Frequency-difference-dependent stochastic resonance in neural systems

Efficient transfer of sensory information to higher (motor or associative) areas in primate visual cortical areas is crucial for transforming sensory input into behavioral actions. Dynamically increasing the level of coordination between single neurons has been suggested as an important contributor to this efficiency. We propose that differences between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas, ensuring a proper routing of the information flow. Here we determined the level of coordination between neurons in area MT in macaque visual cortex in a visual attention task via the strength of synchronization between the neurons’ spike timing relative to the phase of oscillatory activities in local field potentials. In contrast to reports on the ventral visual pathway, we observed the synchrony of spikes only in the range of high gamma (180 to 220 Hz), rather than gamma (40 to 70 Hz) (as reported previously) to predict the animal’s reaction speed. This supports a mechanistic role of the phase of high-gamma oscillatory activity in dynamically modulating the efficiency of neuronal information transfer. In addition, for inputs to higher cortical areas converging from the dorsal and ventral pathway, the distinct frequency bands of these inputs can be leveraged to preserve the identity of the input source. In this way source-specific oscillatory activity in primate cortex can serve to establish and maintain “functionally labeled lines” for dynamically adjusting cortical information transfer and multiplexing converging sensory signals.

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“…6, and refs. 45 and 46). We further set these model neurons to generate a spike when they reach a threshold (0.8).…”

Section: Resultsmentioning

confidence: 98%

Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex

Khamechian

Kozyrev

Treue

et al. 2019

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

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“…The additional periodic forcing considered here was meant to illustrate the stability of the suggested control approach. However, future studies could also provide a detailed analysis of the interplay of one or more periodic inputs and noise, thereby focusing on stochastic resonance and related phenomena (e.g., Pikovsky and Kurths, 1997 ; Gammaitoni et al, 1998 ; Manjarrez et al, 2002 ; Torres et al, 2011 ; Bordet et al, 2015 ; Yu et al, 2016 ; Guo et al, 2017 ; Uzuntarla et al, 2017 and references therein). The number of stimulation sites and CR stimulation spatial decay was based on Lysyansky et al ( 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Short-Term Dosage Regimen for Stimulation-Induced Long-Lasting Desynchronization

Manos

Zeitler²,

Tass

2018

Front. Physiol.

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In this paper, we computationally generate hypotheses for dose-finding studies in the context of desynchronizing neuromodulation techniques. Abnormally strong neuronal synchronization is a hallmark of several brain disorders. Coordinated Reset (CR) stimulation is a spatio-temporally patterned stimulation technique that specifically aims at disrupting abnormal neuronal synchrony. In networks with spike-timing-dependent plasticity CR stimulation may ultimately cause an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and neuronal synchrony. This long-lasting desynchronization was theoretically predicted and verified in several pre-clinical and clinical studies. We have shown that CR stimulation with rapidly varying sequences (RVS) robustly induces an anti-kindling at low intensities e.g., if the CR stimulation frequency (i.e., stimulus pattern repetition rate) is in the range of the frequency of the neuronal oscillation. In contrast, CR stimulation with slowly varying sequences (SVS) turned out to induce an anti-kindling more strongly, but less robustly with respect to variations of the CR stimulation frequency. Motivated by clinical constraints and inspired by the spacing principle of learning theory, in this computational study we propose a short-term dosage regimen that enables a robust anti-kindling effect of both RVS and SVS CR stimulation, also for those parameter values where RVS and SVS CR stimulation previously turned out to be ineffective. Intriguingly, for the vast majority of parameter values tested, spaced multishot CR stimulation with demand-controlled variation of stimulation frequency and intensity caused a robust and pronounced anti-kindling. In contrast, spaced CR stimulation with fixed stimulation parameters as well as singleshot CR stimulation of equal integral duration failed to improve the stimulation outcome. In the model network under consideration, our short-term dosage regimen enables to robustly induce long-term desynchronization at comparably short stimulation duration and low integral stimulation duration. Currently, clinical proof of concept is available for deep brain CR stimulation for Parkinson's therapy and acoustic CR stimulation for tinnitus therapy. Promising first in human data is available for vibrotactile CR stimulation for Parkinson's treatment. For the clinical development of these treatments it is mandatory to perform dose-finding studies to reveal optimal stimulation parameters and dosage regimens. Our findings can straightforwardly be tested in human dose-finding studies.

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“…In contrast, our current model utilizes a single synapse type, and we do not model noise explicitly. Guo et al (2017) consider the behavior of a neural system that responds to superposed signals of different frequencies. In the current paper, we consider only a narrow range of frequencies.…”

Section: Multisensory Processing In the Brainmentioning

confidence: 99%

An oscillatory neural network model that demonstrates the benefits of multisensory learning

Rao

2018

Cogn Neurodyn

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Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.

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Frequency-difference-dependent stochastic resonance in neural systems

Cited by 75 publications

References 55 publications

Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex

Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex

Short-Term Dosage Regimen for Stimulation-Induced Long-Lasting Desynchronization

An oscillatory neural network model that demonstrates the benefits of multisensory learning

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