Emergence of perceptual states in nonlinear lattices: A new computational model for perception

Abstract

Insects show the ability to react to certain stimuli with simple reflexes using direct sensory-motor pathways, which can be considered as basic behaviors, while high brain regions provide secondary pathway allowing the emergence of a cognitive behavior which modulates the basic abilities. Taking inspiration from this evidence, a new general purpose perceptual control architecture is briefly presented and experimentally applied to a rover navigating in a cluttered environment. The core of the architecture is constituted by the Representation layer, where different stimuli, triggering competitive reflexes, are fused to form a unique abstract picture of the environment. Each representation induces a learnable modulation of the basic behaviors in order to determine the robot overall behavior. The representation is formalized by means of Reaction-Diffusion nonlinear partial differential equations, under the paradigm of the Cellular Neural Networks (CNNs), whose dynamics converges to steady-state Turing patterns. A suitable unsupervised learning leads to the shaping of the basins of attraction of the Turing patterns that, at the end of the leaning stage, represent a particular behavior modulation. Both simulations and robot experiments are drawn to demonstrate the potentiality and the effectiveness of the approach.