Principled Theoretical Frameworks for the Perception-Action Cycle
Daniel Polani · Naftali Tishby

Fri Dec 12th 07:30 AM -- 06:30 PM @ Westin: Glacier
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A significant emphasis in trying to achieve adaptation and learning in the perception-action cycle of agents lies in the development of suitable algorithms. While partly these algorithms result from mathematical constructions, in modern research much attention is given to methods that mimic biological processes. However, mimicking the apparent features of what appears to be a biologically relevant mechanism makes it difficult to separate the essentials of adaptation and learning from accidents of evolution. This is a challenge both for the understanding of biological systems as well as for the design of artificial ones. Therefore, recent work is increasingly concentrating on identifying general principles rather than individual mechanisms for biologically relevant information processing. One advantage is that a small selection of principles can give rise to a variety of - effectively equivalent - mechanisms. The ultimate goal is to attain a more transparent and unified view on the phenomena in question. Possible candidates for such principles governing the dynamics of the perception-action cycle include but are not limited to information theory, Bayesian models, energy-based concepts or group-theoretical principles. The workshops aims at bringing together various principle-based directions for the investigation of various aspects of the perception-action cycle and at identifying promising directions of work.

Author Information

Daniel Polani (University of Hertfordshire)
Naftali Tishby (The Hebrew University Jerusalem)

Naftali Tishby, is a professor of computer science and the director of the Interdisciplinary Center for Neural Computation (ICNC) at the Hebrew university of Jerusalem. He received his Ph.D. in theoretical physics from the Hebrew University and was a research staff member at MIT and Bell Labs from 1985 to 1991. He was also a visiting professor at Princeton NECI, the University of Pennsylvania and the University of California at Santa Barbara. Dr. Tishby is a leader of machine learning research and computational neuroscience. He was among the first to introduce methods from statistical physics into learning theory, and dynamical systems techniques in speech processing. His current research is at the interface between computer science, statistical physics and computational neuroscience and concerns the foundations of biological information processing and the connections between dynamics and information.

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