Command: ginzburg_neuron


The ginzburg_neuron is an implementation of a binary neuron that
is irregularly updated as Poisson time points. At each update
point the total synaptic input h into the neuron is summed up
passed through a gain function g whose output is interpreted as
the probability of the neuron to be in the active (1) state.

The gain function g used here is g(h) = c1*h + c2 * 0.5*(1 +
tanh(c3*(h-theta))) (output clipped to [0 1]). This allows to
obtain affin-linear (c1!=0 c2!=0 c3=0) or sigmoidal (c1=0
c2=1 c3!=0) shaped gain functions. The latter choice
corresponds to the definition in [1] giving the name to this
neuron model.
The choice c1=0 c2=1 c3=beta/2 corresponds to the Glauber
dynamics [2] g(h) = 1 / (1 + exp(-beta (h-theta))).
The time constant tau_m is defined as the mean
inter-update-interval that is drawn from an exponential
distribution with this parameter. Using this neuron to reprodce
simulations with asynchronous update [1] the time constant needs
to be chosen as tau_m = dt*N where dt is the simulation time
step and N the number of neurons in the original simulation with
asynchronous update. This ensures that a neuron is updated on
average every tau_m ms. Since in the original paper [1] neurons
are coupled with zero delay this implementation follows this
definition. It uses the update scheme described in [3] to
maintain causality: The incoming events in time step t_i are
taken into account at the beginning of the time step to calculate
the gain function and to decide upon a transition. In order to
obtain delayed coupling with delay d the user has to specify the
delay d+h upon connection where h is the simulation time step.


tau_m double - Membrane time constant (mean inter-update-interval) in ms.
theta double - threshold for sigmoidal activation function mV
c1 double - linear gain factor (probability/mV)
c2 double - prefactor of sigmoidal gain (probability)
c3 double - slope factor of sigmoidal gain (1/mV)

Moritz Helias
SpikeEvent PotentialRequest

[1] Iris Ginzburg Haim Sompolinsky. Theory of correlations in stochastic neural networks (1994).
PRE 50(4) p. 3171
[2] Hertz Krogh Palmer. Introduction to the theory of neural computation. Westview (1991).
[3] Abigail Morrison Markus Diesmann. Maintaining Causality in Discrete Time Neuronal
In: Lectures in Supercomputational Neuroscience p. 267. Peter beim Graben Changsong Zhou Marco
Thiel Juergen Kurths (Eds.) Springer 2008.


This neuron has a special use for spike events to convey the
binary state of the neuron to the target. The neuron model
only sends a spike if a transition of its state occurs. If the
state makes an up-transition it sends a spike with multiplicity 2
if a down transition occurs it sends a spike with multiplicity 1.
The decoding scheme relies on the feature that spikes with multiplicity
larger 1 are delivered consecutively also in a parallel setting.
The creation of double connections between binary neurons will
destroy the deconding scheme as this effectively duplicates
every event. Using random connection routines it is therefore
advisable to set the property 'multapses' to false.
The neuron accepts several sources of currents e.g. from a