Automated long-term recording and analysis of neural activity in behaving animals.

Ashesh K Dhawale,Valentin A Normand,Evi Kopelowitz,Rajesh Poddar,Bence P Ölveczky,Steffen Be Wolff

doi:10.7554/elife.27702

Abstract

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

Highlights

The goal of systems neuroscience is to understand how neural activity generates behavior
Intermittent recordings are ill-suited for reliably tracking the same neurons over time (Dickey et al, 2009; Emondi et al, 2004; Fraser and Schwartz, 2012; McMahon et al, 2014a; Santhanam et al, 2007; Tolias et al, 2007), making it difficult to discern how neural activity and task representations are shaped by developmental and learning processes that evolve over longer timescales (Ganguly et al, 2011; Jog et al, 1999; Lutcke et al, 2013; Marder and Goaillard, 2006; Peters et al, 2014; Singer et al, 2013)
We tracked units over days using a novel spike-sorting algorithm we developed to cluster continuously recorded neural data. (D) Continuous extracellular recordings pose challenges for spikesorting methods assuming stationarity in spike shapes. (Top) Peak-to-peak spike amplitudes of two continuously recorded units accumulated over 1 hr, 25 hr and 49 hr. (Bottom) Drift in spike waveforms can lead to inappropriate splitting of single-units and/or merging of distinct units, even though these two units are separable in the hour-long ‘sessions’ shown in C

Summary

Introduction

The goal of systems neuroscience is to understand how neural activity generates behavior. Intermittent recordings are ill-suited for reliably tracking the same neurons over time (Dickey et al, 2009; Emondi et al, 2004; Fraser and Schwartz, 2012; McMahon et al, 2014a; Santhanam et al, 2007; Tolias et al, 2007), making it difficult to discern how neural activity and task representations are shaped by developmental and learning processes that evolve over longer timescales (Ganguly et al, 2011; Jog et al, 1999; Lutcke et al, 2013; Marder and Goaillard, 2006; Peters et al, 2014; Singer et al, 2013) Addressing such fundamental questions would be greatly helped by recording neural activity and behavior continuously over days and weeks in freely moving animals.

Methods

Results

Conclusion