scholarly journals Synchronization of Neuronal Networks via Control Rank Pinning Scheme

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Qingying Miao ◽  
W. K. Wong ◽  
Dan Shan
Keyword(s):  
Simple Model ◽  
Coupled Oscillators ◽  
Neuronal Networks ◽  
Pinning Control ◽  
Cortical Network ◽  
Cortical Areas ◽  
Functional Community ◽  
Pinning Scheme

Recent studies have proposed the controlling regions in the corticocortical network of cats' brain at different scales. Here we study pinning control using a simple model of coupled oscillators assigned to cortical areas in the corticocortical network of cats' brain. We analyze control rank (CR) values of areas in the cortical network. It is found that most of the hubs have bigger CR values than other nodes in the same functional community. Moreover, we analyze the synchrony state of the functional communities in the cortical network, revealing that a community with a larger percentage of pinned nodes inside will be conducive to synchronization.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

Hydrodynamically coupled oscillators

2011 ◽  
Vol 677 ◽  
pp. 589-606 ◽  
Author(s):  
R. TIRON ◽  
E. KANSO ◽  
P. K. NEWTON
Keyword(s):  
Angular Momentum ◽  
Simple Model ◽  
Coupled Oscillators ◽  
Normal Modes ◽  
Mass Effect ◽  
Added Mass ◽  
Momentum Exchange ◽  
Ring Configuration ◽  
Curved Paths ◽  
Surrounding Fluid

A submerged spring–mass ring is analysed as a simple model for the way in which an underwater swimmer couples its body deformations to the surrounding fluid in order to accomplish locomotion. We adopt an inviscid, incompressible, irrotational assumption for the surrounding fluid and analyse the coupling response to various modes of excitation of the ring configuration. Due to the added mass effect, the surrounding fluid provides an environment which effectively couples the ‘normal modes’ of oscillation of the ring, leading to nonlinear trajectories if the ring is free to accelerate based on the effective forces the oscillations induce. Through a series of examples, we demonstrate various features that the model supports, including the locomotion on curved paths as a result of energy and angular momentum exchange with the surrounding fluid.

scholarly journals Maximizing the coding capacity of neuronal networks

2019 ◽  
Author(s):  
Sandeep Chowdhary ◽  
Collins Assisi
Keyword(s):  
Coupled Oscillators ◽  
Neuronal Networks ◽  
Spatiotemporal Patterns ◽  
Pulse Coupled Oscillators ◽  
Coding Capacity

Information in neuronal networks is encoded as spatiotemporal patterns of activity. The capacity of a network may thus be thought of as the number of stable spatiotemporal patterns it can generate. To understand what structural attributes of a network enable it to generate a profusion of stable patterns, we simulated an array of 9 × 9 neurons modelled as pulse-coupled oscillators. The structure of the network was inspired by the popular puzzle Sudoku such that its periodic responses mapped to solutions of the puzzle. Given that there are nearly a 109 possible Sudokus, this networks could possibly generate 109 spatiotemporal patterns. We show that the number of stable patterns were maximized when excitatory and inhibitory inputs to each neuron were balanced. When this balance was disrupted, only a subset of patterns with certain symmetries survived.

scholarly journals On Hybrid Adaptive and Pinning Consensus for Multiagent Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lianghao Ji ◽  
Yi Tang ◽  
Qun Liu
Keyword(s):  
Convergence Rate ◽  
Discrete Time ◽  
Continuous Time ◽  
Spanning Tree ◽  
Pinning Control ◽  
Control Laws ◽  
Numerical Examples ◽  
Multiagent Networks ◽  
Directed Spanning Tree ◽  
Pinning Scheme

The consensus problems for both continuous-time and discrete-time multiagent networks are deeply investigated by adopting hybrid adaptive and pinning control laws, respectively. Particularly, the topology of the networks needs to neither be symmetric nor contain a directed spanning tree and some useful criteria are addressed analytically. Simultaneously, a comprehensive pinning scheme is proposed as well which shows that the nodes with zero in-degree need to be pinned primarily in order to guarantee the system to achieve consensus, and then the nodes whose out-degrees are bigger than their in-degrees can give priority to be pinned compared to other nodes for improving the convergence rate of the system, whereas it is also interesting to find out that the regular rule does not always hold, that is, the more nodes are selected to be pinned, the faster the system will converge. Finally, the validity of our theoretical findings is verified by several numerical examples.

scholarly journals Time Regained: How the Human Brain Constructs Memory for Time

2017 ◽  
Author(s):  
Brendan I. Cohn-Sheehy ◽  
Charan Ranganath
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Event Structure ◽  
Cortical Network ◽  
Temporal Information ◽  
Temporal Contiguity ◽  
Temporal Context ◽  
List Type ◽  
Temporal Memory ◽  
Cortical Areas

AbstractLife’s episodes unfold against a context that changes with time. Recent neuroimaging studies have revealed significant findings about how specific areas of the human brain may support the representation of temporal information in memory. A consistent theme in these studies is that the hippocampus appears to play a central role in representing temporal context, as operationalized in neuroimaging studies of arbitrary lists of items, sequences of items, or meaningful, lifelike events. Additionally, activity in a posterior medial cortical network may reflect the representation of generalized temporal information for meaningful events. The hippocampus, posterior medial network, and other regions—particularly in prefrontal cortex—appear to play complementary roles in memory for temporal context.HighlightsThe hippocampus encodes information about temporal contiguity, order, and event structure.Posterior medial cortical areas represent order across meaningfully coherent events.Prefrontal and subcortical contributions to temporal memory deserve further study.

scholarly journals Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

2021 ◽  
Author(s):  
Ronaldo V. Nunes ◽  
Marcelo Bussotti Reyes ◽  
Jorge F. Mejias ◽  
Raphael Y. de Camargo
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Brain Regions ◽  
Cortical Network ◽  
Ground Truth Data ◽  
Cortical Areas ◽  
Electrophysiological Measurements ◽  
Flow Of Information

AbstractInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Correlation (GPDC), provide estimates of the causal influence between areas. However, such methods have a limitation because their estimates depend on the number of brain regions simultaneously recorded. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale mouse cortical network. The model contains 19 cortical areas modeled using spiking neural populations, and directed weights for long-range projections were obtained from a tract-tracing cortical connectome. We show that the GPDC estimates correlate positively with structural connectivity. Moreover, the correlation between structural and directed functional connectivity is comparable even when using only a few cortical areas for GPDC estimation, a typical scenario for electro-physiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

scholarly journals Control of Multiagent Systems: A Stochastic Pinning Viewpoint

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Guoliang Wang
Keyword(s):  
Multiagent Systems ◽  
Pinning Control ◽  
Noise Perturbation ◽  
Brownian Noise ◽  
Bounded Uncertainty ◽  
The Given ◽  
First Time ◽  
Pinning Scheme

A stochastic pinning approach for multiagent systems is developed, which guarantees such systems being almost surely stable. It is seen that the pinning is closely related to being a Bernoulli variable. It has been proved for the first time that a series of systems can be stabilized by a Brownian noise perturbation in terms of a pinning scheme. A new terminology named “stochastic pinning control” is introduced to describe the given pinning algorithm. Additionally, two general cases that the expectation of the Bernoulli variable with bounded uncertainty or being unknown are studied. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed methods.

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