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Exact minimax entropy models of large-scale neuronal activity

Autor: Lynn, Christopher W., Yu, Qiwei, Pang, Rich, Palmer, Stephanie E., Bialek, William

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include i

Externí odkaz: http://arxiv.org/abs/2402.00007

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Exactly solvable statistical physics models for large neuronal populations

Autor: Lynn, Christopher W., Yu, Qiwei, Pang, Rich, Bialek, William, Palmer, Stephanie E.

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of $N\sim 100$ neurons. As $N$ increases in new experiments, we

Externí odkaz: http://arxiv.org/abs/2310.10860

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A crossover code for high-dimensional composition

Autor: Pang, Rich

We present a novel way to encode compositional information in high-dimensional (HD) vectors. Inspired by chromosomal crossover, random HD vectors are recursively interwoven, with a fraction of one vector's components masked out and replaced by those

Externí odkaz: http://arxiv.org/abs/1911.06775

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