Blake A. Richards

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Deep learning with segregated dendrites

Timothy P. Lillicrap, Jordan Guergiuev, Blake A. Richards, 2016 .

Variational inference of latent hierarchical dynamical systems in neuroscience: an application to calcium imaging data

Blake A. Richards, Luke Y. Prince, 2019 .

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

Geoffrey E. Hinton, Timothy P. Lillicrap, Adam Santoro, 2018, NeurIPS.

Relevance Realization and the Emerging Framework in Cognitive Science

Timothy P. Lillicrap, Blake A. Richards, John Vervaeke, 2012, J. Log. Comput..

Author response: Towards deep learning with segregated dendrites

Timothy P. Lillicrap, Blake A. Richards, Jordan Guerguiev, 2017 .

Spike-based causal inference for weight alignment pdf

Konrad P. Körding, Blake A. Richards, Jordan Guerguiev, 2020, ICLR.

Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets

Danilo Bzdok, Konrad P. Kording, Marc-Andre Schulz, 2019, bioRxiv.

Learning function from structure in neuromorphic networks

Bratislav Misic, Guillaume Lajoie, Blake A. Richards, 2020, Nature Machine Intelligence.

Biologically feasible deep learning with segregated dendrites

Jordan Guergiuev, Blake A. Richards, Timothy Lillicrap, 2016 .

Moving beyond reward prediction errors

Blake A. Richards, B. Richards, 2019, Nat. Mach. Intell..

Dimensionality and flexibility of learning in biological recurrent neural networks

Claudia Clopath, Wolfgang Maass, Rui Ponte Costa, 2020 .

Inferring hierarchies of latent features in calcium imaging data

Blake A. Richards, Luke Y. Prince, 2019 .

Dissociating memory accessibility and precision in forgetting

Aidan J. Horner, Blake A. Richards, Sam C. Berens, 2020, Nature Human Behaviour.

Dissociating memory accessibility and precision in forgetting [Collection]

Aidan J. Horner, Blake A. Richards, Sam C. Berens, 2019 .

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Richard Naud, Alexandre Payeur, Friedemann Zenke, 2020, Nature Neuroscience.

CCN GAC Workshop: Issues with learning in biological recurrent neural networks pdf

Wolfgang Maass, Claudia Clopath, Rui Ponte Costa, 2021, ArXiv.

The overfitted brain hypothesis

Blake A. Richards, Luke Y. Prince, 2021, Patterns.

Learning function from structure in neuromorphic networks

Bratislav Misic, Guillaume Lajoie, Blake A. Richards, 2021, Nat. Mach. Intell..

Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece

Wolfgang Maass, Claudia Clopath, Rui Ponte Costa, 2021, Neurons, Behavior, Data analysis, and Theory.

PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks

Guillaume Lajoie, Blake A. Richards, Luke Y. Prince, 2021, 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER).

A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions

Joelle Pineau, Blake A. Richards, Anthony GX-Chen, 2022, AAAI.

Towards deep learning with segregated dendrites

Timothy P Lillicrap, Blake A. Richards, Blake A Richards, 2016, eLife.

The Persistence and Transience of Memory

Blake A. Richards, P. Frankland, B. Richards, 2017, Neuron.

Adversarial Feature Desensitization

Blake A. Richards, I. Rish, P. Bashivan, 2020, NeurIPS.

Memory Transformation Enhances Reinforcement Learning in Dynamic Environments

Blake A. Richards, Adam Santoro, P. Frankland, 2016, The Journal of Neuroscience.

Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training

Paul W. Frankland, Blake A. Richards, Sheena A. Josselyn, 2014, Neuron.

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

Christopher C. Pack, Blake A. Richards, Patrick J. Mineault, 2021, bioRxiv.

Cerebral white matter deficiencies in pedophilic men.

Blake A. Richards, D. Mikulis, N. Kabani, 2008, Journal of psychiatric research.

Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Blake A. Richards, Luke Y. Prince, Colleen J. Gillon, 2021, bioRxiv.

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model

Lina M. Tran, Blake A. Richards, P. Frankland, 2019, Behavioural Brain Research.

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Blake A. Richards, Richard Naud, Anisha Goel, 2021, Neuroscience.

Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy

Blake A. Richards, P. Frankland, A. Wheeler, 2014, Science.

Clonal Relationships Impact Neuronal Tuning within a Phylogenetically Ancient Vertebrate Brain Structure

Blake A. Richards, T. Lillicrap, Alistair Muldal, 2014, Current Biology.

Activation alignment: exploring the use of approximate activity gradients in multilayer networks

Thomas Mesnard, Blake A. Richards, 2018 .

Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex

Blake A. Richards, J. Kwag, M. Kohl, 2019, Science Advances.

Relevance learning via inhibitory plasticity and its implications for schizophrenia

Blake A. Richards, N. Insel, B. Richards, 2017, bioRxiv.

Parvalbumin-positive interneurons mediate neocortical-hippocampal interactions that are necessary for memory consolidation

Kaori Takehara-Nishiuchi, Blake A. Richards, Blake A Richards, 2017, eLife.

Title: Dimensionality and flexibility of learning in biological recurrent neural networks Scientific Question: Does full-rank gradient descent accurately describe the dynamics of synaptic plasticity in biological recurrent neural networks? Core Collaborators: Senior:

Blake A. Richards, C. Clopath, Franz Scherr, 2022 .

In Vivo Spike-Timing-Dependent Plasticity in the Optic Tectum of Xenopus Laevis

Colin J. Akerman, Blake A. Richards, Carlos D. Aizenman, 2010, Front. Syn. Neurosci..

Epigenetics and Language: The Minimalist Program, Connectionism and Biology

Blake A. Richards, P. A. Reich, B. Richards, 2013 .

Patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia

Blake A. Richards, Howard Chertkow, Alan C. Evans, 2009, Neurobiology of Aging.

GABAergic circuits control stimulus-instructed receptive field development in the optic tectum

Blake A. Richards, C. Akerman, Oliver P Voss, 2010, Nature Neuroscience.

Neocortical inhibitory interneuron subtypes display distinct responses to synchrony and rate of inputs

Blake A. Richards, Luke Y. Prince, J. Kwag, 2019 .

The conjunctive trace

Blake A. Richards, P. Frankland, B. Richards, 2013, Hippocampus.

COMMENTARY The Conjunctive Trace

Blake A. Richards, P. Frankland, 2013 .

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Blake A. Richards, Eric T. Shea-Brown, Guillaume Lajoie, 2022, ArXiv.

Evaluating Multimodal Interactive Agents

Tamara von Glehn, Blake A. Richards, Petko Georgiev, 2022, ArXiv.

Dissociating memory accessibility and precision in forgetting

Blake A. Richards, A. Horner, S. Berens, 2019, Nature Human Behaviour.

Cognitive Function and Brain Structure in Females With a History of Adolescent-Onset Anorexia Nervosa

Blake A. Richards, D. Mikulis, N. Kabani, 2008, Pediatrics.

For “ Dissociating loss of memory accessibility and precision ” 2

Blake A. Richards, A. Horner, S. Berens, 2019 .

I NDUCTIVE B IASES FOR R ELATIONAL T ASKS

Blake A. Richards, D. Rolnick, Y. Bengio, 2022 .

On Neural Architecture Inductive Biases for Relational Tasks

Blake A. Richards, D. Rolnick, Y. Bengio, 2022, ArXiv.

The neuroconnectionist research programme

Grace W. Lindsay, Blake A. Richards, Tim C Kietzmann, 2022, Nature Reviews Neuroscience.

Evaluating Multimodal Interactive Agents

Tamara von Glehn, Blake A. Richards, Petko Georgiev, 2022, ArXiv.

Contrastive introspection (ConSpec) to rapidly identify invariant steps for success

Blake A. Richards, Y. Bengio, Chen Sun, 2022, ArXiv.

A ternary neural code resolves error and sharpening signals

Blake A. Richards, M. Larkum, Richard Naud, 2022, bioRxiv.

1 Burst-dependent synaptic plasticity can coordinate learning in 2 hierarchical circuits 3

Blake A. Richards, F T Zenke, Jordan Guerguiev, 2020 .

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Blake A. Richards, Guillaume Lajoie, Yuhan Helena Liu, 2022, ArXiv.

Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer

Blake A. Richards, Guillaume Lajoie, Pascal Fortier-Poisson, 2022, ArXiv.

Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Blake A. Richards, S. Panzeri, Colleen J. Gillon, 2022, bioRxiv.

Contrastive introspection (ConSpec) to rapidly identify invariant prototypes for success in RL

Blake A. Richards, Y. Bengio, Chen Sun, 2022, 2210.05845.

PARALLEL INFERENCE OF HIERARCHICAL LATENT DYNAMICS IN 1 TWO-PHOTON CALCIUM IMAGING OF NEURONAL POPULATIONS

Blake A. Richards, Luke Y. Prince, Colleen J. Gillon, 2021 .

Towards a Microcircuit-Based Understanding of Multiplexed Spike Code in the Neocortex

Blake A. Richards, Matthew M Tran, 2020 .

