Horn D, Levy N, Ruppin E
School of Physics and Astronomy, Tel-Aviv University, Israel.
Neural Comput. 1998 Jan 1;10(1):1-18. doi: 10.1162/089976698300017863.
Since their conception half a century ago, Hebbian cell assemblies have become a basic term in the neurosciences, and the idea that learning takes place through synaptic modifications has been accepted as a fundamental paradigm. As synapses undergo continuous metabolic turnover, adopting the stance that memories are engraved in the synaptic matrix raises a fundamental problem: How can memories be maintained for very long time periods? We present a novel solution to this long-standing question, based on biological evidence of neuronal regulation mechanisms that act to maintain neuronal activity. Our mechanism is developed within the framework of a neural model of associative memory. It is operative in conjunction with random activation of the memory system and is able to counterbalance degradation of synaptic weights and normalize the basins of attraction of all memories. Over long time periods, when the variance of the degradation process becomes important, the memory system stabilizes if its synapses are appropriately bounded. Thus, the remnant memory system is obtained by a dynamic process of synaptic selection and growth driven by neuronal regulatory mechanisms. Our model is a specific realization of dynamic stabilization of neural circuitry, which is often assumed to take place during sleep.
自半个世纪前被提出以来，赫布细胞集合体已成为神经科学中的一个基本术语，并且学习通过突触修饰发生这一观点已被接受为一个基本范式。由于突触经历持续的代谢更新，采取记忆铭刻在突触基质中的立场会引发一个基本问题：记忆如何能够被长时间维持？基于作用于维持神经元活动的神经元调节机制的生物学证据，我们提出了这个长期存在问题的一个新解决方案。我们的机制是在联想记忆神经模型的框架内发展起来的。它与记忆系统的随机激活协同起作用，并且能够抵消突触权重的衰减并使所有记忆的吸引盆正常化。在很长一段时间内，当衰减过程的方差变得重要时，如果其突触被适当地限制，记忆系统就会稳定下来。因此，残余记忆系统是由神经元调节机制驱动的突触选择和生长的动态过程获得的。我们的模型是神经回路动态稳定的一种具体实现，而神经回路的动态稳定通常被认为发生在睡眠期间。
