Spike patterning of a stochastic phase model neuron given periodic inhibition

Nesse, William H.; Clark, Gregory A.; Bressloff, Paul C.

doi:10.1103/physreve.75.031912

Cited by 9 publications

(22 citation statements)

References 26 publications

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“…Note that we have not made an approximation with the δ function as in [46], since the ISI density without impulses cannot be derived in this way. Thus, the ISI density p Sp,Sp (T ) becomes…”

Section: G Interspike Interval Densitymentioning

confidence: 99%

“…Nesse et al [46] calculated the ISI density of the phase model with multiplicative noise by considering a population of neuronal oscillators. We extend their idea to the case in which the neuronal dynamics is written in terms of the stochastic differential equations.…”

Section: G Interspike Interval Densitymentioning

confidence: 99%

“…(8) and the full equation in Eq. (7) following the definition in [23,46]. Considering the case for which the nth impulse is added at φ n , the lifted angular coordinate from Eq.…”

Section: F Stochastic Rotation Numbermentioning

confidence: 99%

“…1 in [46]). Using the derivation method outlined in [46], p Sp,I m (τ ) for the phase equation is given as…”

Section: G Interspike Interval Densitymentioning

confidence: 99%

“…According to [46], the ISI density is derived in two steps: (1) calculation of the relative spike density that gives the time of the next input impulse arrival after a spike of the reduced model, and (2) calculation of the conditional ISI density relative to the first input impulse time. To calculate the ISI density, we make the same two assumptions as Nesse et al [46]. The first assumption is that an impulse does not produce the normalized angular coordinate shift across unity.…”

Section: G Interspike Interval Densitymentioning

confidence: 99%

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Global dynamics of a stochastic neuronal oscillator

Yamanobe

2013

Phys. Rev. E

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Nonlinear oscillators have been used to model neurons that fire periodically in the absence of input. These oscillators, which are called neuronal oscillators, share some common response structures with other biological oscillations such as cardiac cells. In this study, we analyze the dependence of the global dynamics of an impulse-driven stochastic neuronal oscillator on the relaxation rate to the limit cycle, the strength of the intrinsic noise, and the impulsive input parameters. To do this, we use a Markov operator that both reflects the density evolution of the oscillator and is an extension of the phase transition curve, which describes the phase shift due to a single isolated impulse. Previously, we derived the Markov operator for the finite relaxation rate that describes the dynamics of the entire phase plane. Here, we construct a Markov operator for the infinite relaxation rate that describes the stochastic dynamics restricted to the limit cycle. In both cases, the response of the stochastic neuronal oscillator to time-varying impulses is described by a product of Markov operators. Furthermore, we calculate the number of spikes between two consecutive impulses to relate the dynamics of the oscillator to the number of spikes per unit time and the interspike interval density. Specifically, we analyze the dynamics of the number of spikes per unit time based on the properties of the Markov operators. Each Markov operator can be decomposed into stationary and transient components based on the properties of the eigenvalues and eigenfunctions. This allows us to evaluate the difference in the number of spikes per unit time between the stationary and transient responses of the oscillator, which we show to be based on the dependence of the oscillator on past activity. Our analysis shows how the duration of the past neuronal activity depends on the relaxation rate, the noise strength, and the impulsive input parameters.

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Section: G Interspike Interval Densitymentioning

confidence: 99%

Section: G Interspike Interval Densitymentioning

confidence: 99%

“…(8) and the full equation in Eq. (7) following the definition in [23,46]. Considering the case for which the nth impulse is added at φ n , the lifted angular coordinate from Eq.…”

Section: F Stochastic Rotation Numbermentioning

confidence: 99%

“…1 in [46]). Using the derivation method outlined in [46], p Sp,I m (τ ) for the phase equation is given as…”

Section: G Interspike Interval Densitymentioning

confidence: 99%

Section: G Interspike Interval Densitymentioning

confidence: 99%

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Global dynamics of a stochastic neuronal oscillator

Yamanobe

2013

Phys. Rev. E

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Relative spike timing in stochastic oscillator networks of the Hermissenda eye

Nesse

Clark

2010

Biol Cybern

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The role of relative spike timing on sensory coding and stochastic dynamics of small pulse-coupled oscillator networks is investigated physiologically and mathematically, based on the small biological eye network of the marine invertebrate Hermissenda. Without network interactions, the five inhibitory photoreceptors of the eye network exhibit quasi-regular rhythmic spiking; in contrast, within the active network, they display more irregular spiking but collective network rhythmicity. We investigate the source of this emergent network behavior first analyzing the role of relative input to spike-timing relationships in individual cells. We use a stochastic phase oscillator equation to model photoreceptor spike sequences in response to sequences of inhibitory current pulses. Although spike sequences can be complex and irregular in response to inputs, we show that spike timing is better predicted if relative timing of spikes to inputs is accounted for in the model. Further, we establish that greater noise levels in the model serve to destroy network phase-locked states that induce non-monotonic stimulus rate-coding, as predicted in Butson and Clark (J Neurophysiol 99:146-154, 2008a; J Neurophysiol 99:155-165, 2008b). Hence, rate-coding can function better in noisy spiking cells relative to non-noisy cells. We then study how relative input to spike-timing dynamics of single oscillators contribute to network-level dynamics. Relative timing interactions in the network sharpen the stimulus window that can trigger a spike, affecting stimulus encoding. Also, we derive analytical inter-spike interval distributions of cells in the model network, revealing that irregular Poisson-like spike emission and collective network rhythmicity are emergent properties of network dynamics, consistent with experimental observations. Our theoretical results generate experimental predictions about the nature of spike patterns in the Hermissenda eye.

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How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment

et al. 2023

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Objective. While brain stimulation therapies such as deep brain stimulation for Parkinson’s disease (PD) can be effective, they have yet to reach their full potential across neurological disorders. Entraining neuronal rhythms using rhythmic brain stimulation has been suggested as a new therapeutic mechanism to restore neurotypical behaviour in conditions such as chronic pain, depression, and Alzheimer’s disease. However, theoretical and experimental evidence indicate that brain stimulation can also entrain neuronal rhythms at sub- and super-harmonics, far from the stimulation frequency. Crucially, these counterintuitive effects could be harmful to patients, for example by triggering debilitating involuntary movements in PD. We therefore seek a principled approach to selectively promote rhythms close to the stimulation frequency, while avoiding potential harmful effects by preventing entrainment at sub- and super-harmonics. Approach. Our open-loop approach to selective entrainment, dithered stimulation, consists in adding white noise to the stimulation period. Main results. We theoretically establish the ability of dithered stimulation to selectively entrain a given brain rhythm, and verify its efficacy in simulations of uncoupled neural oscillators, and networks of coupled neural oscillators. Furthermore, we show that dithered stimulation can be implemented in neurostimulators with limited capabilities by toggling within a finite set of stimulation frequencies. Significance. Likely implementable across a variety of existing brain stimulation devices, dithering-based selective entrainment has potential to enable new brain stimulation therapies, as well as new neuroscientific research exploiting its ability to modulate higher-order entrainment.

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Spike patterning of a stochastic phase model neuron given periodic inhibition

Cited by 9 publications

References 26 publications

Global dynamics of a stochastic neuronal oscillator

Global dynamics of a stochastic neuronal oscillator

Relative spike timing in stochastic oscillator networks of the Hermissenda eye

How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment

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