ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.
Curr Opin Neurobiol. 2011 Oct;21(5):791-800. doi: 10.1016/j.conb.2011.05.014. Epub 2011 Jun 12.
The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning.
基于尖峰时间依赖可塑性的生物物理模型探索了具有分子基础的动力学，这些动力学为诸如巧合检测、突触合格痕迹和赫布学习等计算概念提供了支持。它们总体上支持不同脑区的不同学习算法，特别是小脑的监督学习。由于单个棘突在物理上非常小，因此其化学反应本质上是随机的，因此突触记忆中存在敏感性-寿命困境。在这里，提出了级联的兴奋和双稳态动力学来克服这个困难。不同脑区的各种学习算法都面临着难以泛化的问题。为了解决这个问题，可以通过改变神经放电的同步性来实现自由度的控制。特别是对于小脑的监督学习，提出了由浦肯野细胞、下橄榄核和小脑核组成的三角形闭环电路作为优化控制同步放电和学习自由度的电路。
