Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-25T20:18:28.372Z Has data issue: false hasContentIssue false
  • English
  • Français

Codes, functions, and causes: A critique of Brette's conceptual analysis of coding

Published online by Cambridge University Press:  28 November 2019

David Barack Open the ORCID record for David Barack [Opens in a new window]  and
Andrew Jaegle
David Barack
Affiliation:
Jerome L. Greene Science Center, Columbia University, New York, NY10027; DeepMind, LondonN1C 4AG, United Kingdom. dbarack@gmail.comdrewjaegle@google.comwww.deepmind.com
Andrew Jaegle
Affiliation:
Jerome L. Greene Science Center, Columbia University, New York, NY10027; DeepMind, LondonN1C 4AG, United Kingdom. dbarack@gmail.comdrewjaegle@google.comwww.deepmind.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Brette argues that coding as a concept is inappropriate for explanations of neurocognitive phenomena. Here, we argue that Brette's conceptual analysis mischaracterizes the structure of causal claims in coding and other forms of analysis-by-decomposition. We argue that analyses of this form are permissible and conceptually coherent and offer essential tools for building and developing models of neurocognitive systems like the brain.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e219
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

Cummins, R. (1975) Functional analysis. Journal of Philosophy 72(20):741–65.CrossRefGoogle Scholar
Dennett, D. C. (1981) Brainstorms: Philosophical essays on mind and psychology: MIT Press.CrossRefGoogle Scholar
Funahashi, K.-i. & Nakamura, Y. (1993). Approximation of dynamical systems by continuous time recurrent neural networks. Neural Networks 6(6):801–06.CrossRefGoogle Scholar
Geman, S., & Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6(6):721–41. doi:10.1109/TPAMI.1984.4767596.CrossRefGoogle ScholarPubMed
Graves, A. (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850 [cs.NE].Google Scholar
Heess, N., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Ali Eslami, S. M., Riedmiller, M. J. & Silver, D. (2017) Emergence of locomotion behaviours in rich environments. arXiv:1707.02286 [cs.AI].Google Scholar
Levine, S., Finn, C., Darrell, T. & Abbeel, P. (2016) End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research 17(1):1334–73.Google Scholar
Lycan, W. G. (1981) Form, function, and feel. The Journal of Philosophy 78(1):24–50.CrossRefGoogle Scholar
Marr, D. (1982a) Vision. Henry Holt.Google Scholar
Oppenheim, A. V. & Schafer, R. W. (2013) Discrete-time signal processing (3rd edition). Pearson.Google Scholar
Rice, C. (2015) Moving beyond causes: Optimality models and scientific explanation. Noûs 49(3):589–615.CrossRefGoogle Scholar
Roth, S. & Black, M. J. (2005) Fields of experts: A framework for learning image priors. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 2:860–7. IEEE.Google Scholar
Ryle, G. (1949) The concept of mind. University of Chicago Press.Google Scholar
Santoro, A., Hill, F., Barrett, D., Raposo, D., Botvinick, M. & Lillicrap, T. (2019) Is coding a relevant metaphor for building AI? arXiv:1904.10396 [q-bio.NC].Google Scholar
Schäfer, A. M. & Zimmermann, H. G. (2007) Recurrent neural networks are universal approximators. In: Artificial Neural Networks – ICANN 2006, ed. Kollias, S. D., Stafylopatis, A., Duch, W. & Oja, E.. Lecture Notes in Computer Science, 4131.Google Scholar
Shannon, C. E. & Weaver, W. (1963) The mathematical theory of communication. University of Illinois Press.Google Scholar
Srivastava, N., Mansimov, E. & Salakhutdinov, R. (2015) Unsupervised learning of video representations using LSTMs. Proceedings of Machine Learning Research 37:843–52.Google Scholar
van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. & Kavukcuoglu, K. J. S. (2016) WaveNet: A generative model for raw audio. arXiv:1609.03499n[cs.SD].Google Scholar
van den Oord, A., Kalchbrenner, N. & Kavukcuoglu, K. (2016) Pixel recurrent neural networks. Proceedings of Machine Learning Research 48:1727–36.Google Scholar
Walsh, D. M. & Ariew, A. (1996) A taxonomy of functions. Canadian Journal of Philosophy 26(4):493–514.CrossRefGoogle Scholar