Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

Chance, Frances S.; Aimone, James B.; Musuvathy, Srideep S.; Smith, Michael R.; Vineyard, Craig M.; Wang, Felix

doi:10.3389/fncom.2020.00039

Title: Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

Journal Article · Wed May 06 00:00:00 EDT 2020 · Frontiers in Computational Neuroscience

DOI:https://doi.org/10.3389/fncom.2020.00039· OSTI ID:1617318

Chance, Frances S. ^[1]; Aimone, James B. ^[1]; Musuvathy, Srideep S. ^[1]; Smith, Michael R. ^[1]; Vineyard, Craig M. ^[1]; Wang, Felix ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions. While this might seem to foretell a new generation of AI approaches arising from a deeper exploration of neuroscience specifically for AI, the most direct path for achieving this is unclear. Here we discuss cultural differences between the two fields, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI. For example, the two fields feed two very different applications that at times require potentially conflicting perspectives. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Intelligence Advanced Research Projects Activity (IARPA); Defense Advanced Research Projects Agency (DARPA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1617318

Report Number(s):: SAND-2020-4146J; 685384

Journal Information:: Frontiers in Computational Neuroscience, Vol. 14, Issue 39; ISSN 1662-5188

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 7 works

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References (61)

Deep convolutional models improve predictions of macaque V1 responses to natural images Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y. PLOS Computational Biology, Vol. 15, Issue 4 https://doi.org/10.1371/journal.pcbi.1006897	journal	April 2019
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream Guclu, U.; van Gerven, M. A. J. Journal of Neuroscience, Vol. 35, Issue 27 https://doi.org/10.1523/JNEUROSCI.5023-14.2015	journal	July 2015
Navigating cognition: Spatial codes for human thinking Bellmund, Jacob L. S.; Gärdenfors, Peter; Moser, Edvard I. Science, Vol. 362, Issue 6415 https://doi.org/10.1126/science.aat6766	journal	November 2018
Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations Taube, Js; Muller, Ru; Ranck, Jb The Journal of Neuroscience, Vol. 10, Issue 2 https://doi.org/10.1523/JNEUROSCI.10-02-00436.1990	journal	February 1990
Performance-optimized hierarchical models predict neural responses in higher visual cortex Yamins, D. L. K.; Hong, H.; Cadieu, C. F. Proceedings of the National Academy of Sciences, Vol. 111, Issue 23 https://doi.org/10.1073/pnas.1403112111	journal	May 2014
Probing Deep Brain Circuitry: New Advances in in Vivo Calcium Measurement Strategies Girven, Kasey S.; Sparta, Dennis R. ACS Chemical Neuroscience, Vol. 8, Issue 2 https://doi.org/10.1021/acschemneuro.6b00307	journal	December 2016
Engineering a Less Artificial Intelligence Sinz, Fabian H.; Pitkow, Xaq; Reimer, Jacob Neuron, Vol. 103, Issue 6 https://doi.org/10.1016/j.neuron.2019.08.034	journal	September 2019
Sequencing the Connectome Zador, Anthony M.; Dubnau, Joshua; Oyibo, Hassana K. PLoS Biology, Vol. 10, Issue 10 https://doi.org/10.1371/journal.pbio.1001411	journal	October 2012
A million spiking-neuron integrated circuit with a scalable communication network and interface Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R. Science, Vol. 345, Issue 6197 https://doi.org/10.1126/science.1254642	journal	August 2014
Thalamic regulation of switching between cortical representations enables cognitive flexibility Rikhye, Rajeev V.; Gilra, Aditya; Halassa, Michael M. Nature Neuroscience, Vol. 21, Issue 12 https://doi.org/10.1038/s41593-018-0269-z	journal	November 2018
