B. Richards

发表

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

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

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.

Learning function from structure in neuromorphic networks

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

A ternary neural code resolves error and sharpening signals

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

A deep learning framework for neuroscience

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

Catalyzing next-generation Artificial Intelligence through NeuroAI

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

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

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

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

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

Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy

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

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.

Spike-based causal inference for weight alignment

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

Investigating Power laws in Deep Representation Learning

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

Towards deep learning with segregated dendrites

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

Dissociating memory accessibility and precision in forgetting

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

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

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

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.

Can neurogenesis act as a neural regularizer?

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

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Yazan N. Billeh, Peter A. Groblewski, Jennifer A. Luviano, 2023, Scientific data.

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.

Learning from unexpected events in the neocortical microcircuit

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

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

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

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.

Dendritic solutions to the credit assignment problem

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

Relevance learning via inhibitory plasticity and its implications for schizophrenia

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

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.

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

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

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

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

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

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

The neuroconnectionist research programme

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

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

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

Neuronal chloride and excitability — the big impact of small changes

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

The Persistence and Transience of Memory

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

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.

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

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

Patterns across multiple memories are identified over time

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

Moving beyond reward prediction errors

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

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.

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..

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

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

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.

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.

Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility

Jude A Frie, H. Thorpe, J. Khokhar, 2021, eNeuro.

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

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

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

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

The conjunctive trace

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

Memory Transformation Enhances Reinforcement Learning in Dynamic Environments

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

Epigenetics and Language: The Minimalist Program, Connectionism and Biology

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

Towards Scaling Difference Target Propagation by Learning Backprop Targets

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

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

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

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.

The study of plasticity has always been about gradients

K. Kording, B. Richards, 2023, The Journal of physiology.

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

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

Forgetting Enhances Episodic Control With Structured Memories

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

Adversarial Feature Desensitization

I. Rish, P. Bashivan, Kartik Ahuja, 2020, NeurIPS.

Learning better with Dale’s Law: A Spectral Perspective

Jonathan H. Cornford, Arna Ghosh, Pingsheng Li, 2023, bioRxiv.

Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer

Guillaume Lajoie, Pascal Fortier-Poisson, Damjan Kalajdzievski, 2022, Trans. Mach. Learn. Res..

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

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

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.

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

C. Akerman, C. Aizenman, B. Richards, 2010, Front. Syn. Neurosci..

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.

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

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

Relevance Realization and the Emerging Framework in Cognitive Science

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

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

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

Contrastive introspection to identify critical steps in reinforcement learning

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

Synaptic Weight Distributions Depend on the Geometry of Plasticity

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

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

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.

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.

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

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

On Neural Architecture Inductive Biases for Relational Tasks

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

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

M. Woodin, P. Frankland, S. Kushner, 2014, Neuron.

Biologically feasible deep learning with segregated dendrites

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

Deep learning with segregated dendrites

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

Systems consolidation impairs behavioral flexibility

Blake A Richards, Sankirthana Sathiyakumar, Sofia Skromne Carrasco, 2020, Learning & memory.

Cerebral white matter deficiencies in pedophilic men.

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

Dimensionality and flexibility of learning in biological recurrent neural networks

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

I NDUCTIVE B IASES FOR R ELATIONAL T ASKS

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

Adversarial Feature Desensitization

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

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).

Adversarial Feature Desensitization pdf

Pouya Bashivan, Blake Richards, Irina Rish, 2020, ArXiv.

Fast burst fraction transients convey information independent of the firing rate

M. Larkum, Richard Naud, J. Béïque, 2024, bioRxiv.

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

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

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

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

The overfitted brain hypothesis

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