Topic: compressed representations

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of

The increasing complexity and volume of extracellular neural data generated by modern silicon-based probes present significant storage, transmission, and analysis challenges. Spike sorting algorithms have been developed to cope with this data deluge, but innovative approaches are required to compress the data while maintaining information fidelity efficiently.This project explores the application of deep learning techniques for compressing extracellular neural data, leveraging the hierarchical representations learned by convolutional neural networks to achieve efficient compression without com

While the hippocampus is known to encode physical position through place cells, substantial evidence indicates that hippocampal neurons also respond to a variety of other non-spatial sensory information. We explore the hypothesis that these representations emerge through generic computations implemented by the hippocampus that actively learn from the statistical structure of experience. Theoretical work has shown that place cell coding arises as a special case during spatial exploration in networks trained to learn sparse, compressed representations, due to the local correlations present in th