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Joining Dynamical Neural Fields and the Hebbian paradigm: A bio-inspired approach to knowledge representation and learning in artifical agents

Dynamical neural fields were originally proposed as a simplified mathematical model for cortical information processing in neural populations and are now used as a theoretical framework for endowing artificial agents with cognitive capacities. In this seminar we explore the Hebbian paradigm as a rule for establishing synaptic connections between fields, endowing the agents with learning abilities and therefore allowing them to deal with situations not specified in the design.
 
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