Self-Organization of Local Cortical Circuits and Cortical Orientation Maps: A Nonlinear Hebbian Model of the Visual Cortex with Adaptive Lateral Couplings

In Section 2.1, we provide an introduction to multimodal action perception in humans and the underlying neural mechanisms in the brain, whereas in Section 2.2 we describe a variety

The growth rate and local largest Lyapunov exponent are compared to other often used tools to study atmospheric instabilities, namely the Eady growth rate and the potential

We pro- pose and evaluate possible extensions of the growing self-organizing networks in order to account for a set of visual tasks such as the recognition and prediction

Self-Organization, Nonlinearities, Visual Cortex, (Anti-)Hebbian Learning, Lateral Plasticity A neural network model with incremental Hebbian learning of afferent and lateral synap-

Using natural greyscale images as input patterns, realistic orientation maps develop as well and the lateral coupling profiles of the cortical neurons represent the two

To compute the belief in current hypothesis, given the neural activity of early sensory areas, the generative Bayesian model of figure 5-2 is reformulated in such a way that the

Compared to the spectrum of the linear model, for small to moderate initial energy the spectrum of the nonlinear model contains more energy in the intermediate and large

As has been previously discussed (Vidyasagar, 1999; Bullier, 2001), we suggest that there is feedback from higher area dorsal neurons, which detect an object’s presence and location,