0-360 deg)
Setting start and stop (see StimulatingDevice) only windows the current
as defined above. It does not shift the time axis.
Setting start and stop (see StimulatingDevice) only windows the current
as defined above. It does not shift the time axis.
References:
[1] S. Rotter and M. Diesmann, Exact digital simulation of time-
invariant linear systems with applications to neuronal modeling,
Biol. Cybern. 81, 381-402 (1999)
References:
Sends: CurrentEvent
[1] S. Rotter and M. Diesmann (1999). Exact digital simulation of time-
invariant linear systems with applications to neuronal modeling,
Biol. Cybern. 81, 381-402.
Author: Johan Hake, Spring 2003
Sends: CurrentEvent
SeeAlso: Device, StimulatingDevice, dc_generator, step_current_generator
Author: Johan Hake, Spring 2003
SeeAlso: Device, StimulatingDevice, dc_generator, step_current_generator
*/
class ac_generator : public DeviceNode
{
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2 changes: 2 additions & 0 deletions models/aeif_cond_alpha.h
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Expand Up @@ -75,6 +75,7 @@ Name: aeif_cond_alpha - Conductance based exponential integrate-and-fire neuron
model according to Brette and Gerstner (2005).
Description:
aeif_cond_alpha is the adaptive exponential integrate and fire neuron according
to Brette and Gerstner (2005).
Synaptic conductances are modelled as alpha-functions.
Expand All @@ -91,6 +92,7 @@ and
tau_w * dw/dt= a(V-E_L) -W
Parameters:
The following parameters can be set in the status dictionary.
Dynamic state variables:
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2 changes: 2 additions & 0 deletions models/aeif_cond_alpha_RK5.h
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Expand Up @@ -39,6 +39,7 @@ Name: aeif_cond_alpha_RK5 - Conductance based exponential integrate-and-fire
neuron model according to Brette and Gerstner (2005)
Description:
aeif_cond_alpha_RK5 is the adaptive exponential integrate and fire neuron
according to Brette and Gerstner (2005).
Synaptic conductances are modelled as alpha-functions.
Expand All @@ -56,6 +57,7 @@ and
tau_w * dw/dt= a(V-E_L) -w
Parameters:
The following parameters can be set in the status dictionary.
Dynamic state variables:
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113 changes: 57 additions & 56 deletions models/aeif_cond_alpha_multisynapse.h
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Expand Up @@ -58,43 +58,44 @@ extern "C" int
aeif_cond_alpha_multisynapse_dynamics( double, const double*, double*, void* );

/** @BeginDocumentation
Name: aeif_cond_alpha_multisynapse - Conductance based adaptive exponential
integrate-and-fire neuron model according
to Brette and Gerstner (2005) with
multiple synaptic rise time and decay
time constants, and synaptic conductance
modeled by an alpha function.
Name: aeif_cond_alpha_multisynapse - Conductance based adaptive exponential
integrate-and-fire neuron model according
to Brette and Gerstner (2005) with
multiple synaptic rise time and decay
time constants, and synaptic conductance
modeled by an alpha function.
Description:
Description:
aeif_cond_alpha_multisynapse is a conductance-based adaptive exponential
integrate-and-fire neuron model. It allows an arbitrary number of synaptic
time constants. Synaptic conductance is modeled by an alpha function, as
described by A. Roth and M.C.W. van Rossum in Computational Modeling Methods
for Neuroscientists, MIT Press 2013, Chapter 6.
aeif_cond_alpha_multisynapse is a conductance-based adaptive exponential
integrate-and-fire neuron model. It allows an arbitrary number of synaptic
time constants. Synaptic conductance is modeled by an alpha function, as
described by A. Roth and M.C.W. van Rossum in Computational Modeling Methods
for Neuroscientists, MIT Press 2013, Chapter 6.
The time constants are supplied by an array, "tau_syn", and the pertaining
synaptic reversal potentials are supplied by the array "E_rev". Port numbers
are automatically assigned in the range from 1 to n_receptors.
During connection, the ports are selected with the property "receptor_type".
The time constants are supplied by an array, "tau_syn", and the pertaining
synaptic reversal potentials are supplied by the array "E_rev". Port numbers
are automatically assigned in the range from 1 to n_receptors.
During connection, the ports are selected with the property "receptor_type".
