Self-stabilization of neuronal networks
Self-stabilization of neuronal networks
复制标题
神经元网络的自稳定
作者：
I. Dammasch;G. Wagner;J. Wolff
在有关个体发生（遗传与环境决定）的极端观点之间，我们使用了一种温和的方法：某种预先建立的神经元模型网络对活动偏差做出反应（反映需要补偿的输入），并在复杂的反馈过程中稳定自己。形态发生是基于将突触发生的补偿理论形式化的算法（Wolff和瓦格纳1983）。该算法适用于随机连接的McCulloch-Pitts网络，这些网络能够随着时间的推移保持其活动模式的振荡。该算法可以导致网络的形态稳定，但保持自我维持的振荡活动。这与目前大多数基于Hebbian可塑性规则的突触发生和突触修饰模型相反。赫布网络在没有额外假设的情况下是形态发生不稳定的。补偿网络的结构和功能特性的影响进行了描述。我们认为，突触发生的补偿理论可以解释通过选择性稳定和消除突触，从随机连接的神经元网络中发展出形态发生稳定的神经元网络，补偿算法的逻辑是基于实验结果的。本文表明，补偿理论不仅可以预测突触群体的行为（瓦格纳和沃尔夫，在准备），但它也可以描述在网络中互连的神经元的行为，由此产生的额外的系统属性。神经元的相互作用在某些情况下导致平衡是一个自组织过程，在这个意义上，所有的决定都是在单个细胞水平上执行的，而不知道整个网络的情况或目标。
Between the extreme views concerning ontogenesis (genetic vs. environmental determination), we use a moderate approach: a somehow pre-established neuronal model network reacts to activity deviations (reflecting input to be compensated), and stabilizes itself during a complex feed-back process. Morphogenesis is based on an algorithm formalizing the compensation theory of synaptogenesis (Wolff and Wagner 1983). This algorithm is applied to randomly connected McCulloch-Pitts networks that are able to maintain oscillations of their activity patterns over time. The algorithm can lead to networks which are morphogenetically stable but preserve self-maintained oscillations in activity. This is in contrast to most of the current models of synaptogenesis and synaptic modification based on Hebbian rules of plasticity. Hebbian networks are morphogenetically unstable without additional assumptions. The effects of compensation on structural and functional properties of the networks are described. It is concluded that the compensation theory of synaptogenesis can account for the development of morphogenetically stable neuronal networks out of randomly connected networks via selective stabilization and elimination of synapses.The logic of the compensation algorithm is based on experimental results. The present paper shows that the compensation theory can not only predict the behavior of synaptic populations (Wagner and Wolff, in preparation), but it can also describe the behavior of neurons interconnected in a network, with the resulting additional system properties. The neuronal interactions-leading to equilibrium in certain cases-are a self-organizing process in the sense that all decisions are performed on the individual cell level without knowing the overall network situation or goal.