Performance of an insect-inspired target tracker in natural conditions Bagheri, Zahra M.; Wiederman, Steven D.; Cazzolato, Benjamin S. Bioinspiration & Biomimetics, Vol. 12, Issue 2 https://doi.org/10.1088/1748-3190/aa5b48	journal	February 2017
A predictive focus of gain modulation encodes target trajectories in insect vision Wiederman, Steven D.; Fabian, Joseph M.; Dunbier, James R. eLife, Vol. 6 https://doi.org/10.7554/eLife.26478	journal	July 2017
Genetic Approaches to Neural Circuits in the Mouse Josh Huang, Z.; Zeng, Hongkui Annual Review of Neuroscience, Vol. 36, Issue 1 https://doi.org/10.1146/annurev-neuro-062012-170307	journal	July 2013
Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences Ponce, Carlos R.; Xiao, Will; Schade, Peter F. Cell, Vol. 177, Issue 4 https://doi.org/10.1016/j.cell.2019.04.005	journal	May 2019
Selective Attention in an Insect Visual Neuron Wiederman, Steven D.; O’Carroll, David C. Current Biology, Vol. 23, Issue 2 https://doi.org/10.1016/j.cub.2012.11.048	journal	January 2013
An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments Bagheri, Zahra M.; Cazzolato, Benjamin S.; Grainger, Steven Journal of Neural Engineering, Vol. 14, Issue 4 https://doi.org/10.1088/1741-2552/aa776c	journal	July 2017
To build a brain Furber, Steve IEEE Spectrum, Vol. 49, Issue 8 https://doi.org/10.1109/MSPEC.2012.6247562	journal	August 2012
Neuroscience Needs Behavior: Correcting a Reductionist Bias Krakauer, John W.; Ghazanfar, Asif A.; Gomez-Marin, Alex Neuron, Vol. 93, Issue 3 https://doi.org/10.1016/j.neuron.2016.12.041	journal	February 2017
Overcoming catastrophic forgetting in neural networks Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil Proceedings of the National Academy of Sciences, Vol. 114, Issue 13 https://doi.org/10.1073/pnas.1611835114	journal	March 2017
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Silver, David; Hubert, Thomas; Schrittwieser, Julian Science, Vol. 362, Issue 6419 https://doi.org/10.1126/science.aar6404	journal	December 2018
Feature-based attention influences motion processing gain in macaque visual cortex Treue, Stefan; Trujillo, Julio C. Martínez Nature, Vol. 399, Issue 6736 https://doi.org/10.1038/21176	journal	June 1999
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex Hubel, D. H.; Wiesel, T. N. The Journal of Physiology, Vol. 160, Issue 1 https://doi.org/10.1113/jphysiol.1962.sp006837	journal	January 1962
Neuromorphic Silicon Neuron Circuits Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia Frontiers in Neuroscience, Vol. 5 https://doi.org/10.3389/fnins.2011.00073	journal	January 2011
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization Masse, Nicolas Y.; Grant, Gregory D.; Freedman, David J. Proceedings of the National Academy of Sciences, Vol. 115, Issue 44 https://doi.org/10.1073/pnas.1803839115	journal	October 2018
Inception loops discover what excites neurons most using deep predictive models Walker, Edgar Y.; Sinz, Fabian H.; Cobos, Erick Nature Neuroscience, Vol. 22, Issue 12 https://doi.org/10.1038/s41593-019-0517-x	journal	November 2019
Spatial Representation in the Entorhinal Cortex Fyhn, M. Science, Vol. 305, Issue 5688 https://doi.org/10.1126/science.1099901	journal	August 2004
Accurate Estimation of Neural Population Dynamics without Spike Sorting Trautmann, Eric M.; Stavisky, Sergey D.; Lahiri, Subhaneil Neuron, Vol. 103, Issue 2 https://doi.org/10.1016/j.neuron.2019.05.003	journal	July 2019
