Community Research and Development Information Service - CORDIS

Computational modelling of spiking neural networks based on biological principles

Based on biological principles, Spiking Neural Network Models were proposed to solve problems in artificial intelligent systems. A reliable learning algorithm was obtained to solve function approximate, classification and time-series problems. Based on Spike Time Dependant Plasticity (STDP) learning rules, a Spiking Neural Network Model was proposed to learn arbitrary n-dimensional co-ordinate transformations based on multi-sensory observation of environmental interactions, for example the 2D transformation from an angular representation of arm position to a Cartesian representation.

The network is robust and provides noise immunity as even if some of the neurons do not work, the network can still perform the transformation function. The model can provide a biologically plausible approach for designing artificial intelligent systems.

Based on spiking neuron model and different receptive field models, hierarchical networks of spiking neurons are proposed to process visual stimuli in which multiple objects are represented by groupings of elementary bar elements with different orientation distributions. Simulations show that hierarchical networks of spiking neurons are able to segment the objects and bind the pixels to form shapes of objects by neighbour lateral connections and temporal correlation when they are implemented in biologically realistic networks of spiking neurons.

Reported by

University of Ulster
Intelligent Systems Engineering Laboratory, Magee College, University of Ulster, Nortland Rd.
BT48 7JL Derry
United Kingdom
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