Oscillation Regularity in Noise-Driven Excitable Systems with Multi-Time-Scale Adaptation

Nesse, William H.; Negro, Christopher A. Del; Bressloff, Paul C.

doi:10.1103/physrevlett.101.088101

Cited by 20 publications

(28 citation statements)

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“…6 approximates biophysically realistic models such as ML (Fig. 2) effectively over a realistic range of baseline input and fluctuation levels, provided that the autocorrelation time scale of input current is fast relative to the mean ISI (12,40), which is consistent with the fast-fluctuating noisy input xðtÞ used in Fig. 1.…”

Section: Note Thatmentioning

confidence: 60%

“…We generated pðΔtjhÞ with a spike rate function λðHÞ ≥ 0, in which the probability of a spike in a small time interval δt is λðHÞδt (12,30,40). To correctly model the effect of the AC on spiking, λðHÞ must elicit lower rates for large H and vice versa.…”

Section: Note Thatmentioning

confidence: 99%

“…The exponential dependence of firing rate on H in [6] arises commonly from spiking neural models on the basis of diffusion processes, where the effect of noisy input xðtÞ is represented as a probability of spiking per unit time (12,14,30,(40)(41)(42). Moreover, Eq.…”

Section: Note Thatmentioning

confidence: 99%

“…This drop is associated with baseline input levels near the deterministic spike threshold (see SI Text, Fitting the ML Model). The CV then increases for higher baseline levels, which is a unique feature of adapting models (14,40) and contrasts with the monotonic s-input-CV graph of nonadapting models (42). Very high baselines (s ≳ 0.8) result in exponential firing-rate gains that are considered nonphysiological and will not be considered further (see SI Text, Fitting the ML Model.…”

Section: Note Thatmentioning

confidence: 99%

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Biophysical information representation in temporally correlated spike trains

Nesse

Maler²,

Longtin³

2010

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

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Spike trains commonly exhibit interspike interval (ISI) correlations caused by spike-activated adaptation currents. Here we investigate how the dynamics of adaptation currents can represent spike pattern information generated from stimulus inputs. By analyzing dynamical models of stimulus-driven single neurons, we show that the activation states of the correlation-inducing adaptation current are themselves statistically independent from spike to spike. This paradoxical finding suggests a biophysically plausible means of information representation. We show that adaptation independence is elicited by input levels that produce regular, non-Poisson spiking. This adaptation-independent regime is advantageous for sensory processing because it does not require sensory inferences on the basis of multivariate conditional probabilities, reducing the computational cost of decoding. Furthermore, if the kinetics of postsynaptic activation are similar to the adaptation, the activation state information can be communicated postsynaptically with no information loss, leading to an experimental prediction that simple synaptic kinetics can decorrelate the correlated ISI sequence. The adaptation-independence regime may underly efficient weak signal detection by sensory afferents that are known to exhibit intrinsic correlated spiking, thus efficiently encoding stimulus information at the limit of physical resolution.neural correlations | spike train information N egative-feedback processes are ubiquitous in biological systems. In neurons, spike-triggered adaptation processes inhibit subsequent spiking and can produce temporal correlations in the spike train, in which a longer than average interspike interval (ISI) makes a shorter subsequent ISI more likely and vice versa. Such correlations are common in neurons (1-7) and model neurons (8-14). The impact of temporal relationships in spike trains, including ISI correlations, on neural information processing has been under intense investigation (1,2,(15)(16)(17)(18)(19)(20)(21)(22). Temporally correlated spike trains pose a challenge for ISI-based sensory inferences because each ISI depends on both present and past stimulus activity, requiring complex strategies to make accurate inferences (23,24). Some ISI correlations can be attributed to long-time-scale autocorrelations of the input (23, 25) or correlations related to long-time-scale adaptive responses (23, 26). However, if the ISI correlations arise from inputs or noise with fast autocorrelation time scales relative to both the mean ISI and the adaptation time scales of the cell, the resulting fine-grained time structure of spike trains can shape information processing of the underlying slower input fluctuations (2,3,11,15,18,19,(27)(28)(29). Specifically, Jacobs et al. (29) have recently shown that fine-grained temporal coding of ISIs that takes into account adaptive responses (e.g., refractoriness) not only conveys significant information, but the information benefit accounts for behaviorally important levels of stimulus discrimi...

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Section: Note Thatmentioning

confidence: 60%

Section: Note Thatmentioning

confidence: 99%

Section: Note Thatmentioning

confidence: 99%

Section: Note Thatmentioning

confidence: 99%

See 2 more Smart Citations

Biophysical information representation in temporally correlated spike trains

Nesse

Maler²,

Longtin³

2010

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

Self Cite

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show abstract

“…Recently, a novel kind of resonance effect in which the coherence of the signal can be minimized at a finite value of noise intensity has been found in many investigations [16][17][18][19][20]. It is termed as anticoherence resonance (ACR) or incoherence resonance.…”

Section: Introductionmentioning

confidence: 99%

Numerical study of the influence of recurrent connexions on the signaling in excitable systems: The dynamical effect of noise recycling

Niu

Zhou

et al. 2015

Applied Mathematical Modelling

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Recurrent connexions in dynamical systems provide not only multiple transduction pathway, but also the possibility of the recycling of noise, which may significantly influence the signal character. In this work, its effect is modeled in an excitable system as recycled noise, which includes a delayed stochastic component. Simulation results prove that the noise recycling delay can enhance the noise induced anticoherence resonance in some moderate delay scope. Further investigation suggests that the control effect may come from the additional time scale resonant match introduced by the recycling of noise.

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The Effects of Background Noise on a Biophysical Model of Olfactory Bulb Mitral Cells

Craft

2022

Bull Math Biol

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The spiking activity of mitral cells (MC) in the olfactory bulb is a key attribute in olfactory sensory information processing to downstream cortical areas. A more detailed understanding of the modulation of MC spike statistics could shed light on mechanistic studies of olfactory bulb circuits, and olfactory coding. We study the spike response of a recently developed single-compartment biophysical MC model containing 7 known ionic currents and calcium dynamics subject to constant current input with background white noise. We observe rich spiking dynamics even with constant current input, including multimodal peaks in the interspike interval distribution (ISI). Although weak to moderate background noise for a fixed current input does not change the firing rate much, the spike dynamics can change dramatically, exhibiting non-monotonic spike variability not commonly observed in standard neuron models. We explain these dynamics with a phenomenological model of the ISI probability density function.Our study clarifies some of the complexities of MC spiking dynamics.

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Oscillation Regularity in Noise-Driven Excitable Systems with Multi-Time-Scale Adaptation

Cited by 20 publications

References 8 publications

Biophysical information representation in temporally correlated spike trains

Biophysical information representation in temporally correlated spike trains

Numerical study of the influence of recurrent connexions on the signaling in excitable systems: The dynamical effect of noise recycling

The Effects of Background Noise on a Biophysical Model of Olfactory Bulb Mitral Cells

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