# Interspike interval correlations in networks of inhibitory integrate-and-fire neurons

## TL;DR

This paper investigates how inhibitory neuron networks exhibit temporal correlations in interspike intervals, revealing transitions to negative correlations influenced by network parameters and oscillation states, with a new noise-reduced model explaining these phenomena.

## Contribution

The study introduces a comprehensive analysis of ISI correlations in inhibitory networks and develops a noise-reduced diffusion approximation to explain observed correlation transitions.

## Key findings

- Negative SCCs occur at intermediate bias and network sizes.
- SCC becomes negative at the onset of network oscillations.
- Results are robust across different synaptic connectivity schemes.

## Abstract

We study temporal correlations of interspike intervals (ISIs), quantified by the network-averaged serial correlation coefficient (SCC), in networks of both current- and conductance-based purely inhibitory integrate-and-fire neurons. Numerical simulations reveal transitions to negative SCCs at intermediate values of bias current drive and network size. As bias drive and network size are increased past these values, the SCC returns to zero. The SCC is maximally negative at an intermediate value of the network oscillation strength. The dependence of the SCC on two canonical schemes for synaptic connectivity is studied, and it is shown that the results occur robustly in both schemes. For conductance-based synapses, the SCC becomes negative at the onset of both a fast and slow coherent network oscillation. Finally, we devise a noise-reduced diffusion approximation for current-based networks that accounts for the observed temporal correlation transitions.