Human visual system is able to form overall statistical representation of a visual field by computing average value of several perceptual attributes such as size and speed. We proposed a neural model that computes the mean size and the mean speed of a set of similar objects. The model is a feedforward, two-dimensional neural network with three layers. Computer simulations illustrated the model’ s ability to compute the mean size independently from the visual appearance of objects. This is achieved in a fast, parallel manner without serial scanning of the visual field. The mean size is computed indirectly by comparing the total activity in the input layer and in the third layer. With the assumption that moving objects leave a trace of neural activity along the trajectory they traverse, the model is easily extended to the computation of average speed of a set of moving objects. The problem of speed estimation is reduced to the problem of size estimation of the neural trace. Therefore, both types of computations are explained using the common neural architecture. The model utilised several biophysically plausible mechanisms such as dendritic multiplication, gradient synaptic weights and lateral inhibition.

Psihologija