Temporal Coherence Transitions Induced by Channel Noise in Scale-Free Neuronal Networks with Time Delay

2017 ◽  
Vol 16 (04) ◽  
pp. 1750031 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong
Keyword(s):  
Information Processing ◽  
Time Delay ◽  
Noise Intensity ◽  
Neuronal Networks ◽  
Temporal Coherence ◽  
Neural Systems ◽  
Channel Noise ◽  
Scale Free ◽  
Regulation Effect ◽  
Insight Into

In this paper, we study the effect of channel noise on the temporal coherence of scale-free Hodgkin–Huxley neuronal networks with time delay. It is found that the temporal coherence of the neuronal networks changes as channel noise intensity is varied in different ways depending on the range of channel noise intensity. The temporal coherence monotonically decreases with the increase of channel noise intensity for too small or too big channel noise intensity. However, for intermediate channel noise intensity it intermittently and rapidly becomes high and low as channel noise intensity is varied, exhibiting temporal coherence transitions. Moreover, this phenomenon is dependent on coupling strength and network average degree and becomes strongest when they are optimal. This result shows that channel noise has a regulation effect on the temporal coherence of the delayed neuronal networks by inducing temporal coherence transitions. This provides a new insight into channel noise for the information processing and transmission in neural systems.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

2018 ◽  
Vol 17 (02) ◽  
pp. 1850011 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong ◽  
Baoying Wang
Keyword(s):  
Time Delay ◽  
Noise Intensity ◽  
Neuronal Networks ◽  
Spike Timing ◽  
Average Degree ◽  
Neural Systems ◽  
Channel Noise ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

Effect of Channel Block on Multiple Coherence Resonance Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

2018 ◽  
Vol 17 (04) ◽  
pp. 1850036
Author(s):  
Huijuan Xie ◽  
Yubing Gong
Keyword(s):  
Time Delay ◽  
Neuronal Networks ◽  
Spike Timing ◽  
Neural Systems ◽  
Channel Block ◽  
Coherence Resonance ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Insight Into

In this paper, we study effect of channel block (CB) on multiple coherence resonance (MCR) in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that potassium CB suppresses MCR, but sodium CB can enhance MCR, and there is optimal sodium CB level by which MCR becomes most pronounced. In addition, STDP has a significant influence on the effect of CB on MCR. As adjusting rate [Formula: see text] of STDP increases, for potassium CB there is proper [Formula: see text] by which MCR is most pronounced; however, for sodium CB MCR is reduced. These findings could provide a new insight into effect of CB on information processing in neural systems.

Effect of Autaptic Activity on Intrinsic Coherence Resonance in Newman–Watts Networks of Stochastic Hodgkin–Huxley Neurons

2016 ◽  
Vol 15 (02) ◽  
pp. 1650016 ◽  
Author(s):  
Qi Wang ◽  
Yubing Gong
Keyword(s):  
Information Processing ◽  
Coupling Strength ◽  
Neuronal Networks ◽  
Neural Systems ◽  
Channel Noise ◽  
Coherence Resonance ◽  
Optimal Coupling

In this paper, we study the effect of autaptic activity on intrinsic coherence resonance (CR) induced by channel noise in Newman–Watts (NW) networks of stochastic Hodgkin–Huxley (HH) neurons. It is found that autaptic strength and autaptic delay have a big effect on the intrinsic CR. As autaptic strength increases, there is optimal autaptic strength by which the intrinsic CR is most highly enhanced. Autaptic delay can enhance, reduce, or destroy the intrinsic CR, depending on the delay length. Moreover, there are optimal coupling strength and network randomness by which autaptic activity can most highly enhance the intrinsic CR. These results show that autaptic activity has different effects on the intrinsic CR in the neuronal networks, and it can most highly enhance the intrinsic CR at optimal coupling strength and network randomness. These findings could find potential implications of channel noise and autaptic activity for the information processing and transmission in neural systems.

