Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering

Câteau, Hideyuki; Kitano, Katsunori; Fukai, Tomoki

doi:10.1103/physreve.77.051909

Cited by 47 publications

(41 citation statements)

References 45 publications

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“…Previous work showed that STDP can induce non-trivial synchrony structure between neurons (Câteau et al 2008), and that dendritic delays play an important role (Senn et al 2002;Lubenov and Siapas 2008). The present framework can be adapted to incorporate these aspects.…”

Section: Discussionmentioning

confidence: 98%

“…This has only begun to be addressed for STDP as well as for synaptic plasticity in general (Karbowski and Ermentrout 2002;Masuda and Kori 2007), mainly using numerical simulation (Song and Abbott 2001;Morrison et al 2007). A particular effort was made to understand how STDP can modify synchrony properties in neuronal networks (Senn et al 2002;Câteau et al 2008;Lubenov and Siapas 2008). In recent articles (Burkitt et al 2007;Gilson et al 2009c), we have developed a theoretical framework to model the weight dynamics in a network with any arbitrary architecture and carried out the analysis for the case of a recurrently connected network with no external inputs, both for full and partial connectivity.…”

Section: Introductionmentioning

confidence: 99%

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Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV

et al. 2009

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In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV

et al. 2009

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“…Recent reports claim the relevance of acausal spike pairs in the presence of synaptic delay [10,28]. This and other factors, such as different time scales of LTP and LTD [27], may let bidirectional synapses survive as observed in in vitro experiments [29].…”

Section: Discussionmentioning

confidence: 99%

Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

2009

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Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear.We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network.Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.

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“…The study of the neuronal dynamics induced by STDP in recurrent architectures is therefore a crucial step in order to gain insight into learning in the brain. However, the study of STDP in such recurrent networks has only begun to be addressed, mainly using numerical simulation (Song and Abbott 2001;Senn et al 2002;Wenisch et al 2005;Morrison et al 2007;Lubenov and Siapas 2008;Câteau et al 2008;Kang et al 2008). There exists only a few theoretical results for recurrent architectures (Karbowski and Ermentrout 2002;Masuda and Kori 2007) due to mathematical difficulties in evaluating the effect of feedback synaptic loops.…”

Section: Introductionmentioning

confidence: 99%

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

et al. 2009

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Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of "steady" inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections.

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Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering

Cited by 47 publications

References 45 publications

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV

Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

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