Small-world neural network

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August undefined, 2024

WebAbstract. We study a model of associative memory based on a neural network with small-world structure. The eﬃcacy of the network to retrieve one of the stored patterns exhibits a phase transition at a ﬁnite value of the disorder. The more ordered networks are unable to recover the patterns, and are always attracted to mixture states. WebJun 15, 2014 · A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks. This paper presents two novel small-world neural networks, the Watts-Strogatz small-world based on a BP neural network (WSBP) and a Newman-Watts small-world neural network based on a BP neural network …

Impact of small-world network topology on the conventional artificial …

WebSep 21, 2024 · This paper addresses the problem of nonlinear and dotted defect detection for multi-vision-based mask inspection systems in mask manufacturing lines. As the mask production amounts increased due to the spread of COVID-19 around the world, the mask inspection systems require more efficient defect detection algorithms. However, the … WebOct 19, 2024 · A small-world network refers to an ensemble of networks in which the mean geodesic (i.e., shortest-path) distance between nodes increases sufficiently slowly as a function of the number of nodes in the network. inclination\u0027s nc

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WebMar 26, 2013 · Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. ... WebJan 7, 2001 · The “small-world networks” model described recently by Watts and Strogatz provides an interesting approach to this issue. In this paper, we show that associative memory networks with... WebFeb 23, 2016 · The models are utilized to simulate a subset of units recorded from a large biological neural network using multiple electrodes. Two metrics for quantifying the small-world-ness both suggest that ... inclination\u0027s nd

Fault tolerant small-world cellular neural networks for intermitted ...

SWNet: Small-World Neural Networks and Rapid …

WebSep 12, 2016 · Then, the dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. By tuning the coupling strength, synchronization of two spiking or bursting neurons are simulated. Furthermore, the spatiotemporal behavior of a small-world neuronal network using map-based model is studied. WebApr 9, 2024 · Small-world graphs are known to possess a specific connectivity structure that enables enhanced signal propagation among nodes. Our small-world models, called … incoterm2020与incoterm2010的对比和分析WebFeb 25, 2016 · To quantify the extent to which a network displays small-world structure, we define the Small-World Propensity, ϕ, to reflect the deviation of a network’s clustering coefficient, Cobs, and... incoterm2020

"WebFeb 1, 2016 · We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. " - Small-world neural network

Small-world neural network

(PDF) Small-World Brain Networks - ResearchGate

WebApr 13, 2024 · Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this … WebOct 1, 2024 · Small-world network is an intermediate model between regular and random networks. It is defined as a complex network structure which is highly clustered as the …

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WebAug 1, 2024 · A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks. This paper presents two … WebNov 11, 2024 · The goal of deep anomaly detection is to identify abnormal data by utilizing a deep neural network trained by a normal training dataset. In general, industrial visual anomaly detection problems distinguish normal and abnormal data through small morphological differences, such as cracks and stains. Nevertheless, most existing …

WebJul 14, 2024 · In this study, we proposed a spiking neural network with small-world topology (SWSNN), where Izhikevich neuron models and synaptic plasticity models with excitatory and inhibitory synapses are introduced to represent nodes and … WebJan 1, 2024 · An excitatory small-world network of subthreshold neurons is considered.Effect of spike-timing-dependent plasticity on spike synchronization is investigated.Both the additive and the multiplicative case are considered.A Matthew effect in synaptic plasticity is found to occur.Emergence of potentiation and depression is …

WebSep 26, 2007 · To understand the influence of structure on the function of neural networks, we study the storage capacity and the retrieval time of Hopfield-type neural networks for four network structures: regular, small world, random networks generated by the Watts-Strogatz (WS) model, and the same network as the neural network of the nematode … Small-world networks tend to contain cliques, and near-cliques, meaning sub-networks which have connections between almost any two nodes within them. This follows from the defining property of a high clustering coefficient. Secondly, most pairs of nodes will be connected by at least one short path. This … See more A small-world network is a mathematical graph in which most nodes are not neighbors of one another, but the neighbors of any given node are likely to be neighbors of each other. Due to this, most neighboring … See more Small-world properties are found in many real-world phenomena, including websites with navigation menus, food webs, electric power grids, metabolite processing networks, networks of brain neurons, voter networks, telephone call graphs, and airport networks. … See more It is hypothesized by some researchers, such as Barabási, that the prevalence of small world networks in biological systems may reflect … See more Applications to sociology The advantages to small world networking for social movement groups are their resistance to change due to the filtering apparatus of using … See more In another example, the famous theory of "six degrees of separation" between people tacitly presumes that the domain of discourse is the set of people alive at any one time. The number of degrees of separation between Albert Einstein and Alexander the Great is … See more The main mechanism to construct small-world networks is the Watts–Strogatz mechanism. Small-world networks can also be introduced with time-delay, which will not only produce fractals but also chaos under the right conditions, … See more • Barabási–Albert model – algorithm for generating random networks • Climate as complex networks – Conceptual model to generate insight into climate science • Dual-phase evolution – Process that drives self-organization within complex adaptive systems See more

WebFeb 1, 2016 · We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN.

WebIn this paper a novel method is presented to solve this problem. The method breaks through the traditional methods which only determine the hidden structure and also learns the topological connectivity so that the connectivity structure has small world characteristic. inclination\u0027s nbWebApr 9, 2024 · Our small-world models, called SWNets, provide several intriguing benefits: they facilitate data (gradient) flow within the network, enable feature-map reuse by adding … inclination\u0027s nhWebSmall-world neural networks, as well as diluted Hopfield networks, are constructed by using matrix decomposition and a connection elimination strategy. It is shown that, to a certain … inclination\u0027s ngWebSep 6, 2024 · We employ Watt and Strogatz’s small-world model as the topology for the Hopfield neural network and conduct computer simulations. We observe that the random … incoterm2020 fobWebFeb 28, 2005 · A small-world network is defined by two properties. First, the average clustering coefficient C is larger than for a corresponding random network with the same … incoterm2020中文版WebApr 1, 2024 · The small-world model proposed by Watts and Strogatz (1998). The edge of each node in a regular network (a) is reconnected to another randomly selected node with a probability . A network exhibits a small-world topology if is around 0.1 (b), while a network with becomes a random network (c). inclination\u0027s nkWebWe developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant ... incoterm90