CONNECTION WEIGHTS BASED ON MOLECULAR MECHANISMS IN APLYSIA NEURON SYNAPSES

Most views of neuro-cognitive function including the ANN analogy, assu me that modification of efficacy or sensitivity of existing synapses - referred to as synaptic strength - is the brain's primary mechanism f or information storage and transfer. Many factors influence biological strength, however in artificial neural networks the analogous interco nnection weights represent pragmatic over-simplifications of biologica l synapses. This simplification has been useful and successful in orde r to implement ANN into integrated circuits but these neural chips are far away, in a biological sense, from representing the silicon implem entation of real synapses. Based on the behavioral system of the marin e snail Aplysia we show a biological neural network model where a theo retical synaptic strength value scaled from 0 to 1 results from the in terplay of molecular and cellular mechanisms. Our simulation results s how how synaptic weight values are related to the type of training par adigm, suggesting that real neural computation may only emerge inside a silicon chip as a consequence of a biologically inspired and more re alistic definition of synaptic strength.