Biophysical mechanism of signal encoding in an auditory neuron

Guo, Yitong; Zhou, Ping; Yao, Zhao; Ma, Jun

doi:10.1007/s11071-021-06770-z

Cited by 75 publications

(19 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2(a), the signal's spectrum after the fast Fourier transform should be uniformly distributed. The amplitude in the external signal can be dependent on the frequency, and it is defined by [59]:…”

Section: Modelmentioning

confidence: 99%

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Wang²,

Li³

et al. 2023

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

Section: Modelmentioning

confidence: 99%

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Wang²,

Li³

et al. 2023

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

“…To identify the aperiodic behavior that indicates chaos in the cell, we look at the action potential duration (APD). Since cardiac spikes take longer times than neuron spikes, APD is often the quantity of interest when assessing nonlinear properties of cardiac systems, while neuroscience applications often focus on the interspike interval (ISI) [44,45,46,47]. Fig.…”

Section: Generating Chaos In the Single Cellmentioning

confidence: 99%

Cardiac Reentry Modeled by Spatiotemporal Chaos in a Coupled Map Lattice

Stenzinger,

Tragtenberg

2022

Preprint

View full text Add to dashboard Cite

Arrhythmias are potentially fatal disruptions to the normal heart rhythm, but their underlying dynamics is still poorly understood. Theoretical modeling is an important tool to fill this gap. Typical studies often employ detailed multidimensional conductance-based models. We describe the cardiac muscle with a three-dimensional map-based membrane potential model in lattices. Although maps retain the biophysical behavior of cells and generate computationally efficient tissue models, few studies have used them to understand cardiac dynamics. Our study captures healthy and pathological behaviors with fewer parameters and simpler equations than conductance models. We successfully generalize results obtained previously with reaction-diffusion systems, showing how chaotic properties result in reentry, a pathological propagation of stimuli that evolves to arrhythmias with complex spatiotemporal features. The bifurcation diagram of the single cell is very similar to that obtained in a detailed conductance-based model. We find torsade de pointes, a clinical manifestation of some types of tachycardia in the electrocardiogram, using a generic sampling of the whole network during spiral waves. We also find a novel type of dynamical pattern, with wavefronts composed of synchronized cardiac plateaus and bursts. Our study provides the first in-depth look at the use of map-based models to simulate complex cardiac dynamics.

show abstract

“…Similarly, the optimal Gaussian white noise intensity was also found for signal propagation in FFN composed of hybrid neurons [20]. Guo et al found that there exists an appropriate noise intensity which can enhance the auditory effect through establishing nonlinear resonance, and benefit for the signal detection in auditory system [21].…”

mentioning

confidence: 93%

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

Dai

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network.

show abstract

Biophysical mechanism of signal encoding in an auditory neuron

Cited by 75 publications

References 59 publications

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Cardiac Reentry Modeled by Spatiotemporal Chaos in a Coupled Map Lattice

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

Contact Info

Product

Resources

About