Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control

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.

► We review biophysical models and systems level studies supporting cerebellar supervised learning.
► Biophysical models of early phase in spike-timing dependent plasticity suggest different learning algorithms for different brain areas.
► Cascade of excitable and bistable dynamics is proposed to overcome sensitivity–longevity dilemma.
► Function of a closed-loop circuit consisting of the inferior olive, Purkinje cells and cerebellar nucleus is explored.
► Control of degrees-of-freedom in learning by changing firing synchronisity is proposed as its function.