NeuroChaT: A toolbox to analyse the dynamics of neuronal encoding in freely-behaving rodents in vivo.

Md Nurul Islam,John P Aggleton,Shane M O’Mara,Seán K Martin

doi:10.12688/wellcomeopenres.15533.1

Md Nurul Islam, John P Aggleton + Show 2 more

Open Access

https://doi.org/10.12688/wellcomeopenres.15533.1

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Abstract

There is a dearth of freely-available, standardised open source analysis tools available for the analysis of neuronal signals recorded in vivo in the freely-behaving animal. In response, we have developed a freely-available, open-source toolbox, NeuroChaT ( Neuron Characterisation Toolbox), specifically addressing this lacuna. Although we have particularly emphasised single unit analyses for spatial coding, NeuroChaT also characterises rhythmic properties of units and their dynamics associated with local field potential signals. NeuroChaT was developed using Python and facilitates a complete pipeline from automation of analysis to producing and managing publication-quality figures. Additionally, we have adopted a platform-independent format (Hierarchical Data Format version 5) for storing recorded and analysed data. By providing an easy-to-use software package, we aim to simplify the adoption of standardised analyses for behavioural neurophysiology and facilitate open data sharing and collaboration between laboratories.

Highlights

Where and how spatial information is represented in the brain has been of great scientific interest since O’Keefe and Dostrovsky[1] first described the spatially-receptive fields of hippocampal neurons
Available packages do not often facilitate quick implementation and integration of new techniques along with established ones given the challenges associated with the evolution of new technology. To address this important lacuna, we have developed a toolbox, NeuroChaT (Neuron Characterisation Toolbox), a graphical user interface (GUI)-based open-source software that brings together peer-reviewed analysis methods in a unified framework for greater accessibility and to provide an easier implementation of analyses
NeuroChaT is hosted in a GitHub repository

Summary

Introduction

Where and how spatial information is represented in the brain has been of great scientific interest since O’Keefe and Dostrovsky[1] first described the spatially-receptive fields of hippocampal neurons (since named ‘place cells’). Many spatially-responsive cell types have been described, including head direction cells[2,3], grid cells (neurons with multiple receptive fields arranged in a triangular grid)[4,5], as well as boundary cells and object cells (neurons that respond to objects placed in the environment)[6,7]. Neurons tuned to nonspatial, natural stimuli (e.g. speed cells), have been described, and are likely to contribute to the dynamic representations of ‘self-location’, such as for path integration[8,9]

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