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Featured Co-authors

David Sussillo
17 publications
SueYeon Chung
16 publications
Haim Sompolinsky
14 publications
Alessandro Ingrosso
11 publications
Rafal Jozefowicz
10 publications
Chethan Pandarinath
7 publications
David G. Clark
5 publications
Ashok Litwin-Kumar
4 publications
Christopher J. Cueva
4 publications
Ran Rubin
4 publications
Kanaka Rajan
3 publications

∙ 02/17/2023

Theory of coupled neuronal-synaptic dynamics

In neural circuits, synapses influence neurons by shaping network dynami...

0 David G. Clark, et al. ∙

∙ 07/25/2022

Dimension of Activity in Random Neural Networks

Neural networks are high-dimensional nonlinear dynamical systems that pr...

0 David G. Clark, et al. ∙

∙ 02/05/2022

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

Understanding the asymptotic behavior of gradient-descent training of de...

0 Samuel Lippl, et al. ∙

∙ 01/24/2022

Input correlations impede suppression of chaos and learning in balanced rate networks

Neural circuits exhibit complex activity patterns, both spontaneously an...

4 Rainer Engelken, et al. ∙

∙ 06/08/2021

Credit Assignment Through Broadcasting a Global Error Vector

Backpropagation (BP) uses detailed, unit-specific feedback to train deep...

0 David G. Clark, et al. ∙

∙ 04/14/2021

Neural population geometry: An approach for understanding biological and artificial neural networks

Advances in experimental neuroscience have transformed our ability to ex...

0 SueYeon Chung, et al. ∙

∙ 12/29/2018

Training dynamically balanced excitatory-inhibitory networks

The construction of biologically plausible models of neural circuits is ...

0 Alessandro Ingrosso, et al. ∙

∙ 12/12/2018

Feedback alignment in deep convolutional networks

Ongoing studies have identified similarities between neural representati...

0 Theodore H. Moskovitz, et al. ∙

∙ 10/09/2017

full-FORCE: A Target-Based Method for Training Recurrent Networks

Trained recurrent networks are powerful tools for modeling dynamic neura...

0 Brian DePasquale, et al. ∙

∙ 05/03/2017

Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity

Neurons and networks in the cerebral cortex must operate reliably despit...

0 Ran Rubin, et al. ∙

∙ 08/22/2016

LFADS - Latent Factor Analysis via Dynamical Systems

Neuroscience is experiencing a data revolution in which many hundreds or...

0 David Sussillo, et al. ∙

∙ 12/19/2014

Random Walk Initialization for Training Very Deep Feedforward Networks

Training very deep networks is an important open problem in machine lear...

0 David Sussillo, et al. ∙

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