Search Results for author: Blake Richards

Found 12 papers, 3 papers with code

Synaptic Weight Distributions Depend on the Geometry of Plasticity

no code implementations30 May 2023 Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Richards

Overall, this work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.

Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer

no code implementations29 Nov 2022 Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Richards

When presented with a data stream of two statistically dependent variables, predicting the future of one of the variables (the target stream) can benefit from information about both its history and the history of the other variable (the source stream).

ConSpec: honing in on critical steps for rapid learning and generalization in RL

1 code implementation12 Oct 2022 Chen Sun, Wannan Yang, Thomas Jiralerspong, Dane Malenfant, Benjamin Alsbury-Nealy, Yoshua Bengio, Blake Richards

These critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment.

Continuous Control Contrastive Learning +3

The neuroconnectionist research programme

no code implementations8 Sep 2022 Adrien Doerig, Rowan Sommers, Katja Seeliger, Blake Richards, Jenann Ismael, Grace Lindsay, Konrad Kording, Talia Konkle, Marcel A. J. van Gerven, Nikolaus Kriegeskorte, Tim C. Kietzmann

Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.


On Neural Architecture Inductive Biases for Relational Tasks

1 code implementation9 Jun 2022 Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake Richards, Guillaume Lajoie

Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems.

Inductive Bias Out-of-Distribution Generalization

Investigating Power laws in Deep Representation Learning

no code implementations11 Feb 2022 Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal, Blake Richards

Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with self-supervised learning (SSL).

Representation Learning Scene Recognition +1

Towards Scaling Difference Target Propagation by Learning Backprop Targets

1 code implementation31 Jan 2022 Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio

As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

no code implementations NeurIPS 2021 Shahab Bakhtiari, Patrick Mineault, Timothy Lillicrap, Christopher Pack, Blake Richards

We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex.

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