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Neuronal Synchrony in Complex-Valued Deep Networks [article]

David P. Reichert, Thomas Serre
2014 arXiv   pre-print
Thus, neuronal synchrony could be a flexible mechanism that fulfills multiple functional roles in deep networks.  ...  We introduce a neural network formulation based on complex-valued neuronal units that is not only biologically meaningful but also amenable to a variety of deep learning frameworks.  ...  Modeling neuronal synchrony with complex-valued units In deep networks, a neuronal unit receives inputs from other neurons with states vector x via synaptic weights vector w.  ... 
arXiv:1312.6115v5 fatcat:ak4datu4wjcxvl5pxrjrcnlmai

Failure of Delayed Feedback Deep Brain Stimulation for Intermittent Pathological Synchronization in Parkinson's Disease

Andrey Dovzhenok, Choongseok Park, Robert M. Worth, Leonid L. Rubchinsky, William W. Lytton
2013 PLoS ONE  
This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons.  ...  However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation  ...  For the parameter values corresponding to uncorrelated activity and intermittent synchrony desynchronization of the network was not usually achieved.  ... 
doi:10.1371/journal.pone.0058264 pmid:23469272 pmcid:PMC3585780 fatcat:ajn4hmuh7raexmbcek5iae44jq

Reinforcement Learning Framework for Deep Brain Stimulation Study [article]

Dmitrii Krylov, Remi Tachet, Romain Laroche, Michael Rosenblum, Dmitry V. Dylov
2020 arXiv   pre-print
Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e.g. Parkinson's.  ...  of neurons.  ...  Moreover, a large network of interacting neurons is a complex non-linear system, which, considering limitations of the hardware and the unknown biological pathway of the illness itself, calls for additional  ... 
arXiv:2002.10948v1 fatcat:5bldqgxdcfez5cqap7fjwsrnpu

Analytical condition for synchrony in a neural network with two periodic inputs

Yoichiro Hashizume, Osamu Araki
2013 Physical Review E  
The neurons in neural networks receive sensory inputs and top-down inputs from outside of the network.  ...  In this study, we apply a mean field theory to the neural network model with two periodic inputs in order to clarify the conditions of synchronies.  ...  INTRODUCTION Neurons in neural networks interact by synaptic connections.  ... 
doi:10.1103/physreve.87.012713 pmid:23410365 fatcat:fmpieva7lrccxhncmsloqy3nmy

Multi-Class Imbalanced Graph Convolutional Network Learning

Min Shi, Yufei Tang, Xingquan Zhu, David Wilson, Jianxun Liu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation  ...  Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful  ...  Moreover, a large network of interacting neurons is a complex non-linear system, which, considering limitations of the hardware and the unknown biological pathway of the illness itself, calls for additional  ... 
doi:10.24963/ijcai.2020/394 dblp:conf/ijcai/KrylovCLRD20 fatcat:luqod5aahzdcznmcavv2hiqqqe

Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly

Damien Depannemaecker, Luiz Eduardo Canton Santos, Antônio Márcio Rodrigues, Carla Alessandra Scorza, Fulvio Alexandre Scorza, Antônio-Carlos Guimarães de Almeida
2019 Neural Networks  
Learning in neural networks inspired by brain tissue has been studied for machine learning applications.  ...  In this work, we proposed simple rules for learning inspired by recent findings in machine learning adapted to a realistic spiking neural network.  ...  Due to the propagation dynamic in the network, when the last neurons activate, the first neurons are no longer active. This process explains the low values of synchrony observed.  ... 
doi:10.1016/j.neunet.2019.09.038 pmid:31841876 fatcat:gtwwsr2rgjggxemwgjho56hb6u

The response of the subthalamo-pallidal networks of the Basal Ganglia to oscillatory cortical input in Parkinson's disease

Sungwoo Ahn, S Zauber, Robert M Worth, Leonid L Rubchinsky
2014 BMC Neuroscience  
The analysis of these data reveals complex patters of correlations between synchrony in cortical circuits (which can be studied noninvasively) and synchrony in the basal ganglia circuits (which requires  ...  parameter values).  ... 
doi:10.1186/1471-2202-15-s1-p57 pmcid:PMC4126513 fatcat:lubng6fpfnd3xhdv3jzficm2hy

Emergence of global synchronization in directed excitatory networks of type I neurons [article]

Abolfazl Ziaeemehr, Mina Zarei, Aida Sheshbolouki
2019 arXiv   pre-print
The neuronal PRCs can be classified as having either purely positive values (type I) or distinct positive and negative regions (type II).  ...  The collective behaviour of neural networks depends on the cellular and synaptic properties of the neurons.  ...  In reality inhibitory and excitatory neurons work together to perform complex tasks.  ... 
arXiv:1909.04510v2 fatcat:j2sioqcnuzg2tpdsmbssjzi52y

Reinforcement learning for suppression of collective activity in oscillatory ensembles [article]

