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. [S0031-9007(97)02344-2]

No abstract

Unilateral weakness in established conversion disorder is associated with a distinctive pattern of activation, which overlaps with but is different from the activation pattern associated with simulated weakness. The overall pattern suggests more complex mental activity in patients with conversion disorder than in controls.

Schizophrenia is a highly heritable psychotic disorder. It has been suggested that deficits of the established state arise from abnormal interactions between brain regions. We sought to examine whether such connectivity abnormalities would be present in subjects at high genetic risk for the disorder. Functional connectivity analysis was carried out on functional MRI images from 21 controls and 69 high risk subjects performing the Hayling sentence completion task; 27 high risk subjects reported isolated psychotic symptoms, the remaining high risk subjects and controls did not. There were no significant differences in task performance between the groups. Based on previous findings we hypothesized: (i) state-related differences in connectivity between dorsolateral prefrontal cortex and lateral temporal lobe; (ii) genetically mediated reductions in a medial prefrontal-thalamic-cerebellar network; and (iii) increased prefrontal-parietal connectivity in high risk subjects (to a greater extent in those with isolated psychotic symptoms). Connectivity analysis was performed in two ways: with and without variance associated with task effects modelled and removed from the data. We did not find evidence to support our first hypothesis with either analysis method. However, consistent with hypothesis (ii), decreased connectivity between right medial prefrontal regions and contralateral cerebellum was found. This was only statistically significant in the analysis with task effects modelled and removed from the data. Finally, consistent with hypothesis (iii), increased connectivity between the left parietal and left prefrontal regions in high risk subjects was found in both analyses. These results, all in a situation uncontaminated by the effects of anti-psychotic medication, performance differences and prolonged illness, suggest there are abnormalities in functional connectivity over and above those attributable to task effects in high risk subjects. These connectivity abnormalities may underlie the diverse deficits seen in the established condition and the more subtle deficits seen in close relatives of those with the disorder.

No abstract

No abstract

No abstract

We describe the results of computer simulations of the dynamical behavior of an autoassociative network with a two-dimensional energy landscape.Such a network can model some aspects of the phenomenon of perceptual bistability in the presence of ambiguous figures. The network can be operated at either zero or nonzero temperatures which represent an internal system noise. Our results show that, under the inAuence of a weak periodic external signal, the network exhibits a maximum in the signal-to-noise ratio at an optimum noise level: the characteristic signature of stochastic resonance.Studies of the perception of ambiguous figures have a long history [1,2]. Perception of these kinds of figures (e.g., the Necker cube [2]; see Fig. 1) is characterized by noisy bistable dynamics, that is, the two different interpretations, elicited by the figure, are alternatively perceived by the observer with a stochastic time course. N umerous experiments have shown that the times between such reversals are approximately gamma distributed [3]. Such distributions are common in biology and can be interpreted in terms of the noise driven motion of a state point which randomly crosses a threshold or surmounts an energy barrier. Recently, there has been a revival of interest in these results in connection with the development of dynamical models of brain function during such reversals in perception [4]. There has also been a growing interest in stochastic resonance (SR) associated with noisy nonlinear systems [5]. This is a dynamical behavior wherein noise may enhance the transmission of information through certain systems, such that a defined signal-to-noise ratio (SNR) achieves a maximum for an optimum value of the noise intensity. SR has been demonstrated in numerous physical experiments [5,6] and, more recently, in a simple sensory neuron [7]. The I IG. 1. Necker cube with its two alternative interpretations. theory of SR was first advanced as a possible explanation of the observed periodic recurrences of the Earth's Ice Ages [8], and, stimulated by an interesting experiment with a bistable ring laser [9], has been the object of numerous recent theoretical studies [10]. The possible importance of SR for the processing of information in neural systems seems evident at all levels from the lower physiologieaf levels to the higher eognkil. e ones. Indeed, it has long been recognized that noise can improve the performance of certain neural networks [11],and it may be possible that an optimum noise level can achieve the maximum improvement.In this Letter, we consider a noisy autoassociative neural network which has previously been shown to be an accurate model of the bistable perceptual process involved in the interpretation of ambiguous figures [12]. Our results indicate that SR, as well as other recently studied f'eatures of noisy bistable dynamics, can easily be demonstrated in this system.Our network is made up of binary neurons (activation levels 0 and 1) which are globally, or all to all, connected [13]. The connection matrix is sy...