Empirical scaling laws in balanced networks with conductance-based synapses

TL;DR

This paper demonstrates through simulations that combining conductance-based synapses with spike time correlations in balanced networks yields more realistic membrane potential variability, aligning models closer to biological observations.

Contribution

It shows that realistic membrane potential variability emerges only when conductance-based synapses and spike time correlations are modeled together in balanced networks.

Findings

Conductance-based synapses alone predict small variability.

Spike time correlations alone predict large variability.

Their combination produces realistic variability levels.

Abstract

Strongly coupled, recurrent, balanced network models have been successful in describing and predicting many phenomena observed in cortical neural recordings. However, most balanced network models use current-based synapse models in place of more realistic, conductance-based models. Conductance-based synapse models predict unrealistically small membrane potential variability. On the other hand, introducing realistic levels of spike time correlations to models with current-based synapses predicts unrealistically large membrane potential variability. We use computer simulations to show that these two effects can cancel: Recurrent network models with conductance-based synapses and spike time correlations produce more realistic, moderate levels of membrane potential variability. Consistent with recent work on feedforward networks, our results show that including more realistic modeling assumptions produces more realistic dynamics, but only if when two modeling assumptions are included together.