Dmitriy Krylov, Dmitry V. Dylov, Michael Rosenblum
2020 arXiv   pre-print
We report a model-agnostic synchrony control based on proximal policy optimization and two artificial neural networks in an Actor-Critic configuration.  ...  We present a use of modern data-based machine learning approaches to suppress self-sustained collective oscillations typically signaled by ensembles of degenerative neurons in the brain.  ...  INTRODUCTION Control of complex oscillatory networks is an important problem of nonlinear science, with a number of practical applications.  ... 
arXiv:1909.12154v2 fatcat:noyaupwzdzf3lpxdnzl5tftw5e

Neuronal Synchrony during Anesthesia: A Thalamocortical Model

Jane H. Sheeba, Aneta Stefanovska, Peter V.E. McClintock
2008 Biophysical Journal  
Changes in the degree of intra--ensemble and inter--ensemble synchrony imply that the neuronal ensembles inhibit information coding during deep anæsthesia and facilitate it during light anæsthesia.  ...  There is growing evidence in favour of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in  ...  The RE neurons thus form a network that surrounds the thalamus.  ... 
doi:10.1529/biophysj.108.134635 pmid:18586847 pmcid:PMC2527271 fatcat:wzwk5qyl5vfuhfwsndyuuz3ijq

Synch-Graph: Multisensory Emotion Recognition Through Neural Synchrony via Graph Convolutional Networks

Esma Mansouri-Benssassi, Juan Ye
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we present a novel bio-inspired approach based on neural synchrony in audio-visual multisensory integration in the brain, named Synch-Graph.  ...  We model multisensory interaction using spiking neural networks (SNN) and explore the use of Graph Convolutional Networks (GCN) to represent and learn neural synchrony patterns.  ...  In this paper, we novelly apply GCN in modelling neural synchrony to learn complex interaction patterns between synchronised neuron activities captured in a spiking neural network.  ... 
doi:10.1609/aaai.v34i02.5491 fatcat:v3544eg5azdnrfoojknbk3djtq

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

Huu Hoang, Eric J. Lang, Yoshito Hirata, Isao T. Tokuda, Kazuyuki Aihara, Keisuke Toyama, Mitsuo Kawato, Nicolas Schweighofer, Chris De Zeeuw
2020 PLoS Computational Biology  
Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions  ...  These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning  ...  deep-cerebellar cells are spontaneously active in the anesthetized animal.  ... 
doi:10.1371/journal.pcbi.1008075 pmid:32730255 pmcid:PMC7419012 fatcat:2ciu74uglrbkdcdhbgombmhhne

Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches

H. Yang, W. L. Shew, R. Roy, D. Plenz
2012 Journal of Neuroscience  
Ongoing interactions among cortical neurons often manifest as network-level synchrony.  ...  As network excitability was increased from low to high, we discovered three phenomena at an intermediate excitability level: (1) onset of synchrony, (2) maximized variability of synchrony, and (3) neuronal  ...  Here we studied spontaneously emerging network-level synchrony over a range of Figure 7 . Phase synchrony and neuronal avalanches in a network-level computational model of E-I neurons.  ... 
doi:10.1523/jneurosci.2771-11.2012 pmid:22262904 pmcid:PMC3319677 fatcat:ifrlt3u53zfp3fyqlqbtwvvw54

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons [article]

Huu Hoang, Eric J. Lang, Yoshito Hirata, Isao T. Tokuda, Kazuyuki Aihara, Keisuke Toyama, Mitsuo Kawato, Nicolas Schweighofer
2019 bioRxiv   pre-print
Here, we develop a modeling technique to estimate effective coupling strengths between inferior olive neurons from in vivo recordings of Purkinje cell complex spike activity in three different coupling  ...  In contrast, intermediate coupling strengths induce chaotic firing and increase the dimensionality of firing dynamics.  ...  In 559 Tang, T., Suh C. Y., Blenkinsop T. A., and Lang E. J. (2016). Synchrony is Key: Complex Spike 560 Inhibition of the Deep Cerebellar Nuclei.  ... 
doi:10.1101/542183 fatcat:n2adrsb5qfc75pp3f2f2rwiiqa

Autapses promote synchronization in neuronal networks

Huawei Fan, Yafeng Wang, Hengtong Wang, Ying-Cheng Lai, Xingang Wang
2018 Scientific Reports  
a high degree of synchrony in real neuronal networks with autapses.  ...  In particular, implementing a widely studied nonlinear neuron model on complex networks of different topologies, we assume the existence of autapses on a small fraction of the neurons and investigate quantitatively  ...  Our main finding is that, for a complex neuronal network, even the existence of autapses on a small fraction of the neurons can promote synchrony significantly.  ... 
doi:10.1038/s41598-017-19028-9 pmid:29330551 pmcid:PMC5766500 fatcat:vlwuzh2otrdythzakkmxpxbmh4
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