Bio-inspired Neuron Model for Motion Detection on Base Signal Accumulation

Abstract

The article presents a new model of the MT neuron (neuron of the middle temporal region), which allows motion detecting and determining its direction and speed without the use of recurrent communication. The model is based on signal accumulation and is organized using a space-time vector that sets the weighting coefficients. The space-time vector is formed using the product of the Gaussian, which defines the spatial component, and the "Mexican hat" wavelet, which sets the time vector of the change in the receptive field. This configuration allows not only to motion detect, but also to make the model not sensitive to uniform or textural fill. The model is presented in variants for determining linear and rotational motion. Motion, in this case, is the sequential activation of several edge selection neurons located in the same direction in a certain neighborhood over time i.e. with a change of frame. To assess the motion, the models were tested on the MPI Sintel dataset. The model developed by us shows results better than Spatio-Temporal Gabor. The best accuracy of determining the direction of movement can be obtained with the size of the space-time vector (7*7, 7).