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Neuronal Synchrony in Complex-Valued Deep Networks
[article]
2014
arXiv
pre-print
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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
2013
PLoS ONE
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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]
2020
arXiv
pre-print
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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
2013
Physical Review E
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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
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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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
2019
Neural Networks
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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
2014
BMC Neuroscience
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2008
Biophysical Journal
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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
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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
2020
PLoS Computational Biology
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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
2012
Journal of Neuroscience
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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]
2019
bioRxiv
pre-print
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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
2018
Scientific Reports
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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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