Introduction
Communication between neurons involves chemical and electric synapses. Electric synapses transmission is mediated by gap junctions providing direct communication between cells, allowing faster communication than chemical synapses. Electrical coupling between cells can be found in many parts of the nervous system.
At the network level, electric synapses have several prominent effects such as neural synchronization, and generation of neural rhythms. Additionally, they are responsible for sharp peaks in the auto-correlation of ganglion cells in the retina. Dealing with spike population coding, interactions between neurons highly constrain the collective spike response of a neural assembly to stimuli. Thus, unveiling the respective effect of chemical and electric synapses on spike responses is a mandatory step towards a better understanding of spike coding.
Can we have a reasonable idea of what is the spike train statistics and how it depends on stimulus and connectivity studying a neural network model considering chemical and electrical synapses? This work answers this question.