A possible mechanism of stochastic resonance in the light of an extra-classical receptive field model of retinal ganglion cells

Ghosh, Kuntal; Sarkar, Sandip; Bhaumik, Kamales

doi:10.1007/s00422-009-0306-9

Cited by 24 publications

(21 citation statements)

References 31 publications

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“…Broadband spatial frequency content, on the other hand, may excite neurons with different tuning properties simultaneously producing responses that may be different from their responses to bar or grating stimulus. Therefore, it cannot be said conclusively that the detection sensitivities reported in these studies (Simonotto et al 1997(Simonotto et al , 1999Piana et al 2000;Ghosh et al 2009) correspond to contrast sensitivity (to sine-wave grating stimuli).…”

Section: Introductionmentioning

confidence: 76%

“…A theoretical investigation, about the utility of SR in the light of an extra-classical receptive field, is carried out more recently in (Ghosh et al 2009). In this study, the authors have used stimuli similar to the one used in (Simonotto et al 1997) and the visual processing was approximated by a suitable filter function that included contributions from extra-classical receptive field, and only the zero-crossing information was considered for the analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In all these investigations (Simonotto et al 1997(Simonotto et al , 1999Piana et al 2000;Ghosh et al 2009), authors have used SR to estimate sensitivity to detect a specific stimulus or some structures in the stimulus in a noisy environment, and the detection threshold was measured in terms of threshold contrast. The stimuli used in these studies were either a grating generated by a spatial function A sin(1/x) + 128 (A is Michelson contrast and x is the horizontal coordinate) or capitalized letters, which contained a band of frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic resonance in visual sensitivity

Kundu

Sarkar

2014

Biol Cybern

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It is well known from psychophysical studies that stochastic resonance, in its simplest threshold paradigm, can be used as a tool to measure the detection sensitivity to fine details in noise contaminated stimuli. In the present manuscript, we report simulation studies conducted in the similar threshold paradigm of stochastic resonance. We have estimated the contrast sensitivity in detecting noisy sine-wave stimuli, with varying area and spatial frequency, as a function of noise strength. In all the cases, the measured sensitivity attained a peak at intermediate noise strength, which indicate the occurrence of stochastic resonance. The peak sensitivity exhibited a strong dependence on area and spatial frequency of the stimulus. We show that the peak contrast sensitivity varies with spatial frequency in a nonmonotonic fashion and the qualitative nature of the sensitivity variation is in good agreement with human contrast sensitivity function. We also demonstrate that the peak sensitivity first increases and then saturates with increasing area, and this result is in line with the results of psychophysical experiments. Additionally, we also show that critical area, denoting the saturation of contrast sensitivity, decreases with spatial frequency and the associated maximum contrast sensitivity varies with spatial frequency in a manner that is consistent with the results of psychophysical experiments. In all the studies, the sensitivities were elevated via a nonlinear filtering operation called stochastic resonance. Because of this nonlinear effect, it was not guaranteed that the sensitivities, estimated at each frequency, would be in agreement with the corresponding results of psychophysical experiments; on the contrary, close agreements were observed between our results and the findings of psychophysical investigations. These observations indicate the utility of stochastic resonance in human vision and suggest that this paradigm can be useful in psychophysical studies.

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Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic resonance in visual sensitivity

Kundu

Sarkar

2014

Biol Cybern

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“…Operators are commonly used to describe input/output transformation of neuronal signals. Typical examples are those used to model the receptive field of retinal ganglion cells (17), representation of time (18), auditory stimuli (19), and extracellular neural signals (20). In our case, this seems to be a promising approach that allows one to make general predictions about the properties, number, and structure of the operators representing the odor stimuli.…”

Section: Discussionmentioning

confidence: 99%

Synaptic clusters function as odor operators in the olfactory bulb

Migliore

Cavarretta

Marasco

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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How the olfactory bulb organizes and processes odor inputs through fundamental operations of its microcircuits is largely unknown. To gain new insight we focus on odor-activated synaptic clusters related to individual glomeruli, which we call glomerular units. Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. The results provide new and specific theoretical and experimentally testable predictions. T he organization of olfactory bulb network elements and their synaptic connectivity has evolved to subserve special computational functions needed for odor detection and recognition (1-5). Key to this organization are the olfactory glomeruli, collecting input from olfactory receptor neuron subsets. These connect to the dendrites of mitral, tufted, and periglomerular cells, and the mitral and tufted cells in turn connect to granule cells. We term these interconnected cells a cluster, and a cluster related to a given glomerulus is a glomerular unit (GU), often visualized as a column of granule cell bodies located below a glomerulus (6, 7). The existence of such GUs has also been suggested from 2-deoxyglucose (8) and voltage-sensitive dye studies (9).Understanding the neural basis of odor processing therefore requires understanding the computational functions and role of GUs. These issues, which are difficult or impossible in experiments, can be conveniently explored using realistic computational models, provided they are able to explain and reproduce crucial experimental findings on glomerular clusters or units.Analyzing synaptic interactions between cells with overlapping dendrites requires modeling in real 3D space. Scaling up to the network level further requires scaling up realistic structural and functional properties to many thousands of cells (10). Building on this unique approach, we show that this model generates columnar clusters of cells related to individual glomeruli, as in the experiments, and further demonstrates mechanisms of odor processing within and between the GUs. Finally, interpreting this network activity requires a theoretical framework, incorporating distributed activated glomeruli within the global network, for which we introduce the concept of the odor operator. The results provide a basis for extension to the glomerular level on the one hand and interactions with olfactory cortex on the other.

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“…An evidence of enhanced cortical activity for optimal noise stimulation was observed [18] using functional magnetic resonance techniques. A theoretical investigation about the possible mechanism of stochastic resonance in the light of an extra-classical receptive field is carried out more recently [19]. Enhancement of perceptive threshold contrast sensitivity (inverse of perceptive threshold contrast), via SR, is reported in [12,13], where they have used noisy stimuli that are depressed beneath an artificial threshold.…”

Section: Introductionmentioning

confidence: 99%