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首页> 外文期刊>Journal of Computational Neuroscience >Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity
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Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity

机译：通过尖峰时序相关的可塑性形成前馈网络和频率同步

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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 establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.
机译：带有不对称学习窗口的依赖于时序的可塑性（STDP）通常在大脑中发现，可用于各种基于峰值的计算，例如输入过滤和联想记忆。 STDP的自然结果是因果关系的建立，在某种意义上说，神经元在特定的突触前神经元被激发后会学习滞后激发。 STDP对同步的影响难以捉摸，因为尖峰同步意味着不同神经元的单一尖峰事件，而不是神经元之间的因果延迟关系。我们探索STDP如何在带有起搏器的振荡器网络中促进同步。我们显示，具有非对称学习窗口的STDP导致起搏器开始的前馈网络的自组织。结果，STDP极大地促进了频率同步。即使由于突触可塑性而减少了尖峰时间的差异，但仍存在有限的时间滞后，因此无法实现完美的尖峰同步。与基于耦合神经元相互交互作用的大规模同步的传统机制相比，此处发现的实现同步的途径是上游神经元对下游神经元的奴役。对于具有对称学习窗口的STDP，不会实现这种前馈同步。

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