We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear inputoutput function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitator...

The aim of this contribution is to present a flexible realtime system designed for vision rehabilitation using sensory substitution. In this version, the developed substitutive sensory system is restricted to graphical informations. The system is based on some findings in psychoacoustic obtained from actual experiments. It is based on the auditive localization of virtual sound sources. This sys...

The response of pipe flow to physically realistic, temporally and spatially continuous (periodic) forcing is investigated by decomposing the resolvent into orthogonal forcing and response pairs ranked according to their contribution to the resolvent 2-norm. Modelling the non-linear terms normally neglected by linearisation as unstructured forcing permits qualitative extrapolation of the resolve...

The general goal of this paper is to identify a transformation group that can be used to describe a class of feedback interconnections involving subsystems which are modeled solely in terms of Chen–Fliess functional expansions or Fliess operators and are independent of the existence of any state space models. This interconnection, called an output affine feedback connection, is distinguished fr...

Power control is an essential radio resource function in WCDMA systems. It is needed to compensate the nearfar effect, i.e., a strong signal overriding a weaker one at a receiver. In practical systems power control is handled so that a receiver measures the signal-to-interference ratio (SIR) and compares it to a target value. Based on this comparison, the receiver requests the transmitter to ei...

Subspace identi cation methods (SIMs) for estimating state-space models have been proven to be very useful and numerically e cient. They exist in several variants, but have one feature in common: as a rst step, a collection of high-order ARX models are estimated from vectorized inputoutput data. In order not to obtain biased estimates, this step must include future outputs. However, all but one...

The Information Bottleneck is an information theoretic framework that finds concise representations for an ‘input’ random variable that are as relevant as possible for an ‘output’ random variable. This framework has been used successfully in various supervised and unsupervised applications. However, its learning theoretic properties and justification remained unclear as it differs from standard...

Recently, we proposed a new design technique to construct high-rate convolutional codes based on a structure formed by a block encoder and a simpler convolutional encoder [1]. The search technique was based on the optimization of the output weight enumerating function of the code. Here, we prove that every (n, n − 1) convolutional code can be reduced to this structure. Following this result and...

This paper uses the term “service” for a service instance, and the term “agent” for an agent who provides or consumes a service. We consider real-life or business services (e.g., business process outsourcing, software development service involving human experts). We distinguish business from computational (e.g., Web or grid) services based on the fact that business services lack the typical inp...

Precise sensorimotor mappings between various motor, joint, sensor, and abstract physical spaces are the basis for many robotics tasks. Their cheap construction is a challenge for adaptive and learning methods. However, the practical application of many neural networks suffer from the need of large amounts of training data, which makes the learning phase a costly operation – sometimes beyond re...