Noise-enhanced information transmission in rat SA1 cutaneous mechanoreceptors via aperiodic stochastic resonance

Collins, James J.; Imhoff, Thomas T.; Grigg, Peter

doi:10.1152/jn.1996.76.1.642

Cited by 371 publications

(196 citation statements)

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“…Figure 1b illustrates the firing rate f versus the noise intensity D for both the cases of Gaussian white noise (s c = 0 ms) and colored noise (s c = 1, 2.5, 5 ms). Note that here only small values of s c are considered because the correlation time is short and typically less than several milliseconds in real biological neural systems (Collins et al 1996). As we see in Fig.…”

Section: Isr In Single Hh Neuronmentioning

confidence: 99%

Inhibition of rhythmic spiking by colored noise in neural systems

Guo

2011

Cogn Neurodyn

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We study the effect of colored noise on the rhythmic spiking activity of neural systems in this paper. The phenomenon of the so-called inverse stochastic resonance , that is, noise with appropriate intensity suppresses the spiking activity in neural systems, is clearly observed in a special parameter regime. We find that the inhibition effect of colored noise is stronger than that of Gaussian white noise. Furthermore, our simulation results show that the inhibition effect of colored noise provides a useful mechanism for the generation of synchronized burst in type-2 mixed-feed-forward-feedback loop neuronal network motif, which indicates that such inhibition effect might have some biological implications.

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Section: Isr In Single Hh Neuronmentioning

confidence: 99%

Inhibition of rhythmic spiking by colored noise in neural systems

Guo

2011

Cogn Neurodyn

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“…This is reminiscent of ''stochastic resonance,'' a phenomenon wherein the response of a nonlinear system to a weak input signal is optimized by the presence of a particular, nonzero level of noise (36). For example, in threshold units such as sensory neurons, moderate noise levels improve their responses by bringing them above threshold (37). Stochastic resonance has been observed in sensory systems (37), but not in the central nervous system.…”

Section: Physiological Noise Inputs Do Not Lead To Similar Increase Imentioning

confidence: 99%

“…For example, in threshold units such as sensory neurons, moderate noise levels improve their responses by bringing them above threshold (37). Stochastic resonance has been observed in sensory systems (37), but not in the central nervous system.…”

Section: Physiological Noise Inputs Do Not Lead To Similar Increase Imentioning

confidence: 99%

Chaos may enhance information transmission in the inferior olive

Schweighofer

Doya

Fukai

et al. 2004

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

116

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Despite unique well characterized neuronal properties, such as extensive electrical coupling and low firing rates, the role of the inferior olive (IO), which is the source of the climbing fiber inputs to cerebellar Purkinje cells, is still controversial. We propose that the IO stochastically recodes the high-frequency information carried by its synaptic inputs into stochastic, low-rate spikes in its climbing fiber output. Computer simulations of realistic IO networks showed that moderate electrical coupling produced chaotic firing, which maximized the input-output mutual information. This ''chaotic resonance'' may allow rich error signals to reach individual Purkinje cells, even at low firing rates, allowing efficient cerebellar learning.O ver a century of cerebellar research has provided us with comprehensive understandings of cerebellar anatomy and physiology. It is well known, for instance, that the output neurons of the cerebellar cortex, the Purkinje cells, receive two major types of synaptic inputs: Ͼ100,000 parallel fibers and a single climbing fiber, an axon from an inferior olive (IO) neuron; whereas summation of parallel fiber inputs generate ''simple spikes,'' a single IO spike generates a ''complex spike'' through the powerful climbing fiber input. Furthermore, the major anatomical and electrophysiological properties of the IO neurons are well characterized (1). First, these neurons generate dendritic and somatic spikes at low firing rates in vivo (three spikes per sec at most) (2). Second, they are electrotonically coupled by gap junctions (3), more extensively than any other cells of the mammalian brain (4). Third, they exhibit subthreshold oscillations in vitro (5, 6). However, despite this detailed knowledge, we still lack a clear understanding of the function of the cerebellum, and two seemingly contradictory major hypotheses have been proposed, each based on a dramatically different view of the IO (7,8).According to the cerebellar learning hypothesis (9-13), when conjointly activated with parallel fibers, IO spikes modify cerebellar input-output transformations, in agreement with the known long-term depression (LTD) at the parallel fiberPurkinje cell synapse (14). Recent cerebellar motor learning theories (12, 15-19) make two further postulates relevant to this hypothesis. First, the IO neurons must fire at a low firing rate so that complex spikes encoding error signals do not interfere with simple spikes carrying motor control commands (12, 15). Second, the IO must transmit error signals (15, 20) with hightemporal resolution for cerebellar learning for efficient motor control.Alternatively, the cerebellar timing hypothesis states that the IO exerts its influence on motor control in real time via synchronous and rhythmic discharges (21). This hypothesis is in agreement with the facts that IO neurons are extensively electrically coupled via gap junctions (8) and that IO neurons fire with some degree of rhythmicity and pair-wise synchrony both in vitro (22) and in vivo (21). It is further support...

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“…There is also speculation that internal noise of a biological oscillator may play a constructive role in information transfer through SR [3]. So far, there have been experimental demonstrations of SR in a variety of biological systems [4,5,6,7,8,9,10], including the interesting discovery that SR enhances the electrosensory information available to paddlefish for prey capture [10]. There has even been a psychophysical experiment demonstrating that SR can be used as a measuring tool to characterize the ability of the human brain to interpret visual patterns immersed in noise [11].…”

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confidence: 99%

“…Because of the ubiquity of SR in biological systems [4,5,6,7,8,9,10], a natural question is how a biological oscillator tunes to the optimal noise level to realize SR. For this purpose a measure that is highly sensitive to noise variation is desired. There are also potential technological applications where one might be interested in such a noise-sensitive measure.…”

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confidence: 99%

Noise-sensitive measure for stochastic resonance in biological oscillators

Lai

Park

2006

Mathematical Biosciences and Engineering

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Abstract. There has been ample experimental evidence that a variety of biological systems use the mechanism of stochastic resonance for tasks such as prey capture and sensory information processing. Traditional quantities for the characterization of stochastic resonance, such as the signal-to-noise ratio, possess a low noise sensitivity in the sense that they vary slowly about the optimal noise level. To tune to this level for improved system performance in a noisy environment, a high sensitivity to noise is required. Here we show that, when the resonance is understood as a manifestation of phase synchronization, the average synchronization time between the input and the output signal has an extremely high sensitivity in that it exhibits a cusp-like behavior about the optimal noise level. We use a class of biological oscillators to demonstrate this phenomenon, and provide a theoretical analysis to establish its generality. Whether a biological system actually takes advantage of phase synchronization and the cusp-like behavior to tune to optimal noise level presents an interesting issue of further theoretical and experimental research.

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Noise-enhanced information transmission in rat SA1 cutaneous mechanoreceptors via aperiodic stochastic resonance

Cited by 371 publications

References 0 publications

Inhibition of rhythmic spiking by colored noise in neural systems

Inhibition of rhythmic spiking by colored noise in neural systems

Chaos may enhance information transmission in the inferior olive

Noise-sensitive measure for stochastic resonance in biological oscillators

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