Introduction

hdnet is a Python package for analysis of neural population spiking data, i.e. parallel spike trains.

In particular, it provides a novel method for finding and extracting salient low-dimensional representations of the dynamics of populations of spiking neurons based on a denoising approach to spatiotemporal patterns (STP) contained in the data.

Finding STP is classical problem in data analysis of parallel spike trains, and quite a number of approaches to detect and classify recurring spatiotemporal patterns (STP) of neural population activity were proposed [Gruen2010].

Yet, most published methods so far either focus solely on synchrony detection [Pipa2008], [PicadoMuino2013], [LopesDosSantos2013] or assume a more or less noiseless scenario, seeking to classify exactly recurring STP in neuronal activity (apart from allowing some jitter in spike timing), see e.g. [Gansel2012].

Given the usually high variability of population responses to stimuli, the re-occurrence of such exactly repeating STP becomes more and more unlikely with increasing population size, though. Assuming that despite this variability, network activity is not random per se (under the experimentally well-supported hypothesis [Abeles1993] [Arieli1995] that the population has to code information about stimuli in some form of STP), a much more plausible situation is that some underlying STP appears in several “corrupted” variants, both expressing jitter in spike times and differing in a few missing or excess spikes (characterized by a low Hamming distance to a true, underlying STP).

Our method takes a different aproach. Using Hopfield networks trained with minimum probability flow (MPF), the occuring raw spatiotemporal patterns are grouped into clusters of similar patterns in an unsupervised way, assigning to each cluster a memory (the fixed point of the Hopfield dynamics in each cluster).

The proposed method is robust to this variability in the signal and able to extract the underlying recurring patterns, even for seldomly occurring STP and large population sizes.

See Section Mathematical background for an introduction of the mathematical background behind some of the techniques used in hdnet.