Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.
Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.
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DOI：
10.1371/journal.pone.0170388
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Ikegami T
中科院分区：
文献类型：
--
作者：
Sinapayen L;Masumori A;Ikegami T
基于真实的神经元网络的学习，以及基于神经网络的生物启发模型的学习，还没有找到导致广泛应用的通用学习规则。在本文中，我们认为存在一个原则，允许引导生物启发的神经网络的动态。使用精心定时的外部刺激，网络可以被驱动到期望的动态状态。我们把这个原则称为“刺激回避学习”（LSA）。我们通过模拟证明，在人工网络中导致LSA的最小充分条件也足以重现类似于Shahaf和Marom在生物神经元中获得的学习结果，并解释了突触修剪。我们通过模拟一个由3个神经元组成的小型网络来研究其潜在机制，然后将其扩展到100个神经元。我们表明，LSA具有更高的解释力比现有的假设的生物神经网络对外部模拟的反应，并可以用作一个具体的应用程序的学习规则：学习的墙壁避免模拟机器人。在其他作品中，可以通过类似于模拟多巴胺系统的全局奖励信号来获得具有尖峰网络的强化学习;我们认为这是第一个通过依赖于环境条件的Hebbian学习来展示具有随机尖峰网络的感觉运动学习的项目，而没有单独的奖励系统。
Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle “Learning by Stimulation Avoidance” (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.