The membrane potential is given by the following differential equation:
The membrane potential is given by the following differential equation:
C dV/dt = -g_L(V-E_L) + g_L*Delta_T*exp((V-V_T)/Delta_T) + I_syn_tot(V, t)
- w + I_e
C dV/dt = -g_L(V-E_L) + g_L*Delta_T*exp((V-V_T)/Delta_T) + I_syn_tot(V, t)
- w + I_e
where
where
I_syn_tot(V,t) = \sum_i g_i(t) (V - E_{rev,i}) ,
I_syn_tot(V,t) = \sum_i g_i(t) (V - E_{rev,i}) ,
the synapse i is excitatory or inhibitory depending on the value of E_{rev,i}
and the differential equation for the spike-adaptation current w is:
the synapse i is excitatory or inhibitory depending on the value of E_{rev,i}
and the differential equation for the spike-adaptation current w is:
tau_w * dw/dt = a(V - E_L) - w
tau_w * dw/dt = a(V - E_L) - w
When the neuron fires a spike, the adaptation current w <- w + b.
When the neuron fires a spike, the adaptation current w <- w + b.
Parameters:
The following parameters can be set in the status dictionary.
Dynamic state variables:
Expand Down Expand Up @@ -126,48 +127,48 @@ Integration parameters
GSL integrator. Reduce it if NEST complains about
numerical instabilities.
Examples:
Examples:
import nest
import numpy as np
import nest
import numpy as np
neuron = nest.Create('aeif_cond_alpha_multisynapse')
nest.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5})
nest.SetStatus(neuron, {'E_rev':[0.0, 0.0, 0.0, -85.0],
'tau_syn':[1.0, 5.0, 10.0, 8.0]})
neuron = nest.Create('aeif_cond_alpha_multisynapse')
nest.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5})
nest.SetStatus(neuron, {'E_rev':[0.0, 0.0, 0.0, -85.0],
'tau_syn':[1.0, 5.0, 10.0, 8.0]})
spike = nest.Create('spike_generator', params = {'spike_times':
np.array([10.0])})
spike = nest.Create('spike_generator', params = {'spike_times':
np.array([10.0])})
voltmeter = nest.Create('voltmeter', 1, {'withgid': True})
voltmeter = nest.Create('voltmeter', 1, {'withgid': True})
delays=[1.0, 300.0, 500.0, 700.0]
w=[1.0, 1.0, 1.0, 1.0]
for syn in range(4):
nest.Connect(spike, neuron, syn_spec={'model': 'static_synapse',
'receptor_type': 1 + syn,
'weight': w[syn],
'delay': delays[syn]})
delays=[1.0, 300.0, 500.0, 700.0]
w=[1.0, 1.0, 1.0, 1.0]
for syn in range(4):
nest.Connect(spike, neuron, syn_spec={'model': 'static_synapse',
'receptor_type': 1 + syn,
'weight': w[syn],
'delay': delays[syn]})
nest.Connect(voltmeter, neuron)
nest.Connect(voltmeter, neuron)
nest.Simulate(1000.0)
dmm = nest.GetStatus(voltmeter)[0]
Vms = dmm["events"]["V_m"]
ts = dmm["events"]["times"]
import pylab
pylab.figure(2)
pylab.plot(ts, Vms)
pylab.show()
nest.Simulate(1000.0)
dmm = nest.GetStatus(voltmeter)[0]
Vms = dmm["events"]["V_m"]
ts = dmm["events"]["times"]
import pylab
pylab.figure(2)
pylab.plot(ts, Vms)
pylab.show()
Sends: SpikeEvent
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
Author: Hans Ekkehard Plesser, based on aeif_cond_beta_multisynapse
Author: Hans Ekkehard Plesser, based on aeif_cond_beta_multisynapse
SeeAlso: aeif_cond_alpha_multisynapse
*/
SeeAlso: aeif_cond_alpha_multisynapse
*/
class aeif_cond_alpha_multisynapse : public Archiving_Node
{

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118 changes: 59 additions & 59 deletions models/aeif_cond_beta_multisynapse.h
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Original file line number Diff line number Diff line change
Expand Up @@ -58,41 +58,41 @@ extern "C" int
aeif_cond_beta_multisynapse_dynamics( double, const double*, double*, void* );

/** @BeginDocumentation
Name: aeif_cond_beta_multisynapse - Conductance based adaptive exponential
integrate-and-fire neuron model according
to Brette and Gerstner (2005) with
multiple synaptic rise time and decay
time constants, and synaptic conductance
modeled by a beta function.
Name: aeif_cond_beta_multisynapse - Conductance based adaptive exponential
integrate-and-fire neuron model according
to Brette and Gerstner (2005) with
multiple synaptic rise time and decay
time constants, and synaptic conductance
modeled by a beta function.
Description:
Description:
aeif_cond_beta_multisynapse is a conductance-based adaptive exponential
integrate-and-fire neuron model. It allows an arbitrary number of synaptic
rise time and decay time constants. Synaptic conductance is modeled by a
beta function, as described by A. Roth and M.C.W. van Rossum
in Computational Modeling Methods for Neuroscientists, MIT Press 2013,
Chapter 6.
aeif_cond_beta_multisynapse is a conductance-based adaptive exponential
integrate-and-fire neuron model. It allows an arbitrary number of synaptic
rise time and decay time constants. Synaptic conductance is modeled by a
beta function, as described by A. Roth and M.C.W. van Rossum
in Computational Modeling Methods for Neuroscientists, MIT Press 2013,
Chapter 6.
