Hoshino O, Kashimori Y, Kambara T
Department of Applied Physics and Chemistry, The University of Electro-Communications, Tokyo, Japan.
Proc Natl Acad Sci U S A. 1996 Apr 16;93(8):3303-7. doi: 10.1073/pnas.93.8.3303.
Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.
研究表明，施加在联想神经网络上的动态稳定活动模式之间的转变是由突触连接强度的自组织微小变化引起的，并且是一种相变。在群体编码方案中，信息处理的神经过程的一个关键事件是编码常见实体的活动模式之间的转变。我们提出，基于赫布学习规则的微小且短期的突触变化是这种转变的驱动力。详细研究了以下两种动态稳定状态之间的相变：一种状态是放电模式随时间变化，以便在几种模式之间游走；另一种状态是放电模式固定在几种模式之一上。从模式游走状态到模式固定状态的相变可能由与固定模式相关的弱输入下的赫布学习过程诱导。反向转变可能由无输入的赫布遗忘过程诱导。前一种转变被认为是对输入刺激的识别，而后者被认为是清除已使用的输入数据以准备新的输入。为确保基于相变的信息处理能够通过微小且短期的突触变化来实现，网络必须始终保持在对应于相变点的临界状态附近，这是绝对必要的。
