Astrocyte-induced intermittent synchronization of neurons in a minimal network

2020 ◽  
Vol 138 ◽  
pp. 109951 ◽  
Author(s):  
S Yu Makovkin ◽  
I V Shkerin ◽  
S Yu Gordleeva ◽  
M V Ivanchenko
Keyword(s):  
Minimal Network

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

scholarly journals Distributed Joins and Data Placement for Minimal Network Traffic

2018 ◽  
Vol 43 (3) ◽  
pp. 1-45 ◽  
Author(s):  
Orestis Polychroniou ◽  
Wangda Zhang ◽  
Kenneth A. Ross
Keyword(s):  
Network Traffic ◽  
Data Placement ◽  
Minimal Network

Bursting in Inhibitory Interneuronal Networks: A Role for Gap-Junctional Coupling

1999 ◽  
Vol 81 (3) ◽  
pp. 1274-1283 ◽  
Author(s):  
F. K. Skinner ◽  
L. Zhang ◽  
J. L. Perez Velazquez ◽  
P. L. Carlen
Keyword(s):  
Theoretical Work ◽  
Oscillatory Behavior ◽  
Minimal Network ◽  
Junctional Coupling ◽  
Synchronization Mechanisms ◽  
The Individual ◽  
Gap Junctional ◽  
Gap Junctional Coupling ◽  
Inhibitory Networks

Bursting in inhibitory interneuronal networks: a role for gap-junctional coupling. Much work now emphasizes the concept that interneuronal networks play critical roles in generating synchronized, oscillatory behavior. Experimental work has shown that functional inhibitory networks alone can produce synchronized activity, and theoretical work has demonstrated how synchrony could occur in mutually inhibitory networks. Even though gap junctions are known to exist between interneurons, their role is far from clear. We present a mechanism by which synchronized bursting can be produced in a minimal network of mutually inhibitory and gap-junctionally coupled neurons. The bursting relies on the presence of persistent sodium and slowly inactivating potassium currents in the individual neurons. Both GABAA inhibitory currents and gap-junctional coupling are required for stable bursting behavior to be obtained. Typically, the role of gap-junctional coupling is focused on synchronization mechanisms. However, these results suggest that a possible role of gap-junctional coupling may lie in the generation and stabilization of bursting oscillatory behavior.

scholarly journals A scalar Poincaré map for struggling in Xenopus tadpoles

2019 ◽  
Author(s):  
Mark Olenik ◽  
Conor Houghton
Keyword(s):  
Fixed Points ◽  
Activity Patterns ◽  
Synaptic Depression ◽  
The Body ◽  
Synaptic Conductance ◽  
Inhibitory Synapses ◽  
Minimal Network ◽  
Cell Network ◽  
Neuronal Mechanisms ◽  
The Central Nervous System

AbstractSynaptic plasticity is widely found in many areas of the central nervous system. In particular, it is believed that synaptic depression can act as a mechanism to allow simple networks to generate a range of different firing patterns. The simplicity of the locomotor circuit in hatchling Xenopus tadpoles provides an excellent place to understand such basic neuronal mechanisms. Depending on the nature of the external stimulus, tadpoles can generate two types of behaviours: swimming when touched and slower, stronger struggling movements when held. Struggling is associated with rhythmic bends of the body and is accompanied by anti-phase bursts in neurons on each side of the spinal cord. Bursting in struggling is thought to be governed by a short-term synaptic depression of inhibition. To better understand burst generation in struggling, we study a minimal network of two neurons coupled through depressing inhibitory synapses. Depending on the strength of the synaptic conductance between the two neurons, such a network can produce symmetric n - n anti-phase bursts, where neurons fire n spikes in alternation, with the period of such solutions increasing with the strength of the synaptic conductance. Using a fast/slow analysis, we reduce the multidimensional network equations to a scalar Poincaé burst map. This map tracks the state of synaptic depression from one burst to the next, and captures the complex bursting dynamics of the network. Fixed points of this map are associated with stable burst solutions of the full network model, and are created through fold bifurcations of maps. We prove that the map has an infinite number of stable fixed points for a finite coupling strength interval, suggesting that the full two-cell network also can produce n - n bursts for arbitrarily large n. Our findings further support the hypothesis that synaptic depression can enrich the variety of activity patterns a neuronal network generates.

