Gradient computation in time delayed recurrent neural network with memory for static input-output mapping under stability constraint

摘要

The complex retinal neural layer in the human visual system is considered to perform certain early visual processing. The authors examine how an array of such complex neurons and their associated neural circuitry could be used for static input-output mapping without losing stability. The advantage of using the proposed complex retinal type neural model is that it has a spatio-temporal structure whose transient and steady-state responses provide adequate information for use in image recognition systems. In order to utilize these networks' full capability, it is necessary to adapt the network parameters by computing the gradients. A suitable method to obtain gradients for parameter adjustment is given. A constraint satisfaction approach is developed with observations concerning stability criteria for these networks.