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

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.

A Fully Automated Approach to Spike Sorting

“…Errors are expected when these time-collapsed clusters overlap with one another, however. That said, because MountainSort can be run on overlapping sections of data, it is conceptually straightforward to include augmentations that link clusters across time slices using segmentation fusion based algorithms (Dhawale AK, 2015). Cases where clusters drift in and out of noise or other clusters are more problematic, both for MountainSort and for all other current approaches.…”

“…Manual sorting can have error rates in excess of 20% (Wood et al, 2004) and there is substantial variability in labeling across different sorting sessions (Harris et al, 2000; Pedreira et al, 2012; Rossant et al, 2016). Furthermore, the human spike sorter could never keep up with the increasing volume of data arising from increasingly large electrode arrays applied over increasingly long durations (Berenyi et al, 2014; Dhawale AK, 2015; Du et al, 2011; Lopez et al, 2016; Santhanam et al, 2007; Shobe et al, 2015). …”

Improving data quality in neuronal population recordings

“…Similar constraints are faced in several other fields of data-intensive biology, such as electron-microscopic connectomics, which also rely on manual operator curation 38 . Thus, while substantial developments have occurred to reduce the amount of manual operator time required for the sorting process 37, 39 , fully automatic systems are rarely applied in current practice. Fortunately, analyses comparing the decisions made by multiple expert operators have shown that their corrections of automatic cluster performance tend to be similar 17, 37 .…”

“…Sampling bias in extracellular electrophysiology can be ameliorated by performing non-stop chronic recordings, using fixed electrodes, over very long time periods: recording 24 hours/day for days or weeks can lead to sufficient spike numbers to define clusters even for cells of very low firing rate 39 .…”

“…This problem is particularly severe in acute recordings, in which the electrode is fixed not to the skull, but to an external manipulator, meaning that small movements of the head, or relaxation of the brain after compression caused by probe insertion, will cause a movement of the cells relative to the electrodes. Physical electrode drifts are less serious in chronic recordings, where stable recordings have been observed for periods of days or weeks 39, 75 .…”