Hostname: page-component-6766d58669-nf276 Total loading time: 0 Render date: 2026-05-16T08:52:53.268Z Has data issue: false hasContentIssue false
  • English
  • Français

Realistic neurons can compute the operations needed by quantum probability theory and other vector symbolic architectures

Published online by Cambridge University Press:  14 May 2013

Terrence C. Stewart  and
Chris Eliasmith
Terrence C. Stewart
Affiliation:
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada. tcstewar@uwaterloo.ca http://ctn.uwaterloo.ca/ celiasmith@uwaterloo.ca
Chris Eliasmith
Affiliation:
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada. tcstewar@uwaterloo.ca http://ctn.uwaterloo.ca/ celiasmith@uwaterloo.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Quantum probability (QP) theory can be seen as a type of vector symbolic architecture (VSA): mental states are vectors storing structured information and manipulated using algebraic operations. Furthermore, the operations needed by QP match those in other VSAs. This allows existing biologically realistic neural models to be adapted to provide a mechanistic explanation of the cognitive phenomena described in the target article by Pothos & Busemeyer (P&B).

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 307 - 308
Copyright
Copyright © Cambridge University Press 2013 

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.)

Article purchase

Temporarily unavailable

References

Choo, F.-X. & Eliasmith, C. (2010) A spiking neuron model of serial-order recall. In: Proceedings of the 32nd Annual Conference of the Cognitive Science Society, ed. Ohlsson, S. & Cattrambone, R., pp. 2188–93. Cognitive Science Society.Google Scholar
Eliasmith, C. (2005) Cognition with neurons: A large-scale, biologically realistic model of the Wason task. In: Proceedings of the 27th Annual Meeting of the Cognitive Science Society, ed. Bara, B., Barsalou, L. & Bucciarelli, M., pp. 624–29. Cognitive Science Society.Google Scholar
Eliasmith, C. (in press) How to build a brain: A neural architecture for biological cognition. Oxford University Press.CrossRefGoogle Scholar
Eliasmith, C. & Anderson, C. H. (2003) Neural engineering: Computation, representation and dynamics in neurobiological systems. MIT Press.Google Scholar
Gayler, R. W. (2003) Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience. In: ICCS/ASCS International Conference on Cognitive Science, ed. Slezak, P., pp. 133–38. University of New South Wales.Google Scholar
Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. (1986) Neuronal population coding of movement direction. Science 233(4771):1416–19.CrossRefGoogle ScholarPubMed
Plate, T. (2003) Holographic reduced representations. CSLI Publication.Google Scholar
Stewart, T. C., Bekolay, T. & Eliasmith, C. (2011) Neural representations of compositional structures: Representing and manipulating vector spaces with spiking neurons. Connection Science 22(3):145–53.CrossRefGoogle Scholar
Stewart, T. C., Choo, F.-X. & Eliasmith, C. (2010) Dynamic behaviour of a spiking model of action selection in the basal ganglia. In: Proceedings of the 10th International Conference on Cognitive Modeling, ed. Salvucci, D. D. & Gunzelmann, G., pp. 235–40. Drexel University.Google Scholar
Stewart, T. C. & Eliasmith, C. (2011) Neural cognitive modelling: A biologically constrained spiking neuron model of the Tower of Hanoi task. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, ed. Carlson, L., Hölscher, C. & Shipley, T.F., pp. 656–61. Cognitive Science Society.Google Scholar