Biomedical subjects

M de Kamps

Publications and source records attributed to M de Kamps.

3 recordsLinked to original sources

PubMed2003

A simple and stable numerical solution for the population density equation.

A population density description of large populations of neurons has generated considerable interest recently. The evolution in time of the population density is determined by a partial differential equation (PDE). Most of the algorithms proposed to solve this PDE have used finite difference schemes. Here, I use the method of characteristics to reduce the PDE to a set of ordinary differential equations, which are easy to solve. The method is applied to leaky-integrate-and-fire neurons and produces an algorithm that is efficient and yields a stable and manifestly nonnegative density. Contrary to algorithms based directly on finite difference schemes, this algorithm is insensitive to large density gradients, which may occur during evolution of the density.

Algorithms↗

PubMed2001

From knowing what to knowing where: modeling object-based attention with feedback disinhibition of activation.

We propose a neural model of visual object-based attention in which the identity of an object is used to select its location in an array of objects. The model is based on neural activity observed in visual search tasks performed by monkeys. In the model, the identity of the object (target) is selected in the higher areas of the ventral stream by means of a cue. Feedback activation from these higher areas carries information about the identity of the target to the (lower) retinotopic areas of the ventral stream. In these areas, the feedback activation interacts with feedforward activation produced by the object array. The interaction occurs in local microcircuits, and results in a selective activation on locations in the retinotopic areas of the visual stream that correspond to the location of the target in the object array. The selective activation consists of a form of gain control, produced by disinhibition. Transmitted to the dorsal stream, this activation directs spatial attention to the location of the target. In this way, an action directed at the target can be generated.

Animals↗

PubMedDate not supplied

From artificial neural networks to spiking neuron populations and back again.

In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an artificial neuron as the steady state of a cross-inhibitory circuit, in which one population codes for 'positive' artificial neuron activity and another for 'negative' activity. We will show that with this interpretation it is possible, under certain circumstances, to transform conventional ANNs (e.g. trained with 'back-propagation') into biologically plausible networks of spiking populations. However, in general, the use of biologically motivated spike response functions introduces artificial neurons that behave differently from the ones used in the classical ANN paradigm.

Action Potentials↗