Relevance Realization and the Emerging Framework in Cognitive Science. - John Vervaeke; Timothy P. Lillicrap; Blake A. Richards (J. Log. Comput., 2012)

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. - Sergey Bartunov; Adam Santoro; Blake A. Richards; Luke Marris; Geoffrey E. Hinton; Timothy P. Lillicrap (NeurIPS, 2018)

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. - Sergey Bartunov; Adam Santoro; Blake A. Richards; Geoffrey E. Hinton; Timothy P. Lillicrap (CoRR, 2018)

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia. - Nathan Insel; Jordan Guerguiev; Blake A. Richards (PLoS Comput. Biol., 2018)

Moving beyond reward prediction errors. - Blake A. Richards (Nat. Mach. Intell., 2019)

Spike-based causal inference for weight alignment. - Jordan Guerguiev; Konrad P. Körding; Blake A. Richards (ICLR, 2020)

Adversarial Feature Desensitization. - Pouya Bashivan; Blake A. Richards; Irina Rish (CoRR, 2020)

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, 2021)

PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks. - Olivier Tessier-Larivière; Luke Y. Prince; Pascal Fortier-Poisson; Lorenz Wernisch; Oliver Armitage; Emil Hewage; Guillaume Lajoie; Blake A. Richards (NER, 2021)

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. - Shahab Bakhtiari; Patrick J. Mineault; Timothy P. Lillicrap; Christopher C. Pack; Blake A. Richards (NeurIPS, 2021)

Adversarial Feature Desensitization. - Pouya Bashivan; Reza Bayat; Adam Ibrahim; Kartik Ahuja; Mojtaba Faramarzi; Touraj Laleh; Blake A. Richards; Irina Rish (NeurIPS, 2021)

Your head is there to move you around: Goal-driven models of the primate dorsal pathway. - Patrick J. Mineault; Shahab Bakhtiari; Blake A. Richards; Christopher C. Pack (NeurIPS, 2021)

CCN GAC Workshop: Issues with learning in biological recurrent neural networks. - Luke Y. Prince; Ellen Boven; Roy Henha Eyono; Arna Ghosh; Joe Pemberton; Franz Scherr; Claudia Clopath; Rui Ponte Costa; Wolfgang Maass 001; Blake A. Richards; Cristina Savin; Katharina Anna Wilmes (CoRR, 2021)

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. - Nicholas Roy; Ingmar Posner; Tim D. Barfoot; Philippe Beaudoin; Yoshua Bengio; Jeannette Bohg; Oliver Brock; Isabelle Depatie; Dieter Fox; Daniel E. Koditschek; Tomás Lozano-Pérez; Vikash Mansinghka 001; et al. (CoRR, 2021)

Learning function from structure in neuromorphic networks. - Laura E. Suárez; Blake A. Richards; Guillaume Lajoie; Bratislav Misic (Nat. Mach. Intell., 2021)

The overfitted brain hypothesis. - Luke Y. Prince; Blake A. Richards (Patterns, 2021)

A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions. - Anthony GX-Chen; Veronica Chelu; Blake A. Richards; Joelle Pineau (AAAI, 2022)

Towards Scaling Difference Target Propagation by Learning Backprop Targets. - Maxence Ernoult; Fabrice Normandin; Abhinav Moudgil; Sean Spinney; Eugene Belilovsky; Irina Rish; Blake A. Richards; Yoshua Bengio (ICML, 2022)

$ alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. - Kumar Krishna Agrawal; Arnab Kumar Mondal; Arna Ghosh; Blake A. Richards (NeurIPS, 2022)

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules. - Yuhan Helena Liu; Arna Ghosh; Blake A. Richards; Eric Shea-Brown; Guillaume Lajoie (NeurIPS, 2022)

Investigating Power laws in Deep Representation Learning. - Arna Ghosh; Arnab Kumar Mondal; Kumar Krishna Agrawal; Blake A. Richards (CoRR, 2022)

Evaluating Multimodal Interactive Agents. - Josh Abramson; Arun Ahuja; Federico Carnevale; Petko Georgiev; Alex Goldin; Alden Hung; Jessica Landon; Timothy P. Lillicrap; Alistair Muldal; Blake A. Richards; Adam Santoro; Tamara von Glehn; et al. (CoRR, 2022)

On Neural Architecture Inductive Biases for Relational Tasks. - Giancarlo Kerg; Sarthak Mittal; David Rolnick; Yoshua Bengio; Blake A. Richards; Guillaume Lajoie (CoRR, 2022)

Contrastive introspection (ConSpec) to rapidly identify invariant steps for success. - Chen Sun 007; Wannan Yang; Benjamin Alsbury-Nealy; Yoshua Bengio; Blake A. Richards (CoRR, 2022)

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. - Anthony Zador; Blake A. Richards; Bence Ölveczky; Sean Escola; Yoshua Bengio; Kwabena Boahen 001; Matthew M. Botvinick; Dmitri B. Chklovskii; Anne Churchland; Claudia Clopath; James DiCarlo; Surya Ganguli; et al. (CoRR, 2022)

