Noise, neural

We should stress that the temperature T has nothing to do with the real temperature of either a brain or neural circuit. Its sole purpose is to act as a control parameter regulating the amount of noise in the stochastic system. 

In a fear-conditioning experiment, a neutral stimulus, such as a tone or a light, is paired with an aversive stimulus, such as a shock, a loud noise, or an aversive air blast. Following this experience, the formerly neutral stimulus becomes a conditioned stimulus (CS-F) and acquires the ability to elicit behaviors and physiological responses formerly only associated with the aversive stimulus, the unconditioned stimulus (UCS). Enthusiasm for this work derives at least partly from the precise delineation of neural circuits, down to the level of the genome, engaged by environmental components that produce fear conditioning (LeDoux,... 

Derks et al. [70] employed ANNs to cancel out noise in ICP. The results of neural networks (an Adaline network and a multi-layer feed-forward network) were compared with the more conventional Kalman filter. 

Receptor processes must also be selective. You can usually carry on a conversation with one person at a party if the overall noise is not too loud because you can select his or her speech sounds over the many speech sounds available in the room and focus on some to get the desired information. The reception process and the selection process in the ear do not involve the same mechanical and neural parts, but for the sake of simplicity, we will talk of reception processes as including the selectivity factor and the conversion factor. 

It is possible that psi information flows directly from the psi receptor to the brain and then results in overt behavior. An everyday example of this would be your reaction if someone sneaks up behind you and makes a loud noise. You jump We would then talk about the reception and conversion of the sound waves into a barrage of neural impulses from the ear and their direct effect on various startle reflex mechanisms within the nervous system and brain, resulting in your behavior-jumping. The whole thing happens before consciousness has time to get involved. 

I have made this model a little more complicated than the model in Chapter 2 by adding an arrow labeled Internal Stimuli within each possible information process to reflect the fact that more events are occurring in the agent s mind and nervous system than the experimenter s request to influence the target. This is also true for our clairvoyance and precognition model. For example, there may be spontaneous neural discharges or noises within your brain that interfere with the flow of information between the various processes. Or you may consciously dislike the experimenter, so that when he tells you to make the dice come up fours, you say (mentally), Nuts to you, and consciously try for a different target face. Or you may... 

Yin, T.C.T. and Chan, J.C.K. (1988) Neural mechanisms underlie interaural time sensitivity to tones and noise. In W.E. Gall, G.M. Edelman, and W.M. Cowans (Eds.), Auditory Function Neurobiological Bases of Hearing. Wiley, pp. 385 130. 

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

Thus, multilinear models were introduced, and then a wide series of tools, such as nonlinear models, including artificial neural networks, fuzzy logic, Bayesian models, and expert systems. A number of reviews deal with the different techniques [4-6]. Mathematical techniques have also been used to keep into account the high number (up to several thousands) of chemical descriptors and fragments that can be used for modeling purposes, with the problem of increase in noise and lack of statistical robustness. Also in this case, linear and nonlinear methods have been used, such as principal component analysis (PCA) and genetic algorithms (GA) [6]. 

Bulsara, A., and Gammeitoni, L Tuning in to noise. Phys. Today 1996,1996 39-45. Longtin, A., and Hinzer, K. Encoding with bursting, subthreshold oscillations, and noise in mammalian cold receptors. Neural Comput 1996,8 215-255. Mosekilde, E., Sosnovtseva, O.V., Postnov, D., Braun, H.A., and Huber, M.T. Noise-activated and noise-induced rhythms in neural systems. Nonlin Stud 2004,11 449-467. 

Mateos A, Herrero J, Tamames J, Dopazo J, Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, In Lin SM, Johnson KF, eds., Methods of Microarray Data Analysis II, Boston, Kluwer Academic Publ, pp. 91-103, 2002. 

Milton, J., VanDerHeiden, U., Longtin, A., and Mackey, M., Complex dynamics and noise in simple neural networks with delayed mixed feedback, Biomedica Biochimica Acta, Vol. 49, No. 8-9, 1990, pp. 697-707. 

For data classification, the spectra were partitioned into training and validation (test) sets. The four differently preprocessed sets of H MR brain spectra were subjected to two classification methods LDA and a noise-augmented artificial neural net (NN). All classifier training was cross-validated via the LOO method. The two classifiers (LDA and NN) were used on three-class (E, M and A) data. CCD was then implemented based on stacked generalization.61... 

Jensen No, but there are hypotheses, such as the theory that there s simply more noise in the nervous systems of lower IQ people, and that this variation from trial-to-trial in reaction time tests reflects neural noise, whatever that may mean. This should be investigated, because it s a more striking correlate of IQ than is reaction time itself. 

Nearest neighbours, classification, 138 clustering, 103, 107 Neural networks, 147 Noise, 31... 

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