Encoding with Bursting, Subthreshold Oscillations, and Noise in Mammalian Cold Receptors

Longtin, André; Hinzer, Karin

doi:10.1162/neco.1996.8.2.215

Cited by 95 publications

(65 citation statements)

References 29 publications

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“…This is also the case with the membrane voltage oscillations in neocortical neurons reported by Gutfreund, Yarom, and Segev (1995), Klink and Alonso (1993), as well as in other neuron types, for example, Hutcheon, Miura, Yarom, and Puil (1994) and Lampl and Yarom (1997). We suggest that in addition to the deterministic macroscopic mechanisms that were proposed to explain the generation of the subthreshold oscillations, the stochastic nature (and the limited number) of the ion channels may have a dominant effect on the nature of these oscillations (see also Longtin &Hinzer, 1996, andBraun et al, 1998).…”

Section: Subhresholdsupporting

confidence: 68%

“…In a more general perspective, the effect of different kinds of noise on the firing threshold of neurons was examined by Lecar and Nossal (1971a,b). Recently Jensen and Gartner (1997) have dealt with the effect of additive white noise on the firing reliability of different neuron models (see also Longtin &Hinzer, 1996, andBraun, Huber, Dewald, Schafer, &Voigt, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

1998

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The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to DC and fluctuating current input in neocortical neurons, as reported by Mainen & Sejnowski (1995). For DC inputs, spike timing is highly unreliable; the reliability and precision are significantly increased for fluctuating current input. This behavior is critically determined by the relatively small number of excitable channels that are opened near threshold for spike firing rather than by the total number of channels that exist in the membrane patch. Channel fluctuations, together with the inherent bistability in the HH equations, give rise to three additional experimentally observed phenomena: subthreshold oscillations in the membrane voltage for DC input, "spontaneous" spikes for subthreshold inputs, and "missing" spikes for suprathreshold inputs. We suggest that the noise inherent in the operation of ion channels enables neurons to act as "smart" encoders. Slowly varying, uncorrelated inputs are coded with low reliability and accuracy and, hence, the information about such inputs is encoded almost exclusively by the spike rate. On the other hand, correlated presynaptic activity produces sharp fluctuations in the input to the postsynaptic cell, which are then encoded with high reliability and accuracy. In this case, information about the input exists in the exact timing of the spikes. We conclude that channel stochasticity should be considered in realistic models of neurons.

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Section: Subhresholdsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

1998

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“…A famous exception is the repetitive bursting of afferents of mammalian thermoreceptors for cold (Darian-Smith et al 1973;Huber et al 2000;Longtin and Hinzer 1996). Another example is probability (P) type electroreceptor afferents of weakly electric fish, which can be divided into two groups of tonic and bursting spontaneous firing (Bastian 1981;Xu et al 1996).…”

Section: Functional Significance Of Afferent Burstsmentioning

confidence: 99%

“…Thus noise can be used to reveal dynamical regimes of a system that are available but are hidden normally. There are several theoretical studies of noise-induced transitions in biological systems, including noise-induced transitions in Hodgkin-Huxley models of excitable membranes (Horsthemke and Lefever 1981;Tanabe and Pakdaman 2001), and noise-induced bursting in models of mammalian thermoreceptors (Huber et al 2000;Longtin and Hinzer 1996).…”

Section: Introductionmentioning

confidence: 99%

Noise-Induced Transition to Bursting in Responses of Paddlefish Electroreceptor Afferents