A Canonical Microcircuit for Neocortex Douglas, Rodney J.; Martin, Kevan A. C.; Whitteridge, David Neural Computation, Vol. 1, Issue 4 https://doi.org/10.1162/neco.1989.1.4.480	journal	December 1989
A deep learning framework for neuroscience Richards, Blake A.; Lillicrap, Timothy P.; Beaudoin, Philippe Nature Neuroscience, Vol. 22, Issue 11 https://doi.org/10.1038/s41593-019-0520-2	journal	October 2019
Visual information retrieval using synthesized imagery Thomee, Bart; Huiskes, Mark J.; Bakker, Erwin Proceedings of the 6th ACM international conference on Image and video retrieval - CIVR '07 https://doi.org/10.1145/1282280.1282303	conference	January 2007
Classification of electrophysiological and morphological neuron types in the mouse visual cortex Gouwens, Nathan W.; Sorensen, Staci A.; Berg, Jim Nature Neuroscience, Vol. 22, Issue 7 https://doi.org/10.1038/s41593-019-0417-0	journal	June 2019
Principles of connectivity among morphologically defined cell types in adult neocortex Jiang, X.; Shen, S.; Cadwell, C. R. Science, Vol. 350, Issue 6264 https://doi.org/10.1126/science.aac9462	journal	November 2015
Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis Taube, Js; Muller, Ru; Ranck, Jb The Journal of Neuroscience, Vol. 10, Issue 2 https://doi.org/10.1523/JNEUROSCI.10-02-00420.1990	journal	February 1990
Properties of neuronal facilitation that improve target tracking in natural pursuit simulations Bagheri, Zahra M.; Wiederman, Steven D.; Cazzolato, Benjamin S. Journal of The Royal Society Interface, Vol. 12, Issue 108 https://doi.org/10.1098/rsif.2015.0083	journal	July 2015
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs George, Dileep; Lehrach, Wolfgang; Kansky, Ken Science, Vol. 358, Issue 6368 https://doi.org/10.1126/science.aag2612	journal	October 2017
Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity Arleo, Angelo; Gerstner, Wulfram Biological Cybernetics, Vol. 83, Issue 3 https://doi.org/10.1007/s004220000171	journal	August 2000
Functional Imaging and Optogenetics in Drosophila Simpson, Julie H.; Looger, Loren L. Genetics, Vol. 208, Issue 4 https://doi.org/10.1534/genetics.117.300228	journal	April 2018
How advances in neural recording affect data analysis Stevenson, Ian H.; Kording, Konrad P. Nature Neuroscience, Vol. 14, Issue 2 https://doi.org/10.1038/nn.2731	journal	January 2011
A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex Hawkins, Jeff; Lewis, Marcus; Klukas, Mirko Frontiers in Neural Circuits, Vol. 12 https://doi.org/10.3389/fncir.2018.00121	journal	January 2019
A Model for the Detection of Moving Targets in Visual Clutter Inspired by Insect Physiology Wiederman, Steven D.; Shoemaker, Patrick A.; O'Carroll, David C. PLoS ONE, Vol. 3, Issue 7 https://doi.org/10.1371/journal.pone.0002784	journal	July 2008
NeuroSLAM: a brain-inspired SLAM system for 3D environments Yu, Fangwen; Shang, Jianga; Hu, Youjian Biological Cybernetics, Vol. 113, Issue 5-6 https://doi.org/10.1007/s00422-019-00806-9	journal	September 2019
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster Zheng, Zhihao; Lauritzen, J. Scott; Perlman, Eric Cell, Vol. 174, Issue 3 https://doi.org/10.1016/j.cell.2018.06.019	journal	July 2018
Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 McAdams, Carrie J.; Maunsell, John H. R. The Journal of Neuroscience, Vol. 19, Issue 1 https://doi.org/10.1523/JNEUROSCI.19-01-00431.1999	journal	January 1999
Training deep neural networks for binary communication with the Whetstone method Severa, William; Vineyard, Craig M.; Dellana, Ryan Nature Machine Intelligence, Vol. 1, Issue 2 https://doi.org/10.1038/s42256-018-0015-y	journal	January 2019
