论文标题
在经过兴奋性抑制性约束的RNN的特征值和动力学的光谱中的效果
Effect in the spectra of eigenvalues and dynamics of RNNs trained with Excitatory-Inhibitory constraint
论文作者
论文摘要
为了理解和增强描述各个大脑区域的模型，对于研究训练的复发性神经网络的动态很重要。在此类模型中包括Dales法通常提出几个挑战。但是，这是一个重要方面，可以使计算模型更好地捕获大脑的特征。在这里，我们提出了一个使用这种约束来训练网络的框架。然后，我们使用它来训练他们完成简单的决策任务。我们表征了此类网络的复发重量矩阵的特征值分布。有趣的是，我们发现，对于那些在训练前的初始条件是随机的正常状态，对于那些初始条件是随机的正交的训练的初始条件，反复重量矩阵的非优势特征值的半径小于1的圆，而半径小于1。在这两种情况下，半径都不取决于兴奋性和抑制单位的比例或网络的大小。与未经限制的训练的网络相比，半径的减小对我们在这里讨论的活动和动态具有影响。
In order to comprehend and enhance models that describes various brain regions is important to study the dynamics of trained recurrent neural networks. Including Dales law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we present a framework to train networks using such constraint. Then we have used it to train them in simple decision making tasks. We characterized the eigenvalue distributions of the recurrent weight matrices of such networks. Interestingly, we discovered that the non-dominant eigenvalues of the recurrent weight matrix are distributed in a circle with a radius less than 1 for those whose initial condition before training was random normal and in a ring for those whose initial condition was random orthogonal. In both cases, the radius does not depend on the fraction of excitatory and inhibitory units nor the size of the network. Diminution of the radius, compared to networks trained without the constraint, has implications on the activity and dynamics that we discussed here.
