Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-19T21:35:37.269Z Has data issue: false hasContentIssue false
  • English
  • Français

From mental representations to neural codes: A multilevel approach

Published online by Cambridge University Press:  28 November 2019

Jon Gauthier Open the ORCID record for Jon Gauthier [Opens in a new window] ,
João Loula ,
Eli Pollock Open the ORCID record for Eli Pollock [Opens in a new window] ,
Tyler Brooke Wilson Open the ORCID record for Tyler Brooke Wilson [Opens in a new window]  and
Catherine Wong Open the ORCID record for Catherine Wong [Opens in a new window]
Jon Gauthier
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139jon@gauthiers.net jloula@mit.edu  epollock@mit.edu catwong@mit.eduhttp://foldl.me https://joaoloula.github.io/ https://www.elibpollock.com/ https://web.mit.edu/zyzzyva/www/
João Loula
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139jon@gauthiers.net jloula@mit.edu  epollock@mit.edu catwong@mit.eduhttp://foldl.me https://joaoloula.github.io/ https://www.elibpollock.com/ https://web.mit.edu/zyzzyva/www/
Eli Pollock
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139jon@gauthiers.net jloula@mit.edu  epollock@mit.edu catwong@mit.eduhttp://foldl.me https://joaoloula.github.io/ https://www.elibpollock.com/ https://web.mit.edu/zyzzyva/www/
Tyler Brooke Wilson
Affiliation:
Department of Philosophy, Massachusetts Institute of Technology, Cambridge, MA02139. tbw@mit.eduhttps://sites.google.com/site/tylerbrookewilson/
Catherine Wong
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139jon@gauthiers.net jloula@mit.edu  epollock@mit.edu catwong@mit.eduhttp://foldl.me https://joaoloula.github.io/ https://www.elibpollock.com/ https://web.mit.edu/zyzzyva/www/
Get access Rights & Permissions [Opens in a new window]

Abstract

Representation and computation are the best tools we have for explaining intelligent behavior. In our program, we explore the space of representations present in the mind by constraining them to explain data at multiple levels of analysis, from behavioral patterns to neural activity. We argue that this integrated program assuages Brette's worries about the study of the neural code.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e228
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1989) A rational analysis of human memory. In: Varieties of memory and consciousness: Essays in honour of Endel Tulving, ed. Roediger, H. L. III & Craik, F. I. M., pp. 195–210. Lawrence Erlbaum Associates.Google Scholar
Chang, M. B., Ullman, T., Torralba, A., & Tenenbaum, J. B. (2017) A compositional object-based approach to learning physical dynamics. Presented at the 5th International Conference on Learning Representations (ICLR 2017), April 24–26, 2017, Toulon, France. Available at: http://hdl.handle.net/1721.1/112749.Google Scholar
Fragkiadaki, K., Agrawal, P., Levine, S. & Malik, J. (2016) Learning visual predictive models of physics for playing billiards. Presented at the International Conference on Learning Representations, San Juan, Puerto Rico. Available at: https://arxiv.org/pdf/1511.07404.pdf.Google Scholar
Frank, M. J., Seeberger, L. C. & O'Reilly, R. C. (2004) By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science 306(5703):1940–43.CrossRefGoogle ScholarPubMed
Gao, P., Trautmann, E., Yu, B., Santhanam, G., Ryu, S., Shenoy, K. & Ganguli, S. (2017) A theory of multineuronal dimensionality, dynamics and measurement. bioRxiv 214262. doi: https://doi.org/10.1101/214262.CrossRefGoogle Scholar
Gulordava, K., Bojanowski, P., Grave, E., Linzen, T. & Baroni, M. (2018) Colorless green recurrent networks dream hierarchically. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, Vol. 1, pp. 1195–1205. Association for Computational Linguistics. Available at: https://www.aclweb.org/anthology/N18-1108.Google Scholar
Hudson, D. A. & Manning, C. D. (2018) Compositional attention networks for machine reasoning. Presented at the International Conference on Learning Representations. arXiv preprint arXiv:1803.03067.Google Scholar
Jazayeri, M., & Afraz, A. (2017) Navigating the neural space in search of the neural code. Neuron 93(5):1003–14. doi:10.1016/j.neuron.2017.02.019.CrossRefGoogle ScholarPubMed
Johnson, J., Hariharan, B., van der Maaten, L., Hoffman, J., Fei-Fei, L., Lawrence Zitnick, C. & Girshick, R. (2017) Inferring and executing programs for visual reasoning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2989–2998. IEEE.CrossRefGoogle Scholar
Mastrogiuseppe, F. & Ostojic, S. (2018) Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron 99(3):609–23.e29.CrossRefGoogle ScholarPubMed
McClelland, J. L. & Rogers, T. T. (2003) The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience 4(4):310.CrossRefGoogle ScholarPubMed
Młynarski, W. & McDermott, J. H. (2018) Learning midlevel auditory codes from natural sound statistics. Neural Computation 30(3):631–69.CrossRefGoogle ScholarPubMed
Montague, P. R., Dayan, P. & Sejnowski, T. J. (1996) A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience 16(5):1936–47.CrossRefGoogle ScholarPubMed
Niv, Y. (2009) Reinforcement learning in the brain. Journal of Mathematical Psychology 53(3):139–54.CrossRefGoogle Scholar
Rajalingham, R., Issa, E. B., Bashivan, P., Kar, K., Schmidt, K. & DiCarlo, J. J. (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. Journal of Neuroscience 38(33):7255–69.CrossRefGoogle ScholarPubMed
Saxena, S. & Cunningham, J. P. (2019) Towards the neural population doctrine. Current Opinion in Neurobiology 55:103–11. doi:10.1016/j.conb.2019.02.002.CrossRefGoogle ScholarPubMed
Schultz, W., Dayan, P. & Montague, P. R. (1997) A neural substrate of prediction and reward. Science 275(5306):1593–9.CrossRefGoogle ScholarPubMed
Smolensky, P. & Legendre, G. (2006) The harmonic mind: From neural computation to optimality-theoretic grammar: Vol. 1. Cognitive architecture. MIT Press.Google Scholar
Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K. & Janak, P. H. (2013) A causal link between prediction errors, dopamine neurons and learning. Nature Neuroscience 16(7):966–73.CrossRefGoogle Scholar
Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P. & Tenenbaum, J. (2018) Neural-symbolic VQA: Disentangling reasoning from vision and language understanding. In: Advances in Neural Information Processing Systems, Volume 31, ed. Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R., pp. 1031–42. Neural Information Processing Systems Foundation.Google Scholar
Eliasmith, C. & Anderson, C. H. (2004) Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press.Google Scholar