Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks

Abstract

Abstract We present here a novel method for the classical task of extracting reoccurring spatiotemporal patterns from spiking activity of large populations of neurons. In contrast to previous studies that mainly focus on synchrony detection or exactly recurring binary patterns, we perform the search in an approximate way that clusters together nearby, noisy network states in the data. Our approach is to use minimum probability flow (MPF) parameter estimation to determinis- tically fit very large Hopfield networks on windowed spike trains obtained from recordings of spontaneous activity of neurons in cat visual cortex. Examining the structure of the network memories over the spiking activity after training, we find that the networks robustly discover long-range temporal correlations. Specifically, the recurrent network dynamics denoise and group together windowed spike patterns, revealing underlying structure such as cell assemblies. We first demonstrate this by computing an analogy to spike triggered averages that we call memory triggered averages (MTAs). MTAs are obtained by averaging raw spike train windows that converge under the network dynamics to the same memory. The MTAs reveal promi- nent repeating patterns in the data that are difficult to detect with standard methods such as PCA. Additionally, when memories are collected over eight disjoint epochs in 280 seconds of windowed spiking activity from 50 neurons, their counts are nearly identical and the networks store significantly more memories than would be possible if trained on random patterns.