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20072021
most citedA high-bias, low-variance introduction to Machine Learning for physicists

932 citations · 2.2k across the 38 of their papers we have counts for

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5 papers · 1 filter

q-bio.NC2020★ 2 cited

Superlinear Precision and Memory in Simple Population Codes

Jimmy H. J. Kim, Ila Fiete, David J. Schwab

The brain constructs population codes to represent stimuli through widely distributed patterns of activity across neurons. An important figure of merit of population codes is how m…

q-bio.NC2016

The deterministic information bottleneck

DJ Strouse, David J Schwab

Lossy compression and clustering fundamentally involve a decision about what features are relevant and which are not. The information bottleneck method (IB) by Tishby, Pereira, and…

q-bio.NC2014★ 5 cited

A binary Hopfield network with information rate and applications to grid cell decoding

Ila Fiete, David J. Schwab, Ngoc M. Tran

A Hopfield network is an auto-associative, distributive model of neural memory storage and retrieval. A form of error-correcting code, the Hopfield network can learn a set of patte…

q-bio.NC2013★ 122 cited

Zipf's law and criticality in multivariate data without fine-tuning

David J. Schwab, Ilya Nemenman, Pankaj Mehta

The joint probability distribution of many degrees of freedom in biological systems, such as firing patterns in neural networks or antibody sequence composition in zebrafish, often…

q-bio.NC2008★ 45 cited

Rhythmogenic neuronal networks, pacemakers, and k-cores

David J. Schwab, Robijn F. Bruinsma, Alex J. Levine

Neuronal networks are controlled by a combination of the dynamics of individual neurons and the connectivity of the network that links them together. We study a minimal model of th…