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

By Ashesh K. Dhawale, Rajesh Poddar, Steffen B. E. Wolff, Valentin A. Normand, Evi Kopelowitz, Bence Olveczky. 2016

148 citations recorded by the source corpus.

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons during 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 animals. 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 We record neural activity and behavior in rodents continuously (24/7) over months An automated spike-sorting method isolates and tracks single units over many weeks Neural dynamics and motor representations are highly stable over long timescales Neurons cluster into functional groups based on their activity in different states eTOC Blurb Dhawale et al. describe experimental infrastructure for recording neural activity and behavior continuously over months in freely moving rodents. A fully automated spike-sorting algorithm allows single units to be tracked over weeks of recording. Recordings from motor cortex and striatum revealed a remarkable long-term stability in both single unit activity and network dynamics.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Natural continual learning: success is a journey, not (just) a destination (2021, background): “…neuroscience literature have investigated the stability of neural representations with some indicating stable representations (Chestek et al., 2007; Dhawale et al., 2017; Katlowitz et al., 2018; Jensen et al., 2021) and others drifting representations (Liberti et al., 2016; Driscoll et al., 2017;…”
Challenges and opportunities for large-scale electrophysiology with Neuropixels probes (2018, background): “Algorithmic approaches can be taken to join groups of spikes whose shapes have shifted over time [45,46], but Neuropixels probes enable a different solution to this problem: registration of the raw data across time to undo the effects of drift.”
Causes and consequences of representational drift (2019, background): “Current Opinion in Neurobiology 2019, 58:141–147 that drift is not observed in all brain areas and for all tasks [14 ,15].”
High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays (2018, unknown): “We quantified that stability using firing rate similarity (Dhawale et al., 2017) at increasing time lags.”
Variance and invariance of neuronal long-term representations (2017, unknown): “~5 out of 1000 for up to a month [22]).”