La cosiddetta risonanza stocastica (RS) è un fenomeno statistico recentemente studiato in un'ampia gamma di sistemi fisici non lineari. Secondo la RS esisterebbe un livello ottimale di rumore per il quale il contenuto informativo di segnali deboli sotto soglia può essere rilevato. Questo studio analizza le possibilità della RS nel sistema visivo umano in termini di capacità soggettiva di riconoscere parametri in condizioni ottimali di rumore per stabilire le possibilita della fRM nell'obbiettivizzazione dei risultati.

We studied synchronization of electrosensitive cells of the paddlefish by means of electrophysiological experiments. We found that primary afferents of the paddlefish are represented by noisy nonlinear oscillators. Different types of phase locked regimes are observed. The influence of internal noise is discussed.

Stochastic resonance (SR) is a statistical phenomenon recently studied in rr wide range of non linear physical systems. According to SR, there exists nn optimal noise level for which the information content of weak sub-threshold signals cml he detected. This study investigated SR in the human visual system in terms of subjective capacity, to recognise patterns under optimal noise conditions to Establish the possibilities of fMR1 techniques in making results objective.

Psychophysics experiments on the human visual system have established that the sensitivity for the detection of fine detail in noise contaminated images can be quantitatively and repeatably measured using stochastic resonance as a tool. Optimal noise results in maximal sensitivity. Does this mean simply that the retina is averaging out the noise, or instead is the visual cortex involved in some level of computation? We report results of functional magnetic resonance imaging experiments which indicate the latter. (C) 1999 Elsevier Science B.V. All rights reserved.

Subthreshold information carrying signals can be detected and their information content enhanced by the addition of a random process, or ''noise'', in a large class of nonlinear systems. And often a maximal enhancement is possible with the inclusion of an optimal noise intensity. This process is known as stochastic resonance. It has a history of demonstrations in a variety of physical systems and has more recently been observed in both sensory and molecular biology and in medical science. In all of these observations, the noise enhanced signal was analyzed for information content by some algorithm implemented by computer. By contrast, in this work human perception replaces computer analysis. We report the results of an extensive psychophysics experiment wherein subjects analyzed computer generated visual images enhanced by time varying noise. The images were generated in analogy to classic stochastic resonance experiments. The results are robust and can be accurately described with simple threshold stochastic resonance theory, suggesting that the brain possibly interprets spatio-temporal visual information by similar computational processes.

Stochastic resonance can be used as a measuring tool to quantify the ability of the human brain to interpret noise contaminated visual patterns. Here we report the results of a psychophysics experiment which show that the brain can consistently and quantitatively interpret detail in a stationary image obscured with time varying noise and that both the noise intensity and its temporal characteristics strongly determine the perceived image quality.