Dissertation Defense - Tibin John

Award Date

Friday, August 5, 2022

August 5, 2022

“Time-warped Behaviors are Logarithmically Transformed into Stable Hippocampal Sequences”

The mammalian brain evolved to robustly encode spatial sequences despite time-warp caused by movement at variable speeds, but time-warped behaviors are incompatible with traditionally-assumed fixed neural coding schemes. Here, we propose and empirically validate an alternative framework – the logarithmic theta transform (LTT) – that can convert variable behavioral time into stable neural spike timing patterns that do not change with speed. LTT makes three predictions about hippocampal place cells that go beyond previous assumptions: 1) a logarithmic theta-phase curve; 2) non-uniform sequence spacing within individual theta cycles; 3) phase-shifted theta sequences at faster running speeds. Analysis of place cells during spatial exploration confirms each prediction. We discover that the hippocampus implements LTT using a scaling-factor of 1.4, identical to the scaling-factor governing the spacing of entorhinal grid modules. Thus, LTT represents an evolutionarily-optimized neural algorithm for stable encoding of information despite the warping of time by natural speed variation.


Assistant Professor Omar J. Ahmed, Chair

Professor Victoria Booth

Associate Professor Kamran Diba

Professor W. Michael King

Professor Michal Zochowski