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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…

Neurons and Cognition · Quantitative Biology 2017-08-16 Sang-Yoon Kim , Woochang Lim

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barab\'{a}si-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory…

Neurons and Cognition · Quantitative Biology 2018-08-21 Sang-Yoon Kim , Woochang Lim

We consider clustered small-world networks with both inhibitory (I) and excitatory (E) populations. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation…

Neurons and Cognition · Quantitative Biology 2020-02-12 Sang-Yoon Kim , Woochang Lim

We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population…

Neurons and Cognition · Quantitative Biology 2017-11-28 Sang-Yoon Kim , Woochang Lim

Fast cortical rhythms with stochastic and intermittent neural discharges have been observed in electric recordings of brain activity. Recently, Brunel et al. developed a framework to describe this kind of fast sparse synchronization in both…

Neurons and Cognition · Quantitative Biology 2014-09-03 Sang-Yoon Kim , Woochang Lim

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…

Quantitative Methods · Quantitative Biology 2021-04-26 Joel Zirkle , Leonid L Rubchinsky

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of pre- and post-synaptic spikes and this is called spike-timing-dependent plasticity (STDP). Many authors have claimed that a…

Disordered Systems and Neural Networks · Physics 2007-05-23 Narihisa Matsumoto , Masato Okada

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…

The collective dynamics of excitatory pulse coupled neural networks with spike timing dependent plasticity (STDP) is studied. Depending on the model parameters stationary states characterized by High or Low Synchronization can be observed.…

Disordered Systems and Neural Networks · Physics 2015-04-14 Kaare Mikkelsen , Alberto Imparato , Alessandro Torcini

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…

Neurons and Cognition · Quantitative Biology 2021-11-16 Philippe Robert , Gaëtan Vignoud

The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states,…

Neurons and Cognition · Quantitative Biology 2013-05-21 Kaare Mikkelsen , Alberto Imparato , Alessandro Torcini

We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We…

We consider a directed Barab\'{a}si-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees, and study emergence of sparsely synchronized rhythms for a fixed attachment degree in an…

Neurons and Cognition · Quantitative Biology 2015-09-02 Sang-Yoon Kim , Woochang Lim

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…

Neurons and Cognition · Quantitative Biology 2023-08-15 Marius E. Yamakou , Mathieu Desroches , Serafim Rodrigues

We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the effect of regulating the trans-synaptic weights of loops of any length within a simulated network of neurons. We show that depending on STDP's polarity, functional…

Neurons and Cognition · Quantitative Biology 2010-03-23 James R. Kozloski , Guillermo A. Cecchi

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…

Neurons and Cognition · Quantitative Biology 2012-07-13 Naoki Masuda , Hiroshi Kori

For modeling complex synaptic connectivity, we consider the Watts-Strogatz small-world network which interpolates between regular lattice and random network via rewiring, and investigate the effect of small-world connectivity on emergence…

Neurons and Cognition · Quantitative Biology 2014-07-11 Sang-Yoon Kim , Woochang Lim

We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons. By varying the fraction of LR interneurons $p_{long}$, we investigate the effect of network architecture on…

Neurons and Cognition · Quantitative Biology 2016-08-17 Sang-Yoon Kim , Woochang Lim

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at…

Neurons and Cognition · Quantitative Biology 2023-01-03 Marius E. Yamakou , Estelle M. Inack

Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related…

Neural and Evolutionary Computing · Computer Science 2020-09-10 Timoleon Moraitis , Abu Sebastian , Irem Boybat , Manuel Le Gallo , Tomas Tuma , Evangelos Eleftheriou
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