The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. - Blake A. Richards; Timothy P. Lillicrap (Frontiers Comput. Sci., 2022)

Forgetting Enhances Episodic Control With Structured Memories. - Annik Yalnizyan-Carson; Blake A. Richards (Frontiers Comput. Neurosci., 2022)

How gradient estimator variance and bias impact learning in neural networks. - Arna Ghosh; Yuhan Helena Liu; Guillaume Lajoie; Konrad P. Körding; Blake Aaron Richards (ICLR, 2023)

Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL. - Chen Sun 007; Wannan Yang; Thomas Jiralerspong; Dane Malenfant; Benjamin Alsbury-Nealy; Yoshua Bengio; Blake A. Richards (NeurIPS, 2023)

A Unified, Scalable Framework for Neural Population Decoding. - Mehdi Azabou; Vinam Arora; Venkataramana Ganesh; Ximeng Mao; Santosh Nachimuthu; Michael Mendelson; Blake A. Richards; Matthew G. Perich; Guillaume Lajoie; Eva L. Dyer (NeurIPS, 2023)

Formalizing locality for normative synaptic plasticity models. - Colin Bredenberg; Ezekiel Williams; Cristina Savin; Blake A. Richards; Guillaume Lajoie (NeurIPS, 2023)

Learning better with Dale's Law: A Spectral Perspective. - Pingsheng Li; Jonathan Cornford; Arna Ghosh; Blake A. Richards (NeurIPS, 2023)

Addressing Sample Inefficiency in Multi-View Representation Learning. - Kumar Krishna Agrawal; Arna Ghosh; Adam Oberman; Blake A. Richards (CoRR, 2023)

Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. - Damjan Kalajdzievski; Ximeng Mao; Pascal Fortier-Poisson; Guillaume Lajoie; Blake Aaron Richards (Trans. Mach. Learn. Res., 2023)

Sufficient conditions for offline reactivation in recurrent neural networks. - Nanda H. Krishna; Colin Bredenberg; Daniel Levenstein; Blake Aaron Richards; Guillaume Lajoie (ICLR, 2024)

Synaptic Weight Distributions Depend on the Geometry of Plasticity. - Roman Pogodin; Jonathan Cornford; Arna Ghosh; Gauthier Gidel; Guillaume Lajoie; Blake Aaron Richards (ICLR, 2024)

Learning Successor Features the Simple Way. - Raymond Chua; Arna Ghosh; Christos Kaplanis; Blake A. Richards; Doina Precup (NeurIPS, 2024)

Harnessing small projectors and multiple views for efficient vision pretraining. - Arna Ghosh; Kumar Krishna Agrawal; Shagun Sodhani; Adam Oberman; Blake A. Richards (NeurIPS, 2024)

Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. - Yizi Zhang; Yanchen Wang; Donato Jiménez-Benetó; Zixuan Wang; Mehdi Azabou; Blake A. Richards; Renee Tung; Olivier Winter; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (NeurIPS, 2024)

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. - David Raposo; Samuel Ritter; Blake A. Richards; Timothy P. Lillicrap; Peter Conway Humphreys; Adam Santoro (CoRR, 2024)

Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent. - Karolis Jucys; George Adamopoulos; Mehrab Hamidi; Stephanie Milani; Mohammad Reza Samsami; Artem Zholus; Sonia Joseph; Blake A. Richards; Irina Rish; Özgür Simsek (CoRR, 2024)

Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. - Yizi Zhang; Yanchen Wang; Donato Jiménez-Benetó; Zixuan Wang; Mehdi Azabou; Blake A. Richards; Olivier Winter; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (CoRR, 2024)

Multi-session, multi-task neural decoding from distinct cell-types and brain regions. - Mehdi Azabou; Krystal Xuejing Pan; Vinam Arora; Ian Jarratt Knight; Eva L. Dyer; Blake Aaron Richards (ICLR, 2025)

Multi-agent cooperation through learning-aware policy gradients. - Alexander Meulemans; Seijin Kobayashi; Johannes von Oswald; Nino Scherrer; Eric Elmoznino; Blake Aaron Richards; Guillaume Lajoie; Blaise Agüera y Arcas; João Sacramento (ICLR, 2025)

Steering CLIP's vision transformer with sparse autoencoders. - Sonia Joseph; Praneet Suresh; Ethan Goldfarb; Lorenz Hufe; Yossi Gandelsman; Robert Graham; Danilo Bzdok; Wojciech Samek; Blake Aaron Richards (CoRR, 2025)

Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video. - Sonia Joseph; Praneet Suresh; Lorenz Hufe; Edward Stevinson; Robert Graham; Yash Vadi; Danilo Bzdok; Sebastian Lapuschkin; Lee Sharkey; Blake Aaron Richards (CoRR, 2025)

Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists? - Anthony GX-Chen; Dongyan Lin; Mandana Samiei; Doina Precup; Blake A. Richards; Rob Fergus; Kenneth Marino (CoRR, 2025)

The challenge of hidden gifts in multi-agent reinforcement learning. - Dane Malenfant; Blake A. Richards (CoRR, 2025)