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Self-Healing CodesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Thiago Burghi. Zoom meeting link: https://zoom.us/j/92275948382 Neural representations change over time, even for habitual behaviors. This phenomena, termed “representational drift”, seems to be at odds with long-term stable neural representations. Previously, we showed that representational drift was gradual, and might be tracked using weak error feedback. In this talk, I show how stable representations could be achieved without external error feedback. I’ll discuss a model for representational drift, which captures features of neural population codes observed experimentally: Tunings are typically stable, but occasionally undergo larger reconfigurations. I then discuss “self healing codes”, which combine error-correction with neural plasticity. Self-healing codes can track drift without outside error feedback. The learning rule required is biologically plausible, and amounts to a form of homeostatic Hebbian plasticity. When combined with network interactions that allow neurons to share information, such homeostatic plasticity could allow a subpopulation of stable cells to maintain an accurate readout of an unstable population code. This talk is part of the CUED Control Group Seminars series. This talk is included in these lists:
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