  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.
