Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T06:03:11.316Z Has data issue: false hasContentIssue false

God, the devil, and the details: Fleshing out the predictive processing framework

Published online by Cambridge University Press:  10 May 2013

Daniel Rasmussen
Affiliation:
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada. [email protected]@uwaterloo.ca
Chris Eliasmith
Affiliation:
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada. [email protected]@uwaterloo.ca

Abstract

The predictive processing framework lacks many of the architectural and implementational details needed to fully investigate or evaluate the ideas it presents. One way to begin to fill in these details is by turning to standard control-theoretic descriptions of these types of systems (e.g., Kalman filters), and by building complex, unified computational models in biologically realistic neural simulations.

God is in the details

— Mies van der Rohe

The devil is in the details

— Anonymous

Type
Open Peer Commentary
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.)

References

Brown, R. G. & Hwang, P. Y. C. (1992) Introduction to random signals and applied Kalman filtering, 2nd edition. Wiley.Google Scholar
Craik, K. (1943) The nature of explanation. Cambridge University Press.Google Scholar
Eliasmith, C. (in press) How to build a brain: A neural architecture for biological cognition. Oxford University Press.Google Scholar
Eliasmith, C. & Anderson, C. (2003) Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press.Google Scholar
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y. & Rasmussen, D. (2012) A large-scale model of the functioning brain. Science 338(6111):1202–205.Google Scholar
Kalman, R. E. (1960) A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering (Series D) 82:3545.Google Scholar
Townsend, B. R., Paninski, L. & Lemon, R. N. (2006) Linear encoding of muscle activity in primary motor cortex and cerebellum. Journal of Neurophysiology 96(5): 2578–92.Google Scholar
Tudusciuc, O. & Nieder, A. (2009) Contributions of primate prefrontal and posterior parietal cortices to length and numerosity representation. Journal of Neurophysiology 101(6):2984–94.Google Scholar
Villalon-Turrubiates, I. E., Andrade-Lucio, J. A. & Ibarra-Manzano, O. G. (2004) Multidimensional digital signal estimation using Kalman's theory for computer-aided applications. In: Proceedings of the International Conference on Computing, Communications, and Control Technologies, Austin, Texas, August 14–17, 2004 (CCCT Proceedings, Vol. 7), ed. Chu, H.-W., pp. 4853. University of Texas Press.Google Scholar
Wu, Z. (1985) Multidimensional state space model Kalman filtering with applications to image restoration. IEEE Transactions on Acoustics, Speech, and Signal Processing 33:1576–92.Google Scholar