Model and hardware emulation of the first synapse of the retina using Discrete-Time Cellular Neural Networks

A retinal model and its implementation on reconfigurable hardware are proposed in this paper. The model incorporates the neural circuits found in the different regions of the first synapse of the retina. The model is based on a discrete-time cellular neural network (DTCNN) approach. The implementation on reconfigurable hardware makes it possible to carry out in real time the processing tasks implied in the model execution. Like in the first synapse of the retina, it has been observed that contrast detection and detail resolution are influenced by the convergence factor of neurons and by the lateral inhibition, which are specific parameters of each neural circuit.