Recent breakthroughs in neural recording technologies suggest the possibility of understanding the collective dynamics of large-scale brain circuits. However, investigations into these circuits are typically limited to short recording sessions, and overlook the possibility that dynamics can change over longer timescales due to differences in arousal, cognitive state, learning, and low-level biochemical turnover. Identifying which aspects of network behavior are sensitive to these factors, and which are persistent, would produce deeper and more contextualized understandings of many different neural systems. This goal poses severe data analytic challenges. While hundreds of neurons can be recorded over long time periods, we lack established statistical methods that track changes to the high-dimensional structure of network interactions over time. I will work with Dr. Scott Linderman, an expert in neural time series analysis, to overcome this challenge. I will collaborate with two premier experimental labs (Dr. Lisa Giocomo and Dr. Krishna Shenoy) to study circuit-level plasticity across different species (rodents and nonhuman primates) and behavioral tasks (navigation and motor learning). How these circuits reconfigure themselves over multiple hours, days, and weeks is poorly understood. This work will yield early scientific results in this regard, and develop general-purpose statistical tools for the neuroscience community.
Neural circuits can exhibit remarkable stability (e.g., to support long-term memory) as well as flexibility (e.g., to support rapid learning), but quantifying and summarizing these effects in large-scale neural recordings remains a fundamental, unsolved challenge. By developing new statistical methodologies, this research will enable scientists and clinicians to monitor changes in neural circuit behavior over long time periods in laboratory experiments and in human patients with chronic electrode implants. This will lead to a better fundamental understanding of how neurobiological systems maintain operation over long time periods, provide insights into long-term progression of neurodegenerative conditions, and improve the long-term effectiveness of neural prosthetics, which must adapt to changes in the underlying neural substrate.
