Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
Rajesh Poddar
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
Steffen BE Wolff
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
Valentin A Normand
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
Evi Kopelowitz
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States; Center for Brain Science, Harvard University, Cambridge, United States
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.
