Clopath neuron models¶

class
aeif_psc_delta_clopath
: public Clopath_Archiving_Node  #include <aeif_psc_delta_clopath.h>
Name: aeif_psc_delta_clopath  Exponential integrateandfire neuron model according to Clopath et al. (2010).
Description:
aeif_psc_delta_clopath is an implementation of the neuron model as it is used in [1]. It is an extension of the aeif_psc_delta model and capable of connecting to a Clopath synapse.
Note that there are two points that are not mentioned in the paper but present in a MATLAB implementation by Claudia Clopath [3]. The first one is the clamping of the membrane potential to a fixed value after a spike occured to mimik a real spike and not just the upswing. This is important since the finite duration of the spike influences the evolution of the convolved versions (u_bar_[plus/minus]) of the membrane potential and thus the change of the synaptic weight. Secondly, there is a delay with which u_bar_[plus/minus] are used to compute the change of the synaptic weight.
Parameters:
The following parameters can be set in the status dictionary.
Dynamic state variables
V_m
mV
Membrane potential
w
pA
Spikeadaptation current
z
pA
Spikeadaptation current
V_th
mV
Adaptive spike initiation threshold
u_bar_plus
mV
Lowpass filtered Membrane potential
u_bar_minus
mV
Lowpass filtered Membrane potential
u_bar_bar
mV
Lowpass filtered u_bar_minus
Membrane Parameters
C_m
pF
Capacity of the membrane
t_ref
ms
Duration of refractory period
V_reset
mV
Reset value for V_m after a spike
E_L
mV
Leak reversal potential
g_L
nS
Leak conductance
I_e
pA
Constant external input current
tau_plus
ms
Time constant of u_bar_plus
tau_minus
ms
Time constant of u_bar_minus
tau_bar_bar
ms
Time constant of u_bar_bar
Spike adaptation parameters
a
nS
Subthreshold adaptation
b
pA
Spiketriggered adaptation
Delta_T
mV
Slope factor
tau_w
ms
Adaptation time constant
V_peak
mV
Spike detection threshold
V_th_max
mV
Value of V_th afer a spike
V_th_rest
mV
Resting value of V_th
Clopath rule parameters
A_LTD
1/mV
Amplitude of depression
A_LTP
1/mV^2
Amplitude of facilitation
theta_plus
mV
Threshold for u
theta_minus
mV
Threshold for u_bar_[plus/minus]
A_LTD_const
boolean
Flag that indicates whether A_LTD_ should be constant (true, default) or multiplied by u_bar_bar^2 / u_ref_squared (false).
delay_u_bars
real
Delay with which u_bar_[plus/minus] are processed to compute the synaptic weights.
U_ref_squared
real
Reference value for u_bar_bar_^2.
Other parameters
t_clamp
ms
Duration of clamping of Membrane potential after a spike
V_clamp
mV
Value to which the Membrane potential is clamped
Integration parameters
gsl_error_tol
real
This parameter controls the admissible error of the GSL integrator. Reduce it if NEST complains about numerical instabilities.
Note:
Neither the clamping nor the delayed processing of u_bar_[plus/minus] are mentioned in [1]. However, they are part of an reference implementation by Claudia Clopath et al. that can be found on ModelDB [3]. The clamping is important to mimic a spike which is otherwise not described by the aeif neuron model.
Author: Jonas Stapmanns, David Dahmen, Jan Hahne
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
References:
 1
Clopath et al. (2010). Connectivity reflects coding: a model of voltagebased STDP with homeostasis. Nature Neuroscience 13(3):344352. DOI: https://doi.org/10.1038/nn.2479
 2
Clopath and Gerstner (2010). Voltage and spike timing interact in STDP – a unified model. Frontiers in Synaptic Neuroscience. 2:25 DOI: https://doi.org/10.3389/fnsyn.2010.00025
 3
Voltagebased STDP synapse (Clopath et al. 2010) on ModelDB https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=144566&file=%2f modeldb_package%2fVoTriCode%2faEIF.m

class
hh_psc_alpha_clopath
: public Clopath_Archiving_Node¶  #include <hh_psc_alpha_clopath.h>
Name: hh_psc_alpha_clopath  HodgkinHuxley neuron model with support for the Clopath synapse.
Description:
hh_psc_alpha_clopath is an implementation of a spiking neuron using the HodgkinHuxley formalism and that is capable of connecting to a Clopath synapse.
(1) Postsynaptic currents Incoming spike events induce a postsynaptic change of current modelled by an alpha function. The alpha function is normalised such that an event of weight 1.0 results in a peak current of 1 pA.
(2) Spike Detection Spike detection is done by a combined thresholdandlocalmaximum search: if there is a local maximum above a certain threshold of the membrane potential, it is considered a spike.
Parameters:
The following parameters can be set in the status dictionary.
Dynamic state variables
V_m
mV
Membrane potential
u_bar_plus
mV
Lowpass filtered Membrane potential
u_bar_minus
mV
Lowpass filtered Membrane potential
u_bar_bar
mV
Lowpass filtered u_bar_minus
Membrane Parameters
E_L
mV
Leak reversal potential
C_m
pF
Capacity of the membrane
g_L
nS
Leak conductance
tau_ex
ms
Rise time of the excitatory synaptic alpha function
tau_in
ms
Rise time of the inhibitory synaptic alpha function
E_Na
mV
Sodium reversal potential
g_Na
nS
Sodium peak conductance
E_K
mV
Potassium reversal potential
g_K
nS
Potassium peak conductance
Act_m
real
Activation variable m
Act_h
real
Activation variable h
Inact_n
real
Inactivation variable n
I_e
pA
External input current
Clopath rule parameters
A_LTD
1/mV
Amplitude of depression
A_LTP
1/mV^2
Amplitude of facilitation
theta_plus
mV
Threshold for u
theta_minus
mV
Threshold for u_bar_[plus/minus]
A_LTD_const
boolean
Flag that indicates whether A_LTD_ should be constant (true, default) or multiplied by u_bar_bar^2 / u_ref_squared (false).
delay_u_bars
real
Delay with which u_bar_[plus/minus] are processed to compute the synaptic weights.
U_ref_squared
real
Reference value for u_bar_bar_^2.
Problems/Todo:
better spike detection initial wavelet/spike at simulation onset
References:
 1
Gerstner W and Kistler WM (2002). Spiking neuron models: Single neurons, populations, plasticity. New York: Cambridge university press.
 2
Dayan P and Abbott L (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press. https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3006127
 3
Hodgkin AL and Huxley A F (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 117. DOI: https://doi.org/10.1113/jphysiol.1952.sp004764
 4
Clopath et al. (2010). Connectivity reflects coding: a model of voltagebased STDP with homeostasis. Nature Neuroscience 13(3):344352. DOI: https://doi.org/10.1038/nn.2479
 5
Clopath and Gerstner (2010). Voltage and spike timing interact in STDP – a unified model. Frontiers in Synaptic Neuroscience. 2:25 DOI: https://doi.org/10.3389/fnsyn.2010.00025
 6
Voltagebased STDP synapse (Clopath et al. 2010) connected to a HodgkinHuxley neuron on ModelDB: https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=144566&file =%2fmodeldb_package%2fstdp_cc.mod
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
Author: Jonas Stapmanns, David Dahmen, Jan Hahne (adapted from hh_psc_alpha by Schrader)
SeeAlso: hh_psc_alpha, clopath_synapse, aeif_psc_delta_clopath