Neural population control via deep image synthesis Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J. Science, Vol. 364, Issue 6439 https://doi.org/10.1126/science.aav9436	journal	May 2019
Neuroscience-Inspired Artificial Intelligence Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher Neuron, Vol. 95, Issue 2 https://doi.org/10.1016/j.neuron.2017.06.011	journal	July 2017
The neocortical circuit: themes and variations Harris, Kenneth D.; Shepherd, Gordon M. G. Nature Neuroscience, Vol. 18, Issue 2 https://doi.org/10.1038/nn.3917	journal	January 2015
High-precision automated reconstruction of neurons with flood-filling networks Januszewski, Michał; Kornfeld, Jörgen; Li, Peter H. Nature Methods, Vol. 15, Issue 8 https://doi.org/10.1038/s41592-018-0049-4	journal	July 2018
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat O'Keefe, J.; Dostrovsky, J. Brain Research, Vol. 34, Issue 1 https://doi.org/10.1016/0006-8993(71)90358-1	journal	November 1971
Neural algorithms and computing beyond Moore's law Aimone, James B. Communications of the ACM, Vol. 62, Issue 4 https://doi.org/10.1145/3231589	journal	March 2019
A Survey on Transfer Learning Pan, Sinno Jialin; Yang, Qiang IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10 https://doi.org/10.1109/TKDE.2009.191	journal	October 2010
Toward an Integration of Deep Learning and Neuroscience Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P. Frontiers in Computational Neuroscience, Vol. 10 https://doi.org/10.3389/fncom.2016.00094	journal	September 2016
Gradient-based learning applied to document recognition Lecun, Y.; Bottou, L.; Bengio, Y. Proceedings of the IEEE, Vol. 86, Issue 11 https://doi.org/10.1109/5.726791	journal	January 1998
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior Kar, Kohitij; Kubilius, Jonas; Schmidt, Kailyn Nature Neuroscience, Vol. 22, Issue 6 https://doi.org/10.1038/s41593-019-0392-5	journal	April 2019
A Self-organizing Method for Robot Navigation based on Learned Place and Head-Direction Cells Zhou, Xiaomao; Weber, Cornelius; Wermter, Stefan 2018 International Joint Conference on Neural Networks (IJCNN) https://doi.org/10.1109/IJCNN.2018.8489348	conference	July 2018
Deep learning in spiking neural networks Tavanaei, Amirhossein; Ghodrati, Masoud; Kheradpisheh, Saeed Reza Neural Networks, Vol. 111 https://doi.org/10.1016/j.neunet.2018.12.002	journal	March 2019
Neurogenesis deep learning: Extending deep networks to accommodate new classes Draelos, Timothy J.; Miner, Nadine E.; Lamb, Christopher C. 2017 International Joint Conference on Neural Networks (IJCNN) https://doi.org/10.1109/IJCNN.2017.7965898	conference	May 2017
A Neuromorphic Approach to Path Integration: A Head-Direction Spiking Neural Network with Vision-driven Reset Kreiser, Raphaela; Cartiglia, Matteo; Martel, Julien N. P. 2018 IEEE International Symposium on Circuits and Systems (ISCAS) https://doi.org/10.1109/ISCAS.2018.8351509	conference	January 2018
Wide. Fast. Deep: Recent Advances in Multiphoton Microscopy of In Vivo Neuronal Activity Lecoq, Jérôme; Orlova, Natalia; Grewe, Benjamin F. The Journal of Neuroscience, Vol. 39, Issue 46 https://doi.org/10.1523/JNEUROSCI.1527-18.2019	journal	October 2019
Vector-based navigation using grid-like representations in artificial agents Banino, Andrea; Barry, Caswell; Uria, Benigno Nature, Vol. 557, Issue 7705 https://doi.org/10.1038/s41586-018-0102-6	journal	May 2018
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus PLoS Computational Biology, Vol. 10, Issue 11 https://doi.org/10.1371/journal.pcbi.1003915	journal	November 2014

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