论文标题
峰值时间依赖性可塑性引起的共鸣
Resonances induced by Spiking Time Dependent Plasticity
论文作者
论文摘要
暴露于某种刺激的神经种群学会更好地代表它。但是，领导本地自组织规则的过程尚不清楚。我们解决了如何学习神经周期性输入并使用差异HEBBIAN学习框架的问题，再加上一种稳态机制，以得出两个自持矛盾方程，从而导致对同一刺激的反应增加。尽管我们所有的模拟都使用简单的泄漏综合和消防神经元以及标准的峰值时间依赖性可塑性学习规则来完成，但我们的结果可以很容易地用速率和人口代码来解释。
Neural populations exposed to a certain stimulus learn to represent it better. However, the process that leads local, self-organized rules to do so is unclear. We address the question of how can a neural periodic input be learned and use the Differential Hebbian Learning framework, coupled with a homeostatic mechanism to derive two self-consistency equations that lead to increased responses to the same stimulus. Although all our simulations are done with simple Leaky-Integrate and Fire neurons and standard Spiking Time Dependent Plasticity learning rules, our results can be easily interpreted in terms of rates and population codes.
