Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
Pulsed neural networks
Guide to Neural Computing Applications
Guide to Neural Computing Applications
High-Level Connectionist Models
High-Level Connectionist Models
Small-World Effects in Lattice Stochastic Diffusion Search
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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One of the most pervading concepts underlying computational models of information processing in the brain is linear input integration of rate coded uni-variate information by neurons. After a suitable learning process this results in neuronal structures that statically represent knowledge as a vector of real valued synaptic weights. Although this general framework has contributed to the many successes of connectionism, in this paper we argue that for all but the most basic of cognitive processes, a more complex, multi-variate dynamic neural coding mechanism is required - knowledge should not be spacially bound to a particular neuron or group of neurons. We conclude the paper with discussion of a simple experiment that illustrates dynamic knowledge representation in a spiking neuron connectionist system.