Dendritic solutions to the credit assignment problem

Timothy P Lillicrap, Blake A. Richards, Blake A Richards, 2019, Current Opinion in Neurobiology.

Sensory-Evoked Spiking Behavior Emerges via an Experience-Dependent Plasticity Mechanism

Blake A. Richards, C. Akerman, J. V. Rheede, 2015, Neuron.

Neuronal chloride and excitability — the big impact of small changes

Blake A. Richards, M. Woodin, J. V. Raimondo, 2017, Current Opinion in Neurobiology.

Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers

Blake A. Richards, J. Kwag, M. Kohl, 2020, BMC Biology.

Visuospatial information in the retinotectal system of xenopus before correct image formation by the developing eye

Blake A. Richards, C. Akerman, Joram J. van Rheede, 2012, Developmental neurobiology.

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence pdf

Oliver Brock, Marc Toussaint, Stefan Schaal, 2021, ArXiv.

Your head is there to move you around: Goal-driven models of the primate dorsal pathway

Christopher C. Pack, Blake A. Richards, Patrick J. Mineault, 2021, bioRxiv.

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Danilo Bzdok, Joshua T. Vogelstein, Marc-Andre Schulz, 2020, Nature Communications.

Patterns across multiple memories are identified over time

Adam Santoro, Blake A. Richards, Blake A Richards, 2014, Nature Neuroscience.

Can neurogenesis act as a neural regularizer?

Lina M. Tran, Blake A. Richards, Adam Santoro, 2022, bioRxiv.

Blake A. Richards, J. Kwag, M. Kohl, 2020, BMC Biology.

Increasing CRTC1 Function in the Dentate Gyrus during Memory Formation or Reactivation Increases Memory Strength without Compromising Memory Quality

Paul W. Frankland, Blake Richards, Melanie J. Sekeres, 2012, The Journal of Neuroscience.

Bidirectional Control of Anxiety-Related Behaviors in Mice: Role of Inputs Arising from the Ventral Hippocampus to the Lateral Septum and Medial Prefrontal Cortex

Blake A. Richards, Jee Yoon Bang, G. M. Parfitt, 2017, Neuropsychopharmacology.

A deep learning framework for neuroscience

Surya Ganguli, Yoshua Bengio, Claudia Clopath, 2019, Nature Neuroscience.

Adult neurogenesis acts as a neural regularizer

Lina M. Tran, Blake A. Richards, Adam Santoro, 2022, Proceedings of the National Academy of Sciences of the United States of America.

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia

Blake A. Richards, Nathan Insel, Blake A Richards, 2018, PLoS Comput. Biol..

Forgetting Enhances Episodic Control With Structured Memories

Blake A. Richards, Annik Yalnizyan-Carson, B. Richards, 2021, bioRxiv.

Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

Blake A. Richards, Dong-Yi Lin, B. Richards, 2021, bioRxiv.

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

Blake Richards, Damjan Kalajdzievski, Dimitri M. Kullmann, 2020, bioRxiv.

Learning from unexpected events in the neocortical microcircuit

Yazan N. Billeh, Blake A. Richards, Jennifer A. Luviano, 2021, bioRxiv.

The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics

Blake A. Richards, T. Lillicrap, B. Richards, 2022, Frontiers in Computer Science.

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Contrastive introspection to identify critical steps in reinforcement learning

Blake A. Richards, Y. Bengio, Chen Sun, 2022, 2210.05845.

Investigating Power laws in Deep Representation Learning

Kumar Krishna Agrawal, Blake A. Richards, Arna Ghosh, 2022, ArXiv.

Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information

Blake A. Richards, Luke Y. Prince, J. Kwag, 2021, Communications biology.

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Blake A. Richards, Eric T. Shea-Brown, Guillaume Lajoie, 2022, NeurIPS.

Catalyzing next-generation Artificial Intelligence through NeuroAI

Eero P. Simoncelli, Konrad Paul Kording, Blake A. Richards, 2023, Nature Communications.

Towards Scaling Difference Target Propagation by Learning Backprop Targets

Blake A. Richards, Y. Bengio, I. Rish, 2022, ICML.

A double dissociation of dorsal and ventral hippocampal function on a learning and memory task mediated by the dorso‐lateral striatum

R. J. McDonald, Blake A. Richards, N. S. Hong, 2006, The European journal of neuroscience.

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Blake A. Richards, Gauthier Gidel, Guillaume Lajoie, 2023, ArXiv.

Spike-based causal inference for weight alignment

Konrad Paul Kording, Blake A. Richards, Jordan Guerguiev, 2019, ICLR.