Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-13T19:42:19.010Z Has data issue: false hasContentIssue false
  • English
  • Français

Polychrony and the Process View of Computation

Published online by Cambridge University Press:  01 January 2022

Colin Klein
Get access Rights & Permissions [Opens in a new window]

Abstract

Some realistic models of neural spiking take into account spike timing, yet the practical relevance of spike timing is often unclear. In Eugene Izhikevich’s model, timing plays a crucial role by allowing for the natural formation of polychronous circuits. In such circuits, individual elements may figure in a number of distinct assemblies, their role in each determined by their timing relative to other neurons. I show that this reflects a distinct organizational principle from notions of pluripotency, redundancy, or reuse and argue that properly understanding this phenomenon requires a shift to a time-sensitive, process-based view of computation.

Type
Cognitive Sciences
Information
Philosophy of Science , Volume 87 , Issue 5: PSA 2018 - Proceedings of the 2018 biennial meeting of the Philosophy of Science Association. Part II Symposia Papers , December 2020 , pp. 1140 - 1149
Copyright
Copyright © The Philosophy of Science Association

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.)

Footnotes

†

This work was supported by Australian Research Council grant FT140100422. Thanks to Felipe de Brigard and two anonymous reviewers for helpful feedback on previous drafts.

References

Aaronson, S. 2015. “Why Philosophers Should Care about Computational Complexity.” In Computability: Gödel, Turing, Church, and Beyond, ed. Copeland, B. J., Posy, C., and Shagrir, O., 261–327. Cambridge, MA: MIT Press.Google Scholar
Anderson, M. 2010. “Neural Reuse: A Fundamental Organizational Principle of the Brain.” Behavioral and Brain Sciences 33 (4): 245–66.CrossRefGoogle ScholarPubMed
Anderson, M.. 2014. After Phrenology: Neural Reuse and the Interactive Brain. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Barack, D. L. 2019. “Cognitive Recycling.” British Journal for the Philosophy of Science 70 (1): 239–68.CrossRefGoogle Scholar
Bellman, R. E. 1961. Adaptive Control Processes: A Guided Tour. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Burnston, D. C. 2016. “A Contextualist Approach to Functional Localization in the Brain.” Biology and Philosophy 31 (4): 527–50.CrossRefGoogle Scholar
Calcott, B., Balcan, D., Hohenlohe, P. A., and Califano, A.. 2008. “A Publish-Subscribe Model of Genetic Networks.” PloS One 3 (9): e3245.CrossRefGoogle ScholarPubMed
Danks, D., and Plis, S.. 2019. “Amalgamating Evidence of Dynamics.” Synthese 196 (8): 3213–30.CrossRefGoogle ScholarPubMed
De Brigard, F., and Gessell, B.. 2016. “Time Is Not of the Essence: Understanding the Neural Correlates of Mental Time Travel.” In Seeing the Future: Theoretical Perspectives on Future-Oriented Mental Time Travel, ed. Michaelian, K. and Klein, S. B., 153–80. Oxford: Oxford University Press.Google Scholar
Dennett, D. 1991. Consciousness Explained. Boston: Little, Brown.Google Scholar
Doberstein, D. 2011. Fundamentals of GPS Receivers: A Hardware Approach. New York: Springer.Google Scholar
Faries, F., and Chemero, A.. 2019. “Dynamic Information Processing.” In The Routledge Handbook of the Computational Mind, ed. Sprevak, M. and Colombo, M., 134–48. London: Routledge.Google Scholar
Galison, P. 2004. Einstein’s Clocks, Poincaré’s Maps: Empires of Time. New York: Norton.Google Scholar
Izhikevich, E. M. 2006. “Polychronization: Computation with Spikes.” Neural Computation 18 (2): 245–82.CrossRefGoogle ScholarPubMed
Izhikevich, E. M., Gally, J. A., and Edelman, G. M.. 2004. “Spike-Timing Dynamics of Neuronal Groups.” Cerebral Cortex 14 (8): 933–44.CrossRefGoogle ScholarPubMed
Jékely, G., Keijzer, F., and Godfrey-Smith, P.. 2015. “An Option Space for Early Neural Evolution.” Philosophical Transactions of the Royal Society B 370 (1684): 20150181.CrossRefGoogle ScholarPubMed
Klein, C. 2012. “Two Paradigms for Individuating Implementations.” Journal of Cognitive Science 13 (2): 167–79.Google Scholar
Klein, C.. 2018. “Mechanisms, Resources, and Background Conditions.” Biology and Philosophy 33 (36): 1–14.CrossRefGoogle Scholar
Price, C. J., and Friston, K. J.. 2005. “Functional Ontologies for Cognition: The Systematic Definition of Structure and Function.” Cognitive Neuropsychology 22 (3): 262–75.CrossRefGoogle ScholarPubMed
Putnam, H. 1967/1991. “The Nature of Mental States.” In The Nature of Mind, ed. Rosenthal, D. M., 197–210. New York: Oxford University Press.Google Scholar
Rathkopf, C. A. 2013. “Localization and Intrinsic Function.” Philosophy of Science 80 (1): 1–21.CrossRefGoogle Scholar
Smith, B. C. 1996. On the Origin of Objects. Cambridge, MA: MIT Press.Google Scholar
Sterling, P., and Laughlin, S.. 2015. Principles of Neural Design. Cambridge, MA: MIT Press.Google Scholar