The time constants are supplied by two arrays, "tau_rise" and "tau_decay" for
the synaptic rise time and decay time, respectively. The synaptic
reversal potentials are supplied by the array "E_rev". The port numbers
are automatically assigned in the range from 1 to n_receptors.
During connection, the ports are selected with the property "receptor_type".
The time constants are supplied by two arrays, "tau_rise" and "tau_decay" for
the synaptic rise time and decay time, respectively. The synaptic
reversal potentials are supplied by the array "E_rev". The port numbers
are automatically assigned in the range from 1 to n_receptors.
During connection, the ports are selected with the property "receptor_type".
The membrane potential is given by the following differential equation:
C dV/dt = -g_L(V-E_L) + g_L*Delta_T*exp((V-V_T)/Delta_T) + I_syn_tot(V, t)
- w + I_e
The membrane potential is given by the following differential equation:
C dV/dt = -g_L(V-E_L) + g_L*Delta_T*exp((V-V_T)/Delta_T) + I_syn_tot(V, t)
- w + I_e
where:
I_syn_tot(V,t) = \sum_i g_i(t) (V - E_{rev,i}) ,
where:
I_syn_tot(V,t) = \sum_i g_i(t) (V - E_{rev,i}) ,
the synapse i is excitatory or inhibitory depending on the value of E_{rev,i}
and the differential equation for the spike-adaptation current w is:
the synapse i is excitatory or inhibitory depending on the value of E_{rev,i}
and the differential equation for the spike-adaptation current w is:
tau_w * dw/dt = a(V - E_L) - w
tau_w * dw/dt = a(V - E_L) - w
When the neuron fires a spike, the adaptation current w <- w + b.
When the neuron fires a spike, the adaptation current w <- w + b.
Parameters:
The following parameters can be set in the status dictionary.
Expand Down Expand Up @@ -129,49 +129,49 @@ Integration parameters
GSL integrator. Reduce it if NEST complains about
numerical instabilities.
Examples:
Examples:
import nest
import numpy as np
import nest
import numpy as np
neuron = nest.Create('aeif_cond_beta_multisynapse')
nest.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5})
nest.SetStatus(neuron, {'E_rev':[0.0,0.0,0.0,-85.0],
'tau_decay':[50.0,20.0,20.0,20.0],
'tau_rise':[10.0,10.0,1.0,1.0]})
neuron = nest.Create('aeif_cond_beta_multisynapse')
nest.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5})
nest.SetStatus(neuron, {'E_rev':[0.0,0.0,0.0,-85.0],
'tau_decay':[50.0,20.0,20.0,20.0],
'tau_rise':[10.0,10.0,1.0,1.0]})
spike = nest.Create('spike_generator', params = {'spike_times':
np.array([10.0])})
spike = nest.Create('spike_generator', params = {'spike_times':
np.array([10.0])})
voltmeter = nest.Create('voltmeter', 1, {'withgid': True})
voltmeter = nest.Create('voltmeter', 1, {'withgid': True})
delays=[1.0, 300.0, 500.0, 700.0]
w=[1.0, 1.0, 1.0, 1.0]
for syn in range(4):
nest.Connect(spike, neuron, syn_spec={'model': 'static_synapse',
'receptor_type': 1 + syn,
'weight': w[syn],
'delay': delays[syn]})
delays=[1.0, 300.0, 500.0, 700.0]
w=[1.0, 1.0, 1.0, 1.0]
for syn in range(4):
nest.Connect(spike, neuron, syn_spec={'model': 'static_synapse',
'receptor_type': 1 + syn,
'weight': w[syn],
'delay': delays[syn]})
nest.Connect(voltmeter, neuron)
nest.Connect(voltmeter, neuron)
nest.Simulate(1000.0)
dmm = nest.GetStatus(voltmeter)[0]
Vms = dmm["events"]["V_m"]
ts = dmm["events"]["times"]
import pylab
pylab.figure(2)
pylab.plot(ts, Vms)
pylab.show()
nest.Simulate(1000.0)
dmm = nest.GetStatus(voltmeter)[0]
Vms = dmm["events"]["V_m"]
ts = dmm["events"]["times"]
import pylab
pylab.figure(2)
pylab.plot(ts, Vms)
pylab.show()
Sends: SpikeEvent
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
Author: Bruno Golosio 07/10/2016
Author: Bruno Golosio 07/10/2016
SeeAlso: aeif_cond_alpha_multisynapse
*/
SeeAlso: aeif_cond_alpha_multisynapse
*/
class aeif_cond_beta_multisynapse : public Archiving_Node
{

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