Search Results for author: Eric Shea-Brown

Found 9 papers, 4 papers with code

Expressive probabilistic sampling in recurrent neural networks

no code implementations22 Aug 2023 Shirui Chen, Linxin Preston Jiang, Rajesh P. N. Rao, Eric Shea-Brown

We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution.


Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

1 code implementation2 Jun 2022 Yuhan Helena Liu, Arna Ghosh, Blake A. Richards, Eric Shea-Brown, Guillaume Lajoie

We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely.

Learning Theory

Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators

1 code implementation2 Jun 2022 Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül

Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex

no code implementations NeurIPS 2019 Jianghong Shi, Eric Shea-Brown, Michael Buice

Several groups have developed metrics that provide a quantitative comparison between representations computed by networks and representations measured in cortex.

Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity

no code implementations NeurIPS Workshop Neuro_AI 2019 Daniel Zdeblick, Eric Shea-Brown, Daniela Witten, Michael Buice

Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.

High resolution neural connectivity from incomplete tracing data using nonnegative spline regression

1 code implementation NeurIPS 2016 Kameron Decker Harris, Stefan Mihalas, Eric Shea-Brown

We demonstrate the efficacy of a low rank version on visual cortex data and discuss the possibility of extending this to a whole-brain connectivity matrix at the voxel scale.

Matrix Completion regression

On stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons

1 code implementation21 Sep 2010 Joshua H. Goldwyn, Nikita S. Imennov, Michael Famulare, Eric Shea-Brown

We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits.

Neurons and Cognition Quantitative Methods

Cannot find the paper you are looking for? You can Submit a new open access paper.