Spatiotemporal neural network using axodendritic chemical synapse model

An artificial neural network which can process spatiotemporal signals is developed. In the proposed time-varying neural network, in addition to the weights as used in the classical artificial neural network, the encoding of timing information in the input signal is also considered. The proposed processing unit uses (a) weights as a lumped parameter of synaptic efficacy and attenuation of the postsynaptic potential due to propagation through the dendrite; (b) time delay between the arrival of action potential at the presynaptic site and the generation of action potential at the axon hillock; and (c) decay rate of the postsynaptic potential generated by the previous inputs. These properties are derived from the axodendritic chemical synapse model. Computer simulation of the proposed neural network shows that this network is sensitive to the phase dependencies in the input signals and that it can detect specific sequences in the spatiotemporal input signals