Abstract: Systems and methods for configuring and training neural networks for visual processing tasks, specifically focusing on higher-order feature selectivity with techniques to preconfigure higher-order features into convolutional neural networks (CNNs). The method involves configuring an artificial neural network to be selective to contours comprising curved sections and straight or nearly straight sections. This includes creating a topographically organized layer of orientation-selective neurons that collectively detect multiple orientations in an image patch. Additionally, layers of neurons selective for curve segments and approximately straight contours are created, where the selection is based on inputs from previous layers. The method further extends to creating neurons selective for curves with specified curvature, orientation, and center.

Abstract: Certain aspects of the present disclosure provide systems and methods for configuring and training neural networks. The method includes models of individual neurons in a network that avoid certain biologically impossible or implausible features of conventional artificial neural networks. Exemplary networks may use patterns of local connections between excitatory and inhibitory neurons to provide desirable computational properties. A network configured in this manner is shown to solve a digit classification problem.

Abstract: Certain aspects of the present disclosure provide systems and methods for configuring and training neural networks. The method includes models of individual neurons in a network that avoid certain biologically impossible or implausible features of conventional artificial neural networks. Exemplary networks may use patterns of local connections between excitatory and inhibitory neurons to provide desirable computational properties. A network configured in this manner is shown to solve a digit classification problem.

Abstract: Certain aspects of the present disclosure provide systems and methods for configuring and training neural networks. The method includes models of individual neurons in a network that avoid certain biologically impossible or implausible features of conventional artificial neural networks. Exemplary networks may use patterns of local connections between excitatory and inhibitory neurons to provide desirable computational properties. A network configured in this manner is shown to solve a digit classification problem.