Temporal Decoding Dynamics: Insights from Prefrontal Cortex and Striatum During Rapid Learning

Understanding neural encoding of time is crucial for advancing computational models of cognition. We investigated time encoding evolution in the medial prefrontal cortex (mPFC) and dorsal striatum (STR) during a rapid learning task in rats. Using a novel protocol, rats achieved proficient timing of a 1.5-second interval within a single session. We applied machine learning algorithms to decode neural activity, revealing a dynamic shift: the mPFC encoded time during the initial learning stages, while the STR assumed this role as proficiency increased. Pharmacological inactivation provided causal evidence of these roles. Our study suggests that even simple uses of machine learning in decoding and modeling complex neural processes can help gain insights into the dynamic nature of temporal encoding in the brain.

Short Bio

Dr. Marcelo Bussotti Reyes was born in São Paulo, Brazil, and completed his undergraduate degree in physics at the University of São Paulo in 1998. He obtained his Master's in Physics (2001) and his Ph.D. in Neuroscience (2005) at the same institution. Dr. Reyes undertook a postdoctoral fellowship at the University of California, San Diego, investigating the electrophysiological dynamics of crustacean central pattern generators. He then pursued another postdoctoral term from 2008 to 2010 at the Medical University of South Carolina, where he expanded his research into cognitive processes and experimental psychology, studying time perception in rats. Dr. Reyes is an Associate Professor at the Center for Mathematics, Computing, and Cognition at the Federal University of ABC (UFABC) in Santo André, Brazil. His research interests encompass timing and time perception, utilizing electrophysiological experiments and computational modeling.

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