Self-organization in a parametrically coupled logistic map network: a model for information processing in the visual cortex

Authors:
Ramin Pashaie;Nabil H. Farhat
Affiliations:
Bioengineering Department, Stanford University, Stanford, CA;Electrical and System Engineering Department and the Mahoney Institute of Neurological Sciences, University of Pennsylvania, Philadelphia, PA
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 9
Cited 1

The cortical column: a new processing unit for multilayered networks

Neural Networks
What does the retina know about natural scenes?

Neural Computation
What is the goal of sensory coding?

Neural Computation
The bifurcating neuron network 1

Neural Networks
Sparse Distributed Memory

Sparse Distributed Memory
Biomorphic Dynamical Networks for Cognition and Control

Journal of Intelligent and Robotic Systems
The bifurcating neuron network 2: an analog associative memory

Neural Networks
Learning innate face preferences

Neural Computation
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex

Neural Computation

Decoding stimulus-reward pairing from local field potentials recorded from monkey visual cortex

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new model seeking to emulate the way the visual cortex processes information and interacts with subcortical areas to produce higher level brain functions is described. We developed a macroscopic approach that incorporates salient attributes of the cortex based on combining tools of nonlinear dynamics, information theory, and the known organizational and anatomical features of cortex. Justifications for this approach and demonstration of its effectiveness are presented. We also demonstrate certain capabilities of this model in producing efficient sparse representations and providing the cortical computational maps.