Computing with dynamic synapses: a case study of speech recognition

A novel concept of dynamic synapse is presented which incorporates fundamental features of biological neurons including presynaptic mechanisms influencing the probability of neurotransmitter release from an axon terminal. The consequence of the presynaptic mechanisms is that the probability of release becomes a function of the temporal pattern of action potential occurrence, and hence, the strength of a given synapse varies upon the arrival of each action potential invading the terminal region. From the perspective of neural information processing, the capability of dynamically tuning the synaptic strength as a function of the level of neuronal activation gives rise to a significant representational and processing power at the synaptic level. Furthermore, there is an exponential growth in such computational power when the specific dynamics of presynaptic mechanisms varies quantitatively across axon terminals of a single neuron. A dynamic learning algorithm is developed in which alterations of the presynaptic mechanisms lead to different pattern transformation functions while changes in the postsynaptic mechanisms determines how the synaptic signals are to be combined. We demonstrate the computational capability of dynamic synapses by performing speech recognition from unprocessed, noisy raw waveforms of words spoken by multiple speakers with a simple neural network consisting of a small number of neurons connected with synapses incorporating dynamically determined probability of release