Jonathan Cornford — accepted AI-conference papers

2026

Learning From the Past with Cascading Eligibility Traces

Tokiniaina Raharison Ralambomihanta, Ivan Anokhin, Roman Pogodin, Samira Ebrahimi Kahou, Jonathan Cornford, Blake Aaron Richards

ICLR 2026poster

Animals often receive information about errors and rewards after significant delays. In some cases these delays are fixed aspects of neural processing or sensory feedback, for example, there is typically a delay of tens to hundreds of milliseconds between motor actions and visual feedback. The stand…

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2024

How connectivity structure shapes rich and lazy learning in neural circuits

Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Todd SheaBrown, Guillaume Lajoie

ICLR 2024poster

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) chan…

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2024

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards

ICLR 2024spotlight

A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. th…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2023

Learning better with Dale’s Law: A Spectral Perspective

Pingsheng Li, Jonathan Cornford, Arna Ghosh, Blake Aaron Richards

NeurIPS 2023poster

Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dale's Law, which implies that neurons must be excitatory (E) or inhibitory (I). Dale's Law is generally absent from RNNs because simply partitioning a standard network's units into E and I populat…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗

2021

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units

Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette, Dimitri Michael Kullmann, Blake Aaron Richards

ICLR 2021poster

The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principl…

Cited by 47 Source ↗PDF ↗Scholar ↗