Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework

TL;DR

This paper examines how conductance-based synapses, which are more biologically realistic, affect the Neural Engineering Framework's models, especially in complex neural dynamics, highlighting limitations of simple translation methods.

Contribution

It provides a detailed analysis of integrating conductance-based synapses into the NEF and evaluates the effectiveness of naive translation approaches.

Findings

Naive translation works well for feed-forward channels.

Performance degrades in complex, integrative NEF networks.

Conductance-based models influence inhibitory effects significantly.

Abstract

The mathematical model underlying the Neural Engineering Framework (NEF) expresses neuronal input as a linear combination of synaptic currents. However, in biology, synapses are not perfect current sources and are thus nonlinear. Detailed synapse models are based on channel conductances instead of currents, which require independent handling of excitatory and inhibitory synapses. This, in particular, significantly affects the influence of inhibitory signals on the neuronal dynamics. In this technical report we first summarize the relevant portions of the NEF and conductance-based synapse models. We then discuss a na\"ive translation between populations of LIF neurons with current- and conductance-based synapses based on an estimation of an average membrane potential. Experiments show that this simple approach works relatively well for feed-forward communication channels, yet performance degrades for NEF networks describing more complex dynamics, such as integration.