Grace W Lindsay
Grace W Lindsay
Assistant Professor, New York University
Verified email at - Homepage
Cited by
Cited by
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
Convolutional neural networks as a model of the visual system: Past, present, and future
GW Lindsay
Journal of cognitive neuroscience 33 (10), 2017-2031, 2021
Parallel processing by cortical inhibition enables context-dependent behavior
KV Kuchibhotla, JV Gill, GW Lindsay, ES Papadoyannis, RE Field, ...
Nature Neuroscience 20 (1), 62-71, 2017
Attention in psychology, neuroscience, and machine learning
GW Lindsay
Frontiers in computational neuroscience 14, 516985, 2020
How biological attention mechanisms improve task performance in a large-scale visual system model
GW Lindsay, KD Miller
eLife 7, e38105, 2018
The neuroconnectionist research programme
A Doerig, RP Sommers, K Seeliger, B Richards, J Ismael, GW Lindsay, ...
Nature Reviews Neuroscience 24 (7), 431-450, 2023
Consciousness in artificial intelligence: insights from the science of consciousness
P Butlin, R Long, E Elmoznino, Y Bengio, J Birch, A Constant, G Deane, ...
arXiv preprint arXiv:2308.08708, 2023
Hebbian learning in a random network captures selectivity properties of the prefrontal cortex
GW Lindsay, M Rigotti, MR Warden, EK Miller, S Fusi
Journal of Neuroscience 37 (45), 11021-11036, 2017
Models of the mind: how physics, engineering and mathematics have shaped our understanding of the brain
G Lindsay
Bloomsbury Publishing, 2021
Neuromatch Academy: Teaching computational neuroscience with global accessibility
T van Viegen, A Akrami, K Bonnen, E DeWitt, A Hyafil, H Ledmyr, ...
Trends in cognitive sciences 25 (7), 535-538, 2021
Feature Based Attention in Convolutional Neural Networks
GW Lindsay
arXiv, 2015
A unified circuit model of attention: neural and behavioral effects
GW Lindsay, DB Rubin, KD Miller
bioRxiv, 2019.12. 13.875534, 2019
Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do
GW Lindsay, TD Mrsic-Flogel, M Sahani
bioRxiv, 2022.03. 07.483196, 2022
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
GW Lindsay, J Merel, T Mrsic-Flogel, M Sahani
arXiv preprint arXiv:2112.02027, 2021
Testing the tools of systems neuroscience on artificial neural networks
GW Lindsay
arXiv preprint arXiv:2202.07035, 2022
Recent advances at the interface of Neuroscience and Artificial neural networks
Y Cohen, TA Engel, C Langdon, GW Lindsay, T Ott, MAK Peters, ...
Journal of Neuroscience 42 (45), 8514-8523, 2022
Testing methods of neural systems understanding
GW Lindsay, D Bau
Cognitive Systems Research 82, 101156, 2023
Corrigendum: Attention in psychology, neuroscience, and machine learning
GW Lindsay
Frontiers in Computational Neuroscience 15, 698574, 2021
Understanding the functional and structural differences across excitatory and inhibitory neurons
S Minni, L Ji-An, T Moskovitz, G Lindsay, K Miller, M Dipoppa, GR Yang
bioRxiv, 680439, 2019
Deep Convolutional Neural Networks as Models of the Visual System: Q&A. Neurdiness-Thinking about brains
G Lindsay
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