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

  • Daniel Rasmussen (a1) and Chris Eliasmith (a1)

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

Copyright

References

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Brown, R. G. & Hwang, P. Y. C. (1992) Introduction to random signals and applied Kalman filtering, 2nd edition. Wiley.
Craik, K. (1943) The nature of explanation. Cambridge University Press.
Eliasmith, C. (in press) How to build a brain: A neural architecture for biological cognition. Oxford University Press.
Eliasmith, C. & Anderson, C. (2003) Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press.
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.
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.
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.
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.
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.
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.

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