Decoding Spiking Mechanism with Dynamic Learning on Neuron Population

TL;DR

This paper introduces a novel neural network model called Neuron Activation Network that decodes neural spike trains to infer latent neural states, outperforming existing methods in fidelity and expressiveness.

Contribution

The paper presents a new neural network approach that explicitly extracts neural information from single-trial spike trains using spatiotemporal learning and message passing on a population graph.

Findings

Outperforms state-of-the-art methods in generating high-fidelity neural spike sequences.

More expressive in capturing neural spiking mechanisms.

Potential to reveal latent neural spiking processes.

Abstract

A main concern in cognitive neuroscience is to decode the overt neural spike train observations and infer latent representations under neural circuits. However, traditional methods entail strong prior on network structure and hardly meet the demand for real spike data. Here we propose a novel neural network approach called Neuron Activation Network that extracts neural information explicitly from single trial neuron population spike trains. Our proposed method consists of a spatiotemporal learning procedure on sensory environment and a message passing mechanism on population graph, followed by a neuron activation process in a recursive fashion. Our model is aimed to reconstruct neuron information while inferring representations of neuron spiking states. We apply our model to retinal ganglion cells and the experimental results suggest that our model holds a more potent capability in generating neural spike sequences with high fidelity than the state-of-the-art methods, as well as being more expressive and having potential to disclose latent spiking mechanism. The source code will be released with the final paper.