Contextual learning in the neurosolver

Authors:
Andrzej Bieszczad;Kasia Bieszczad
Affiliations:
Computer Science, California State University Channel Islands, Camarillo, CA;Center for the Neurobiology of Learning and Memory, U.C. Irvine 320 Qureshey Research Laboratory, University of California, Irvine, CA
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, we introduce an enhancement to the Neurosolver, a neuromorphic planner and a problem solving system. The enhanced architecture enables contextual learning. The Neurosolver was designed and tested on several problem solving and planning tasks such as re-arranging blocks and controlling a software-simulated artificial rat running in a maze. In these tasks, the Neurosolver learned temporal patterns independent of the context. However in the real world no skill is acquired in vacuum; Contextual cues are a part of every situation, and the brain can incorporate such stimuli as evidenced through experiments with live rats. Rats use cues from the environment to navigate inside mazes. The enhanced architecture of the Neurosolver accommodates similar learning.