Low firing rates: an effective Hamiltonian for excitatory neurons

Abstract

The authors analyse the behaviour of an attractor neural network which exhibits low mean temporal activity levels, despite the fact that the intrinsic neuronal cycle time is very short (2-3 ms). Information and computation are represented on the excitatory neurons only. The influence of inhibitory neurons, which are assumed to react on a shorter timescale than the excitatory ones, is expressed as an effective interaction of the excitatory neurons. This leads to an effective model, which describes the interplay of excitation and inhibition acting on excitatory neurons in terms of the excitatory neural variables alone. The network operates in the presence of fast noise, which is large relative to the frozen randomness induced by the stored patterns. The overall fraction of active neurons is controlled by a single free parameter, which expresses the relative strength of the effective inhibition. Associative retrieval is identified, as usual, with the breakdown of ergodicity in the dynamics of the network, in particular with the presence of dynamical attractors corresponding to the retrieval of a given pattern. In such an attractor, the activity of neurons corresponding to active sites in the stored patterns increases at the expense of other neurons. Yet only a small fraction of the neurons active in the pattern are in the active state in each elementary time cycle, and they vary from cycle to cycle in an uncorrelated fashion, due to the noise. Hence, the observed mean activity rate of any individual neuron is kept low. This scenario is demonstrated by an analytical study based on the replica method, and the results are tested by numerical simulations.