1 code implementation • 2 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.
no code implementations • 5 Jan 2022 • Anthony GX-Chen, Veronica Chelu, Blake A. Richards, Joelle Pineau
We illustrate that incorporating predictive knowledge through an $\eta\gamma$-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i. e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models.
no code implementations • 12 May 2021 • Luke Y. Prince, Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks.
1 code implementation • ICLR 2020 • Jordan Guerguiev, Konrad P. Kording, Blake A. Richards
Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Luke Y. Prince, Blake A. Richards
A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.
1 code implementation • NeurIPS 2018 • Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap
Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance.
1 code implementation • 1 Oct 2016 • Jordan Guergiuev, Timothy P. Lillicrap, Blake A. Richards
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology.