标题： 通过神经网络中的刺激诱导可塑性进行任务学习 显示英文标题

标题： Task learning through stimulation-induced plasticity in neural networks

摘要： 突触可塑性动态地塑造神经系统的连接性，并是大脑学习过程的关键。 机制的可塑性能在多大程度上被利用来驱动神经网络并使其执行某种计算任务仍不清楚。 这个问题在生物工程背景下相关，可以被表述为一个高维系统上的控制问题，该系统具有强烈约束和非线性动力学。 我们提出了一种自包含的程序，通过适当的时空刺激神经元，能够将基于速率的神经网络从任意初始连接驱动到期望的功能状态。 我们在两个不同的计算任务上展示了我们的方法：多个输入刺激和活动模式之间的非线性关联（代表数字图像），以及在神经元群体中构建一个编码集体变量的连续吸引子。 因此，我们的工作为基于真实神经元的体外计算新兴范式提供了一个原理证明。 显示英文摘要

摘要： Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploited to drive a neural network and make it perform some kind of computational task remains unclear. This question, relevant in a bioengineering context, can be formulated as a control problem on a high-dimensional system with strongly constrained and non-linear dynamics. We present a self-contained procedure which, through appropriate spatio-temporal stimulations of the neurons, is able to drive rate-based neural networks with arbitrary initial connectivity towards a desired functional state. We illustrate our approach on two different computational tasks: a non-linear association between multiple input stimulations and activity patterns (representing digit images), and the construction of a continuous attractor encoding a collective variable in a neural population. Our work thus provides a proof of principle for emerging paradigms of in vitro computation based on real neurons.