A model of attention-guided visual perception and recognition

A model of visual perception and recognition is described. The model contains: (i) a low-level subsystem which performs both a fovea-like transformation and detection of primary features (edges), and (ii) a high-level subsystem which includes separated 'what' (sensory memory) and 'where' (motor memory) structures. Image recognition occurs during the execution of a 'behavioral recognition program' formed during the primary viewing of the image. The recognition program contains both programmed attention window movements (stored in the motor memory) and predicted image fragments (stored in the sensory memory) for each consecutive fixation. The model shows the ability to recognize complex images (e.g. faces) invariantly with respect to shift, rotation and scale.

Neural network models of cortical functions based on the computational properties of the cerebral cortex

We describe a biologically plausible modelling framework based on the architectural and processing characteristics of the cerebral cortex. Its key feature is a multicellular processing unit (cortical column) reflecting the modular nature of cortical organization and function. In this framework, we describe a neural network model organization and function. In this framework, we describe a neural network model of the neuronal circuits of the cerebral cortex that learn different functions associated with different parts of the cortex: 1) visual integration for invariant pattern recognition, performed by a cooperation between temporal and parietal areas; 2) visual-to-motor transformation for 3D arm reaching movements, performed by parietal and motor areas; and 3) temporal integration and storage of sensorimotor programs, performed by networks linking the prefrontal cortex to associative sensory and motor areas. The architecture of the network is inspired from the features of the architecture of cortical pathways involved in these functions. We propose two rules which describe neural processing and plasticity in the network. The first rule (adaptive tuning if gating) is an analog of operant conditioning and permits to learn to anticipate an action. The second rule (adaptive timing) is based on a bistable state of activity and permits to learn temporally separate events forming a behavioral sequence.