Abstract: Brains use a variety of coordinate systems to encode information. Sometimes these coordinate systems are linear and can be recovered from population activity using standard techniques. Often, how ever, they are not: many coordinate systems exhibit nonlinear global topolo gy for which such tools can be less effective. Notably, grid cells in the e ntorhinal cortex comprise two linearly independent circular coordinate syst ems that, together, exhibit toroidal topology. Recent recordings using high -density probes confirm this toroidal topology persists during spatial and non-spatial behavior, and can be quantified and decoded with persistent (co )homology.
We ask a next natural question: is the propagation of circular coordinate systems through neural circuits a generic feature of biological neural networks, or must this be learned? I f learning is necessary, how does it occur? We apply methods from topologic al data analysis developed to quantitatively measure propagation of such no nlinear manifolds across populations to address these problems. We identify a collection of connectivity and parameter regimes for feed-forward networ ks in which learning is required, and demonstrate that simple Hebbian spike -timing dependent plasticity reorganizes such networks to correctly propaga te circular coordinate systems.
CONTACT:Niko Shoncheck (Wesleyan) DTSTAMP:20260826T195818 DTSTART;TZID=America/New_York:20260902T140000 DTEND;TZID=America/New_York:20260902T150000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR