Although rewards are physical stimuli and objects, their value for survival and reproduction is subjective. The phasic, neurophysiological and voltammetric dopamine reward prediction error response signals subjective reward value. The signal incorporates crucial reward aspects such as amount, probability, type, risk, delay and effort. Differences of dopamine release dynamics with temporal delay...

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron fun...

Speech technology is one of the key technical issues involved in Information Technology as it constitutes an important aspect of Human Computer Interaction. Prediction of speech signal has applications in speech technology, especially in coding. Conventionally linear prediction is used. However, non-linear phenomena exist in speech production. Therefore, considering this non-linearity should le...

We propose a general hypothesis that integrates affective and cognitive processing with neuroanatomy to explain anxiety pronenes. The premise is that individuals who are prone to anxiety show an altered interoceptive prediction signal, i.e., manifest augmented detection of the difference between the observed and expected body state. As a consequence, the increased prediction signal of a prospec...

Prediction occurs in many biological nervous systems e.g. in the cortex [7]. We introduce a method of adapting the recurrent layer dynamics of an echo-state network (ESN) without attempting to train the weights directly. Initially a network is generated that fulfils the echo state liquid state condition. A second network is then trained to predict the next internal state of the system. In simul...

The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG) signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1)th sample from n previous samples To train and adjust the networ...

This paper presents a PDF-matched modification to Stone’s measure of predictability. The modified measure of predictability is a measure of non-gaussianity too. It is an extent of signal predictability by two different prediction terms. One prediction term is based on a normal gaussian PDF assumption for signal. In contrast, the other one is based on a unit variance supergaussian PDF assumption...

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means-electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. ABSTRACT Linear prediction theory has had a profound impact in the field of digital signal processing....

rotating machines in particular induction electrical machines are important industry instruments. in manufacturing, electrical motors are exposed to many damages, and this causes stators and rotors not to work correctly. in this paper we addressed modal analysis and an intelligent method to detect motor load condition and also the stator faults such as turn-to-turn and coil-to-coil faults using...