Rate chaos and memory lifetime in spiking neural networks
Rate chaos is a collective state of a neural network characterized by slow irregular fluctuations of firing rates of
individual neurons.We study a sparsely connected network of spiking neuronswhich demonstrates three different
scenarios for the emergence of rate chaos, based either on increasing the synaptic strength, increasing the
synaptic integration time, or clustering of the excitatory synaptic connections. Although all the scenarios lead
to collective dynamicswith similar statistical features, it turns out that the implications for the computational capability
of the network in performing a simple delay task are strongly dependent on the particular scenario.
Namely, only the scenario involving slow dynamics of synapses results in an appreciable extension of the
network's dynamic memory. In other cases, the dynamic memory remains short despite the emergence of long
timescales in the neuronal spike trains.