We verified whether a stochastic resonance paradigm (SR), with random interference (“ noise ”) added in optimal amounts, improves the detection of sub-threshold visual information by subjects with retinal disorder and impaired vision as it does in the normally sighted. Six levels of dynamic, zero-mean Gaussian noise were added to each pixel of images (13 contrast levels) in which alphabet characters were displayed against a uniform gray background. Images were presented with contrast below the subjective threshold to 14 visually impaired subjects (age: 22–53 yrs.). The fraction of recognized letters varied between 0 and 0.3 at baseline and increased in all subjects when noise was added in optimal amounts; peak recognition ranged between 0.2 and 0.8 at noise sigmas between 6 and 30 grey scale values (GSV) and decreased in all subjects at noise levels with sigma above 30 GSV. The results replicate in the visually impaired the facilitation of visual information processing with images presented in SR paradigms that has been documented in sighted subjects. The effect was obtained with low-level image manipulation and application appears readily possible: it would enhance the efficiency of today vision-improving aids and help in the development of the visual prostheses hopefully available in the future.

Objective: To study behavioral and brain responses to variations in signal-to-noise ratio (SNR) of cognitive visual stimuli.Methods: We presented meaningful words visually, embedded in varying amounts of dynamic noise, and utilized magnetoencephalography (MEG) to measure responses to the words. A multidipole model of the evoked fields was constructed to quantify the strengths and latencies of the neuronal sources at each noise level. The recognition rates of the words were measured in separate behavioral sessions.Results: MEG revealed sequential activation of occipital and occipito-temporal areas (latencies 130-250 and 170-350 ms, respectively) followed by activity in superior temporal cortex (230-640 ms). The strengths and latencies of all identified sources followed functions similar to the SNR of the stimulus. The peak amplitudes and shortest latencies of all sources coincided with the maximum SNR of the stimulus. The occipito-temporal and temporal sources as well as the word recognition rate accurately followed the SNR of the stimulus whereas the early occipital source exhibited a more peaked dependence on the SNR.Conclusions: Evoked responses expectedly peaked at the maximum SNR of the stimulus. Interestingly, early visual responses showed sharper peaks than longer-latency sources as a function of the noise level. This can be understood as the higher-level processes analyzing the stimuli more holistically and thus being less sensitive to the salience of simple visual features. The similar noise-dependence of the longer-latency sources and the recognition rate provides new evidence for the relevance of these activations in the recognition of written words.Significance: This study contributes to the understanding of brain activity evoked by degraded stimuli with cognitive content. (c) 2006 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

We present a neural-network valuation of financial derivatives in the case of fat-tailed underlying asset returns. A two-layer perceptron is trained on simulated prices taking into account the well-known effect of volatility smile. The prices of the underlier are generated using fractional calculus algorithms, and option prices are computed by means of the Bouchaud-Potters formula. This learning scheme is tested on market data; the results show a very good agreement between perceptron option prices and real market ones.

We study the volatility of the MIB30-stock-index high-frequency data from November 28, 1994 through September 15, 1995. Our aim is to empirically characterize the volatility random walk in the framework of continuous-time finance. To this end, we compute the index volatility by means of the log-return standard deviation. We choose an hourly time window in order to investigate intraday properties of volatility. A periodic component is found for the hourly time window, in agreement with previous observations. Fluctuations are studied by means of detrended fluctuation analysis, and we detect long-range correlations. Volatility values are log-stable distributed. We discuss the implications of these results for stochastic volatility modelling.

An artificial neural network modelling some peculiar aspects of human perception in the presence of so-called ambiguous figures is described. When one of such patterns is observed, the same visual input can elicit two different interpretations, A and B, giving rise to a cyclic perceptual alternation of the two competitive percepts. The neural network used to model the phenomenon consists of two layers of identical subunits, which have been derived from the brain-state-in-a-box (BSB) model developed by Anderson and co-workers. Computer simulations have demonstrated that the model based on this two-layer neural network allows one to obtain the stochastic gamma distributions of the experimental perceptual durations of the two alternative interpretations of an ambiguous pattern. Moreover, simulation results are in good agreement with some other characteristics of the perceptual alternation phenomenon.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The perspective reversals elicited by a set of drawings based on the Mach truncated pyramid are examined. We obtained each pattern of the set from the previous one by adding to it some graphic cues, which were easily integrated into one of the two competing interpretations, thus reducing step by step the ambiguity of the basic pattern. The phenomenological model, proposed to link the mean times of both alternative interpretations with their complexities, is in close agreement with our experimental data. Furthermore, different aspects of such data are well described by the model equations: The measure of the prevalence of the supported interpretation is well correlated with the difference in complexity between the two alternative interpretations; the two different trends of the mean time of the unfavored interpretation, found in the data obtained from different observers as a function of the various patterns of the set, are well fitted by the model without the need for any specific additional hypothesis.