Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
This is an incremental technical improvement for computational neuroscience and neuromorphic engineering, addressing a specific limitation in neural modeling.
The paper tackled the mismatch between the Neural Engineering Framework's linear current-based synapse model and biological nonlinear conductance-based synapses, particularly for inhibitory signals, by proposing a naive translation method that works well for feed-forward channels but degrades for complex dynamics like integration.
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 naïve 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.