mcculloch_pitts_neuron – Binary deterministic neuron with Heaviside activation function¶
mcculloch_pitts_neuron is an implementation of a binary
neuron that is irregularly updated as Poisson time points 1. At
each update point the total synaptic input h into the neuron is
summed up, passed through a Heaviside gain function \(g(h) = H(h-\theta)\),
whose output is either 1 (if input is above) or 0 (if input is below
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 reproduce
simulations with asynchronous update 1, the time constant needs
to be chosen as \(\tau_m = dt \times N\), 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.
Membrane time constant (mean inter-update-interval)
Threshold for sigmoidal activation function
Special requirements for binary neurons
mcculloch_pitts_neuron is a binary neuron, the user must
ensure that the following requirements are observed. NEST does not
enforce them. Breaching the requirements can lead to meaningless
Binary neurons must only be connected to other binary neurons.
No more than one connection must be created between any pair of binary neurons. When using probabilistic connection rules, specify
'allow_autapses': Falseto avoid accidental creation of multiple connections between a pair of neurons.
Binary neurons can be driven by current-injecting devices, but not by spike generators.
Activity of binary neurons can only be recored using a
McCulloch W, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133. DOI: https://doi.org/10.1007/BF02478259
Hertz J, Krogh A, Palmer R (1991). Introduction to the theory of neural computation. Addison-Wesley Publishing Conmpany.
Morrison A, Diesmann M (2007). Maintaining causality in discrete time neuronal simulations. In: Lectures in Supercomputational Neuroscience, p. 267. Peter beim Graben, Changsong Zhou, Marco Thiel, Juergen Kurths (Eds.), Springer. DOI: https://doi.org/10.1007/978-3-540-73159-7_10