Unsupervised identification of states from voltage recordings of neural networks

TitleUnsupervised identification of states from voltage recordings of neural networks
Publication TypeJournal Article
Year of Publication2019
AuthorsGarolini D., Vitalis A., Caflisch A.
JournalJournal of Neuroscience Methods
Volume318
Start Page104
Pagination104-117
Date Published2019 Feb 23
Type of ArticleResearch Article
ISSN1872-678X
KeywordsBRIAN, density clustering, Hippocampus, interneurons, metastable states, NetSAP, neural network, neuroscience, SAPPHIRE plot, simulations, unsupervised learning, voltage recordings
Abstract

BACKGROUND: Modern techniques for multi-neuronal recording produce large amounts of data. There is no automatic procedure for the identification of states in recurrent voltage patterns.

NEW METHOD: We propose NetSAP (Network States And Pathways), a data-driven analysis method that is able to recognize multi-neuron voltage patterns (states). To capture the subtle differences between snapshots in voltage recordings, NetSAP infers the underlying functional neural network in a time-resolved manner with a sliding window approach. Then NetSAP identifies states from a reordering of the time series of inferred networks according to a user-defined metric. The procedure for unsupervised identification of states was developed originally for the analysis of molecular dynamics simulations of proteins.

RESULTS: We tested NetSAP on neural network simulations of GABAergic inhibitory interneurons. Most simulation parameters are chosen to reproduce literature observations, and we keep noise terms as control parameters to regulate the coherence of the simulated signals. NetSAP is able to identify multiple states even in the case of high internal noise and low signal coherence. We provide evidence that NetSAP is robust for networks with up to about 50 % of the neurons spiking randomly. NetSAP is scalable and its code is open source.

COMPARISON WITH EXISTING METHODS: NetSAP outperforms common analysis techniques, such as PCA and k-means clustering, on a simulated recording of voltage traces of 50 neurons.

CONCLUSIONS: NetSAP analysis is an efficient tool to identify voltage patterns from neuronal recordings.

DOI10.1016/j.jneumeth.2019.01.019
pubindex

0245

Alternate JournalJ. Neurosci. Methods
PubMed ID30807781
Highlight Role: 
Algorithms and Data Analysis