Fold-Pitchfork Bifurcation, Arnold Tongues and Multiple Chaotic Attractors in a Minimal Network of Three Sigmoidal Neurons

2018 ◽  
Vol 28 (10) ◽  
pp. 1850123 ◽  
Author(s):  
Yo Horikawa ◽  
Hiroyuki Kitajima ◽  
Haruna Matsushita
Keyword(s):  
Periodic Solutions ◽  
Period Doubling ◽  
Pitchfork Bifurcation ◽  
Quasiperiodic Solution ◽  
Chaotic Attractors ◽  
Dimensional Parameter ◽  
Chaotic Neural Networks ◽  
Minimal Network ◽  
Arnold Tongues ◽  
Period Doubling Bifurcations

Bifurcations and chaos in a network of three identical sigmoidal neurons are examined. The network consists of a two-neuron oscillator of the Wilson–Cowan type and an additional third neuron, which has a simpler structure than chaotic neural networks in the previous studies. A codimension-two fold-pitchfork bifurcation connecting two periodic solutions exists, which is accompanied by the Neimark–Sacker bifurcation. A stable quasiperiodic solution is generated and Arnold’s tongues emanate from the locus of the Neimark–Sacker bifurcation in a two-dimensional parameter space. The merging, splitting and crossing of the Arnold tongues are observed. Further, multiple chaotic attractors are generated through cascades of period-doubling bifurcations of periodic solutions in the Arnold tongues. The chaotic attractors grow and are destroyed through crises. Transient chaos and crisis-induced intermittency due to the crises are also observed. These quasiperiodic solutions and chaotic attractors are robust to small asymmetry in the output function of neurons.

scholarly journals Regulation of exit from mitosis in multinucleate Ashbya gossypii cells relies on a minimal network of genes

2011 ◽  
Vol 22 (17) ◽  
pp. 3081-3093 ◽  
Author(s):  
Mark R. Finlayson ◽  
A. Katrin Helfer-Hungerbühler ◽  
Peter Philippsen
Keyword(s):  
Regulatory Networks ◽  
Temporal Constraints ◽  
Ashbya Gossypii ◽  
Metaphase Arrest ◽  
Minimal Network ◽  
Filamentous Ascomycete ◽  
Low Degree ◽  
Mitotic Exit Network ◽  
Early Anaphase ◽  
Core Components

In Saccharomyces cerevisiae, mitosis is coupled to cell division by the action of the Cdc fourteen early anaphase release (FEAR) and mitotic exit network (MEN) regulatory networks, which mediate exit from mitosis by activation of the phosphatase Cdc14. The closely related filamentous ascomycete Ashbya gossypii provides a unique cellular setting to study the evolution of these networks. Within its multinucleate hyphae, nuclei are free to divide without the spatial and temporal constraints described for budding yeast. To investigate how this highly conserved system has adapted to these circumstances, we constructed a series of mutants lacking homologues of core components of MEN and FEAR and monitored phenomena such as progression through mitosis and Cdc14 activation. MEN homologues in A. gossypii were shown to have diverged from their anticipated role in Cdc14 release and exit from mitosis. We observed defects in septation, as well as a partial metaphase arrest, in Agtem1Δ, Agcdc15Δ, Agdbf2/dbf20Δ, and Agmob1Δ. A. gossypii homologues of the FEAR network, on the other hand, have a conserved and more pronounced role in regulation of the M/G1 transition. Agcdc55Δ mutants are unable to sequester AgCdc14 throughout interphase. We propose a reduced model of the networks described in yeast, with a low degree of functional redundancy, convenient for further investigations into these networks.

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