Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis.
Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis.
复制标题
神经元网络的自稳定。
作者：
I. Dammasch;G. Wagner;J. Wolff
在关于个体发生（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（反映要补偿的输入），并在复杂的反馈过程中稳定自身。形态发生基于形式化突触发生补偿理论的算法（Wolff 和 Wagner 1983）。该算法应用于随机连接的 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。该算法可以产生形态稳定但在活动中保持自我维持振荡的网络。这与当前大多数基于赫布可塑性规则的突触发生和突触修饰模型形成鲜明对比。如果没有额外的假设，赫布网络在形态发生上是不稳定的。描述了补偿对网络结构和功能特性的影响。结论是，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。补偿算法的逻辑基于实验结果。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，正在准备中），而且还可以描述网络中互连的神经元的行为，以及由此产生的附加系统属性。神经元相互作用（在某些情况下导致平衡）是一个自组织过程，因为所有决策都是在单个细胞水平上执行的，而不知道整体网络情况或目标。
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.