Effects of topologies on signal propagation in feedforward networks

Zhao, Jia; Qin, Ying Mei; Qiu, Yan

doi:10.1063/1.4999996

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Interestingly, this range of noise (std of membrane fluctuations (no stimulus) ≈ 4 mV for std of noise = 25 pA) is in agreement with the level of background synaptic noise observed in-vivo high-conductance state (Destexhe et al, 2003 ). Thus, from a system level perspective and in the context of consistent information transfer, one can conclude that stochastic resonance might occur in an FFN with the optimal level of background synaptic noise (see also the effect of network topologies on stochastic resonance in FFNs Zhao et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate

Hasanzadeh

Rezaei

Faraz

et al. 2020

Front. Comput. Neurosci.

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Reliable propagation of slow-modulations of the firing rate across multiple layers of a feedforward network (FFN) has proven difficult to capture in spiking neural models. In this paper, we explore necessary conditions for reliable and stable propagation of time-varying asynchronous spikes whose instantaneous rate of changes—in fairly short time windows [20–100] msec—represents information of slow fluctuations of the stimulus. Specifically, we study the effect of network size, level of background synaptic noise, and the variability of synaptic delays in an FFN with all-to-all connectivity. We show that network size and the level of background synaptic noise, together with the strength of synapses, are substantial factors enabling the propagation of asynchronous spikes in deep layers of an FFN. In contrast, the variability of synaptic delays has a minor effect on signal propagation.

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Section: Discussionmentioning

confidence: 99%

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate

Hasanzadeh

Rezaei

Faraz

et al. 2020

Front. Comput. Neurosci.

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“…For this purpose it would be useful to implement FFT [159,160] and wavelet analyses [161,162] of the oscillations of nodes of the networks. Several methods exist which demonstrate how analysis of signal transmission may work in neuronal networks [163][164][165][166]. Transmission of propagating signals can be analyzed in terms of spreading pattern exhibiting cross networks communications as well as a straight forward transmission with minimal interaction through a certain route of a connectome.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Reaction-diffusion models in weighted and directed connectomes

Schmitt

Nitzsche²,

Eipert³

et al. 2022

PLoS Comput Biol

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Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances. Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway. In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

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“…Network topology can reflect the connection form among neurons and affect network functions. A study showed that the distribution of neural connections could affect the propagation of firing rate (FR) and firing pattern in the feed-forward networks [27]. However, an enormous amount of evidence based on fMRI and EEG investigations have suggested that the biological brain function network has a scale-free property and/or small-world property [28,29].…”

Section: Introductionmentioning

confidence: 99%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

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With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

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Effects of topologies on signal propagation in feedforward networks

Cited by 12 publications

References 32 publications

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate

Reaction-diffusion models in weighted and directed connectomes

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

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