ENHANCEMENT OF INTRINSIC SPIKING COHERENCE BY EXTERNAL NON-GAUSSIAN NOISE IN A STOCHASTIC HODGKIN–HUXLEY NEURON

2011 ◽  
Vol 10 (04) ◽  
pp. 359-369 ◽  
Author(s):  
LI WANG ◽  
YUBING GONG ◽  
XIU LIN
Keyword(s):  
Information Processing ◽  
Gaussian Distribution ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Temporal Coherence ◽  
Channel Noise ◽  
Coherence Resonance ◽  
Non Gaussian ◽  
Proper Values ◽  
Membrane Patch

In this paper, we study the effect of external non-Gaussian noise on the temporal coherence of the intrinsic spiking induced by the channel noise in a stochastic Hodgkin–Huxley neuron. It is found that, for a sufficiently large membrane patch, the intrinsic spiking coherence can be enhanced by the proper values of non-Gaussian noise's strength, correlation time, or deviation from Gaussian distribution. And that the intrinsic spiking can exhibit coherence resonance when the noise's strength is optimal. This implies that the channel noise-induced intrinsic spiking may become more or the most ordered in time with the assistance of the external non-Gaussian noise. These results show that the external non-Gaussian noise can play a constructive role for improving the time precision of information processing in stochastic neurons.

Hierarchies, similarity, and interactivity in object recognition: “Category-specific” neuropsychological deficits

2001 ◽  
Vol 24 (3) ◽  
pp. 453-476 ◽  
Author(s):  
Glyn W. Humphreys ◽  
Emer M. E. Forde
Keyword(s):  
Information Processing ◽  
Object Recognition ◽  
Semantic Knowledge ◽  
Visual Similarity ◽  
Neural Systems ◽  
Living Things ◽  
New Processing ◽  
Different Levels ◽  
Insight Into ◽  
Processing Framework

Category-specific impairments of object recognition and naming are among the most intriguing disorders in neuropsychology, affecting the retrieval of knowledge about either living or nonliving things. They can give us insight into the nature of our representations of objects: Have we evolved different neural systems for recognizing different categories of object? What kinds of knowledge are important for recognizing particular objects? How does visual similarity within a category influence object recognition and representation? What is the nature of our semantic knowledge about different objects? We review the evidence on category-specific impairments, arguing that deficits even for one class of object (e.g., living things) cannot be accounted for in terms of a single information processing disorder across all patients; problems arise at contrasting loci in different patients. The same apparent pattern of impairment can be produced by damage to different loci. According to a new processing framework for object recognition and naming, the hierarchical interactive theory (HIT), we have a hierarchy of highly interactive stored representations. HIT explains the variety of patients in terms of (1) lesions at different levels of processing and (2) different forms of stored knowledge used both for particular tasks and for particular categories of object.

Optimization of Intrinsic Stochastic Resonance in Adaptive Newman–Watts Network of Channel Blocked Hodgkin–Huxley Neurons

2014 ◽  
Vol 13 (04) ◽  
pp. 1450026
Author(s):  
Qi Wang ◽  
Yubing Gong ◽  
Yanan Wu
Keyword(s):  
Information Processing ◽  
Stochastic Resonance ◽  
Neural Systems ◽  
Channel Noise ◽  
Channel Blocking ◽  
Adaptive Coupling ◽  
Changing Rate ◽  
Sodium And Potassium ◽  
Fixed Coupling

In this paper, we study stochastic resonance (SR) induced by channel noise in adaptive weighted Newman–Watts networks of Hodgkin–Huxley neurons with channel blocking (CB). It is found that the intrinsic SR is dependent on adaptive coupling and is strongly enhanced when the changing rate of adaptive coupling is optimal, and this phenomenon is independent of sodium and potassium CB levels. As CB increases, the channel noise for SR decreases, but the strength of intrinsic SR nearly does not change in the presence of adaptive coupling, which is different from the case for fixed coupling. These results show that intrinsic SR can be enhanced and optimized by adaptive coupling, and CB's effect on the intrinsic SR can be reduced by adaptive coupling. This implies that adaptive coupling could more efficiently improve the time precision of information processing in neural systems.

Impact of Partial Time Delay on Temporal Dynamics of Watts–Strogatz Small-World Neuronal Networks

2017 ◽  
Vol 27 (07) ◽  
pp. 1750112 ◽  
Author(s):  
Hao Yan ◽  
Xiaojuan Sun
Keyword(s):  
Time Delay ◽  
Firing Rate ◽  
Neuronal Networks ◽  
Temporal Dynamics ◽  
Great Influence ◽  
Small World ◽  
Temporal Coherence ◽  
Small Time ◽  
Neuronal Firing ◽  
Two Parameters

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

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