Neiman¹,

Yakusheva²,

Russell³

2007

Journal of Neurophysiology

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Neiman AB, Yakusheva TA, Russell DF. Noise-induced transition to bursting in responses of paddlefish electroreceptor afferents. J Neurophysiol 98: [2795][2796][2797][2798][2799][2800][2801][2802][2803][2804][2805][2806] 2007. First published September 12, 2007; doi:10.1152/jn.01289.2006. The response properties of ampullary electroreceptors of paddlefish, Polyodon spathula, were studied in vivo, as single-unit afferent responses to external electrical stimulation with varied intensities of several types of noise waveforms, all Gaussian and zero-mean. They included broadband white noise, Ornstein-Uhlenbeck noise, low-or high-frequency band-limited noise, or natural noise recorded from swarms of Daphnia zooplankton prey, or from individual prey. Normally the afferents fire spontaneously in a tonic manner, which is actually quasiperiodic due to embedded oscillators. 1) Weak noise stimuli increased the variability of afferent firing, but it remained tonic. 2) In contrast, stimulation with less-weak broadband noise led to a qualitative change of the firing patterns, to parabolic bursting, even though the mean firing rate was scarcely affected.3) The transition to afferent bursting was marked by the development of two well-separated timescales: the fast frequency of spiking inside bursts at Յ250 spikes/s and the slow frequency of burst occurrences at about 9 (range 5-13) bursts/s. These two timescales were manifested as two regimes in afferent power spectra, bimodal interspike interval histograms, return maps, and autocorrelation functions of afferent spike trains. 4) The stochastic approximately 9-Hz bursts were not simply driven by similar-frequency components of noise stimuli because bursts could be dissociated from stimulus waveforms using high-pass filtered noise, or a 0.1-Hz sine-wave stimulus. 5) Arrhenius plots showed that the threshold noise intensity required to elicit bursting depended on the frequency content of a noise stimulus, being lowest, about 1.2 V/cm, for stimuli matching the 1-to 20-Hz best response band of these cathodally excited ampullary electroreceptors. This is only slightly higher than previous behavioral estimates of the electrosensory threshold as 0.5 V/cm. 6) Comparable threshold values for bursting came from an alternate analytical approach, based on correlation times of spike trains. 7) Simultaneous recordings from pairs of afferents showed that their bursting frequencies (bursts/s) always converged as the amplitude of a noise stimulus was raised. Thus the slow timescale of bursting is similar for different electroreceptors, even though their mean spiking rates can differ. In conclusion, the ampullary electroreceptors of paddlefish have two distinct modes of operation: their spontaneous tonic firing is modulated by the weakest stimuli, but they switch to bursting output for less-weak stimuli. We propose that afferent bursting may mediate close-range tracking of planktonic prey.

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“…For example, neuronal activity in the brain is inherently noisy because of random ion channel dynamics and stochastic synaptic input. For a detailed discussion of noise in neuronal systems, see for example, Longtin and Hinzer (1996) and Tuckwell (1988) as well as White et al (1998White et al ( , 2000 and Huber et al (1998) with respect to oscillatory neurons. Moreover, random fluctuations of RNA concentration and protein expression might contribute to long-lasting neuroplastic sensitization effects.…”

Section: Introductionmentioning

confidence: 99%

On Episode Sensitization in Recurrent Affective Disorders: The Role of Noise

Huber

Braun

Krieg

2003

Neuropsychopharmacol

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Episode sensitization is postulated as a key mechanism underlying the long-term course of recurrent affective disorders. Functionally, episode sensitization represents positive feedback between a disease process and its disease episodes resulting in a transition from externally triggered to autonomous episode generation. Recently, we introduced computational approaches to elucidate the functional properties of sensitization. Specifically, we considered the dynamics of episode sensitization with a simple computational model. The present study extends this work by investigating how naturally occurring, internal or external, random influences ('noise') affect episode sensitization. Our simulations demonstrate that actions of noise differ qualitatively in dependence on both the model's activity state as well as the noise intensity. Thereby induction as well as suppression of sensitization can be observed. Most interestingly, externally triggered sensitization development can be minimized by tuning the noise to intermediate intensities. Our findings contribute to the conceptual understanding of the clinical kindling model for affective disorders and also indicate interesting roles for random fluctuations in kindling and sensitization at the neuronal level. Neuropsychopharmacology (2003) 28, S13-S20.

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Encoding with Bursting, Subthreshold Oscillations, and Noise in Mammalian Cold Receptors

Cited by 95 publications

References 29 publications

Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

Noise-Induced Transition to Bursting in Responses of Paddlefish Electroreceptor Afferents

On Episode Sensitization in Recurrent Affective Disorders: The Role of Noise

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