Connection weights based on molecular mechanisms in Aplysia neuron synapses

Most views of neuro-cognitive function including the ANN analogy, assume that modification of efficacy or sensitivity of existing synapses - referred to as synaptic strength - is the brain's primary mechanism for information storage and transfer. Many factors influence biological strength, however in artificial neural networks the analogous interconnection weights represent pragmatic over-simplifications of biological synapses. This simplification has been useful and successful in order to implement ANN into integrated circuits but these neural chips are far away, in a biological sense, from representing the silicon implementation of real synapses. Based on the behavioral system of the marine snail Aplysia we show a biological neural network model where a theoretical synaptic strength value scaled from 0 to 1 results from the interplay of molecular and cellular mechanisms. Our simulation results show how synaptic weight values are related to the type of training paradigm, suggesting that real neural computation may only emerge inside a silicon chip as a consequence of a biologically inspired and more realistic definition of synaptic strength.