Effects of synaptic noise on a neuronal pool model with strong excitatory drive and recurrent inhibition

Abstract

A model originally proposed by Akazawa and Kato (1990)for the spinal cord was adopted as prototypical of a neuronal pool with strong excitatory drive and strong recurrent inhibition. Our simulations of the model have shown that a strong synchronization occurs between the spike trains in the neuronal pool. This happens because the proposed model has a single and strong excitatory drive on the neuronal pool. However, usually a multitude of other randomly occurring synaptic inputs impinge on the neuronal pool and therefore a new investigation was carried out to study the effects of synaptic noise on the network behavior. The synaptic noise decreased the degree of synchronization of the neuronal spike trains but on the other hand caused an unexpected decrease in the mean firing rate of the neuronal pool. A detailed analysis indicated that this phenomenon is due to a combination of two mechanisms: a saturation of the feedback inhibition and a decrease of the synchronization in the neuronal pool with synaptic noise. The synaptic noise caused a more frequent activation of the saturated recurrent inhibitory feedback loop along time, thereby increasing the inhibitory effect on the neuronal pool.