The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs

TL;DR

The receptron is a nonlinear threshold logic gate with input-dependent weights that enhances classification and selectivity for analog inputs, potentially reducing the need for complex training in high-dimensional applications.

Contribution

We introduce the receptron, a nonlinear threshold logic model with input-dependent weights, demonstrating intrinsic selectivity and improved classification for analog inputs in multidimensional spaces.

Findings

Receptron exhibits intrinsic selective activation for analog inputs within cubic domains.

Receptron can be extended to n-dimensional spaces for complex applications.

Receptron-based networks can handle many analog inputs with high selectivity without complex training.

Abstract

Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of classification performances even with the use of a single unit. Here we formally demonstrate that a receptron, characterized by nonlinear input-dependent weight functions, exhibit intrinsic selective activation properties for analog inputs, when the input vector is within cubic domains in a 3D space. The proposed model can be extended to the n-dimensional case for multidimensional applications. Our results suggest that receptron-based networks can represent a new class of devices capable to manage a large number of analog inputs, for edge applications requiring high selectivity and classification capabilities without the burden of complex training.