Impact of membrane bistability on dynamical response of neuronal populations

Wei, Wei; Wolf, Fred; Wang, Xiao Jing

doi:10.1103/physreve.92.032726

Cited by 9 publications

(5 citation statements)

References 51 publications

(19 reference statements)

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“…Dynamical response of neurons to noisy oscillatory inputs is fundamental for neural information processing [49][50][51]. Our results confirm that neural systems can also respond to the weak frequency-difference information through the mechanism of SR.…”

Section: Discussionsupporting

confidence: 71%

Frequency-difference-dependent stochastic resonance in neural systems

et al. 2017

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Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitationinhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequencydifference-dependent stochastic resonance in neural systems.

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Section: Discussionsupporting

confidence: 71%

Frequency-difference-dependent stochastic resonance in neural systems

et al. 2017

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“…Notably, the LIF FP methods have been used to obtain the linear response to a piecewise linear models [ 33 , 34 ]. In these works, the high frequency artifacts induced by the hard threshold are treated explicitly and removed.…”

Section: Methodsmentioning

confidence: 99%

Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

Touzel

Wolf

2015

PLoS Comput Biol

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The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and fewer have investigated its effects. To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics, we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model. In the Fokker-Planck approach, the dynamic gain is intractable. The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike. This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input. We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales. We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak. We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance, finding all possible intersections of the three. The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons.

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“…The electron pumping experiments have been performed in various semiconductor-based nanoscale devices [2][3][4]. More recently, the topological charge pump was realized in ultracold atoms in optical superlattices [5][6][7], and it was also extensively studied in theory [8][9][10][11][12][13][14][15].…”

mentioning

confidence: 99%

Topological charge pumping by a sliding moiré pattern

2020

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We study the adiabatic topological charge pumping driven by interlayer sliding in the moiré superlattices. We show that, when we slide a single layer of the twisted bilayer system relatively to the other, a moiré pattern flow and a quantized transport of electrons occurs. When the Fermi energy is in a spectral gap, the number of pumped charges in the interlayer sliding process is quantized to a sliding Chern number, which obeys a Diophantine equation analogous to the quantum Hall effect. We apply the argument to the twisted bilayer graphene, and find that energy gaps above and below the nearly-flat bands has non-zero sliding Chern numbers. When the Fermi energy is in either of those gaps, the slide-driven topological pumping occurs perpendicularly to the sliding direction.

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Impact of membrane bistability on dynamical response of neuronal populations

Cited by 9 publications

References 51 publications

Frequency-difference-dependent stochastic resonance in neural systems

Frequency-difference-dependent stochastic resonance in neural systems

Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

Topological charge pumping by a sliding moiré pattern

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