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Jonathan Shaw

CSB 630

Department of Computer Science

University of Rochester

Rochester, NY 14627

jshaw@cs.rochester.edu

 



Since the early days of Hubel and Wiesel, researchers have been trying to understand what individual neurons signal.  Predictive coding has emerged as a general principle to explain perception, but given the uniformity of the cortex, it seems likely that the brain uses the same principle throughout.  If this is the case, then there must exist some principle for which optimal motor control and optimal perception are special cases.  While reinforcement learning could be considered such a principle, it does not make use of all of the available training signal.  Predictive coding, for example, can train purely off of the input, without regard for an external teacher, but reinforcement learning cannot.

 

My research is aimed at finding a principle that merges predictive coding and some form of optimal decision making.  However, there does not yet exist a general principle in motor control to match predictive coding.  Possibilities for this include curiosity and ability to control the environment, and I am actively exploring rigorous formulations of these ideas.

 

 

 

 

 Publications 

 

 

 

 

 

·         Predictive Coding with Temporal Invariance

Jonathan Shaw

TR859, Computer Science Dept., U. Rochester, March 2005. PDF

 

·         Predictive Action

Jonathan Shaw

TR858, Computer Science Dept., U. Rochester, March 2005. PDF

 

 

·         Temporal Sparse Coding with Spiking Neurons without Synfire Chains

Jonathan Shaw

TR834, Computer Science Dept., U. Rochester, March 2004. PDF