Decoding Spiking Mechanism with Dynamic Learning on Neuron Population
This work addresses a challenge in cognitive neuroscience for researchers analyzing real spike data, offering a novel method that improves decoding accuracy, though it appears incremental in advancing existing neural network approaches.
The authors tackled the problem of decoding neural spike trains from neuron populations to infer latent representations, proposing a Neuron Activation Network that reconstructs neuron information and spiking states. Their model applied to retinal ganglion cells generated neural spike sequences with higher fidelity than state-of-the-art methods, showing improved expressiveness and potential for revealing latent mechanisms.
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.