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相关论文: Robustness and Enhancement of Neural Synchronizati…

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Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure…

定量方法 · 定量生物学 2021-04-26 Joel Zirkle , Leonid L Rubchinsky

The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale…

神经元与认知 · 定量生物学 2016-08-02 Gabriel Koch Ocker , Brent Doiron

Spiking neural networks (SNNs) promise energy-efficient computation by mimicking biological neural dynamics, yet existing plasticity rules focus on isolated spike pairs and fail to leverage the synchronous activity patterns that drive…

神经与进化计算 · 计算机科学 2025-08-26 Yuchen Tian , Assel Kembay , Samuel Tensingh , Nhan Duy Truong , Jason K. Eshraghian , Omid Kavehei

Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons…

神经元与认知 · 定量生物学 2023-08-15 Marius E. Yamakou , Mathieu Desroches , Serafim Rodrigues

We consider the Watts-Strogatz small-world network consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity…

神经元与认知 · 定量生物学 2017-08-16 Sang-Yoon Kim , Woochang Lim

We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the Ca2+ concentration in the dendritic spine of the…

神经元与认知 · 定量生物学 2015-02-26 Rodrigo Echeveste , Claudius Gros

In this paper, we investigated the neural spikes synchronisation in a neural network with synaptic plasticity and external perturbation. In the simulations the neural dynamics is described by the Hodgkin Huxley model considering chemical…

In neuroscience, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called synapses and represented by a scalar value, the synaptic weight. A Spike-Timing Dependent Plasticity (STDP) rule is a…

概率论 · 数学 2021-11-17 Philippe Robert , Gaetan Vignoud

We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neural cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two different…

神经元与认知 · 定量生物学 2021-11-16 Philippe Robert , Gaëtan Vignoud

In neuroscience, learning and memory are usually associated to long-term changes of neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called {\em…

概率论 · 数学 2021-06-10 Philippe Robert , Gaetan Vignoud

Spike Timing Dependent Plasticity is form of learning that has been demonstrated in real cortical tissue, but attempts to use it for artificial systems have not produced good results. This paper seeks to remedy this with two significant…

神经与进化计算 · 计算机科学 2020-07-01 Simon Davidson , Stephen B. Furber , Oliver Rhodes

The brain has the phenomenal ability to reorganize itself by forming new connections among neurons and by pruning others. The so-called neural or brain plasticity facilitates the modification of brain structure and function over different…

Spike-Timing-Dependent Plasticity (STDP) provides a biologically grounded learning rule for spiking neural networks (SNNs), but its reliance on precise spike timing and pairwise updates limits fast learning of weights. We introduce a…

神经与进化计算 · 计算机科学 2026-01-14 Gouri Lakshmi S , Athira Chandrasekharan , Harshit Kumar , Muhammed Sahad E , Bikas C Das , Saptarshi Bej

In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently…

神经元与认知 · 定量生物学 2015-03-10 R. R. Borges , F. S. Borges , A. M. Batista , E. L. Lameu , R. L. Viana , K. C. Iarosz , I. L. Caldas , M. A. F. Sanjuán

The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we…

神经元与认知 · 定量生物学 2025-01-17 Xinruo Yang , Brent Doiron

Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is…

神经元与认知 · 定量生物学 2012-07-13 Naoki Masuda , Hiroshi Kori

This paper suggests a learning-theoretic perspective on how synaptic plasticity benefits global brain functioning. We introduce a model, the selectron, that (i) arises as the fast time constant limit of leaky integrate-and-fire neurons…

神经元与认知 · 定量生物学 2012-09-26 David Balduzzi , Michel Besserve

How human brain function emerges from structure has intrigued researchers for decades and numerous models have been put forward, yet none of them yields a close structure-function relation. Here we present a resonance model based on…

神经元与认知 · 定量生物学 2022-10-10 Yanjiang Wang , Jichao Ma , Jiebin Luo , Xue Chen , Yue Yuan

Spike-timing-dependent plasticity(STDP) is a biological process of synaptic modification caused by the difference of firing order and timing between neurons. One of the neurodynamical roles of STDP is to form a macroscopic geometrical…

神经元与认知 · 定量生物学 2021-08-10 Hong-Gyu Yoon , Pilwon Kim

Recently spike timing dependent plasticity was observed in inhibitory synapse in the layer II of entorhinal cortex. The rule provides an interesting zero in the region of $\Delta t=t_{post}-t_{pre}=0$ and in addition the dynamic range of…

神经元与认知 · 定量生物学 2013-04-24 Sachin S Talathi
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