Using goal-driven deep learning models to understand sensory cortex.

Artículos que citan Using goal-driven deep learning models to understand sensory cortex.

Título / Autor	Año
Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of The Royal Society Interface 15 (141), 20170387	2018
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258	2017
The unreasonable effectiveness of deep features as a perceptual metric R Zhang, P Isola, AA Efros, E Shechtman, O Wang Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Toward an integration of deep learning and neuroscience AH Marblestone, G Wayne, KP Kording Frontiers in computational neuroscience 10, 94	2016
What learning systems do intelligent agents need? Complementary learning systems theory updated D Kumaran, D Hassabis, JL McClelland Trends in cognitive sciences 20 (7), 512-534	2016
Multiagent cooperation and competition with deep reinforcement learning A Tampuu, T Matiisen, D Kodelja, I Kuzovkin, K Korjus, J Aru, J Aru, ... PloS one 12 (4), e0172395	2017
Five factors that guide attention in visual search JM Wolfe, TS Horowitz Nature Human Behaviour 1 (3), 0058	2017
Towards deep learning with segregated dendrites J Guerguiev, TP Lillicrap, BA Richards ELife 6, e22901	2017
Neural encoding and decoding with deep learning for dynamic natural vision H Wen, J Shi, Y Zhang, KH Lu, J Cao, Z Liu Cerebral Cortex 28 (12), 4136-4160	2017
Inference in the age of big data: Future perspectives on neuroscience D Bzdok, BTT Yeo Neuroimage 155, 549-564	2017
Computational approaches to fMRI analysis JD Cohen, N Daw, B Engelhardt, U Hasson, K Li, Y Niv, KA Norman, ... Nature neuroscience 20 (3), 304	2017
A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy AJE Kell, DLK Yamins, EN Shook, SV Norman-Haignere, JH McDermott Neuron 98 (3), 630-644. e16	2018
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks R Rajalingham, EB Issa, P Bashivan, K Kar, K Schmidt, JJ DiCarlo Journal of Neuroscience 38 (33), 7255-7269	2018
Making sense of real-world scenes GL Malcolm, IIA Groen, CI Baker Trends in Cognitive Sciences 20 (11), 843-856	2016
Contributions of low-and high-level properties to neural processing of visual scenes in the human brain IIA Groen, EH Silson, CI Baker Philosophical Transactions of the Royal Society B: Biological Sciences 372 …	2017
An explainable deep machine vision framework for plant stress phenotyping S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ... Proceedings of the National Academy of Sciences 115 (18), 4613-4618	2018
Control of synaptic plasticity in deep cortical networks PR Roelfsema, A Holtmaat Nature Reviews Neuroscience 19 (3), 166	2018
Mechanosensation and adaptive motor control in insects JC Tuthill, RI Wilson Current Biology 26 (20), R1022-R1038	2016
Classical statistics and statistical learning in imaging neuroscience D Bzdok Frontiers in neuroscience 11, 543	2017
The functional neuroanatomy of human face perception K Grill-Spector, KS Weiner, K Kay, J Gomez Annual Reviews	2017

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