Schweighofer N, Doya K, Lay F
ERATO Japan Science and Technology Corporation, 2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.
Neuroscience. 2001;103(1):35-50. doi: 10.1016/s0306-4522(00)00548-0.
Marr [J. Physiol. (1969) 202, 437-470] and Albus [Math. Biosci. (1971) 10, 25-61] hypothesized that cerebellar learning is facilitated by a granule cell sparse code, i.e. a neural code in which the fraction of active neurons is low at any one time. In this paper, we re-examine this hypothesis in light of recent experimental and theoretical findings. We argue that cerebellar motor learning is enhanced by a sparse code that simultaneously maximizes information transfer between mossy fibers and granule cells, minimizes redundancies between granule cell discharges, and re-codes the mossy fiber inputs with an adaptive resolution such that inputs corresponding to large errors are finely encoded. We then propose that a set of biologically plausible unsupervised learning rules can produce such a code. To maintain a low mean firing rate compatible with a sparse code, an activity-dependent homeostatic mechanism sets the cells' thresholds. Then, to maximize information transfer, the mossy fiber--granule cell synapses are adjusted by a Hebbian rule. Furthermore, to minimize redundancies between granule cell discharges, the inhibitory Golgi cell--granule cell synapses are tuned by an anti-Hebbian rule. Finally, to allow adaptive resolution, a performance-based neuromodulator-like signal gates these three plastic processes. We integrate these gated learning rules into a simplified model of the cerebellum for arm movement control, and show that unsupervised learning of granule cell sparse codes greatly improves cerebellar adaptive motor control in comparison to a "fixed" Marr--Albus-type model. Until recently, activity-dependent cerebellar plasticity was thought to be largely confined to the granule cell--Purkinje cell synapses. This static view of the cerebellum is, however, quickly being replaced by an extremely dynamic view in which plasticity is omnipresent. The present theoretical study shows how several forms of plasticity in the granular layer of the cerebellum can produce fast, accurate and stable cerebellar learning.
马尔[《生理学杂志》（1969年）202卷，437 - 470页]和阿尔布斯[《数学生物科学》（1971年）10卷，25 - 61页]提出假说，认为小脑学习是由颗粒细胞稀疏编码促进的，即一种在任何时刻活动神经元比例都很低的神经编码。在本文中，我们根据最近的实验和理论发现重新审视这一假说。我们认为，小脑运动学习通过一种稀疏编码得到增强，这种编码同时能使苔藓纤维和颗粒细胞之间的信息传递最大化，使颗粒细胞放电之间的冗余最小化，并用自适应分辨率对苔藓纤维输入进行重新编码，从而对对应大误差的输入进行精细编码。然后我们提出，一组生物学上合理的无监督学习规则能够产生这样一种编码。为了维持与稀疏编码兼容的低平均放电率，一种活动依赖的稳态机制设定细胞的阈值。接着，为了使信息传递最大化，苔藓纤维 - 颗粒细胞突触通过赫布规则进行调整。此外，为了使颗粒细胞放电之间的冗余最小化，抑制性的高尔基细胞 - 颗粒细胞突触通过反赫布规则进行调整。最后，为了实现自适应分辨率，一种基于性能的类神经调质信号控制这三个可塑性过程。我们将这些带门控的学习规则整合到一个用于手臂运动控制的简化小脑模型中，并表明与“固定的”马尔 - 阿尔布斯型模型相比，颗粒细胞稀疏编码的无监督学习极大地改善了小脑的自适应运动控制。直到最近，活动依赖的小脑可塑性还被认为主要局限于颗粒细胞 - 浦肯野细胞突触。然而，这种对小脑的静态观点正迅速被一种极具动态性的观点所取代，在这种动态观点中可塑性无处不在。目前的理论研究表明，小脑颗粒层中的几种可塑性形式如何能够产生快速、准确且稳定的小脑学习。
