Volume 10 Supplement 1
Neural network realization of sensorimotor space organization using predictability and decorrelation
© Rao et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Different coding principles like stability have been successfully applied to passive sensory stimuli to capture sensory representation of neurons . It has become obvious later that the agent's behavioral repertoire has a crucial impact on the formation of the sensory representation and thus highlights the importance of the sensorimotor space. A heuristic rule-based investigation  demonstrates that optimizing the predictability in sensorimotor space of foraging agent leads to the emergence of place fields. The present work implements this principle in a biologically plausible neural-network architecture.
We use a virtual robot exploring environments with different exploration parameters. Its behavior is captured in a transition matrix, quantifying the probability to receive certain sensory signals as a consequence of previous sensory signals and the chosen behavior. Next, we implemented predictability and decorrelation in a Hebbian framework. After learning, response properties of neurons are quantified and compared to alternative adaptation scheme .
Although predictability appears mathematically complex, it is possible to implement it in a biologically plausible neural network. The performance is as good as the heuristic rule based algorithm in terms of convergence. Therefore, predictability holds the promise to be a general principle in cortical processing.
The European Commission for the SF project; contract number FP7-ICT-217148-SF.
The authors and contributors of IQR, particularly Ulysses Bernardet, UPF, Barcelona.
Joao Hespanha, for providing Matlab function for graph partitioning. http://www.ece.ucsb.edu/~hespanha.
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This article is published under license to BioMed Central Ltd.