ADAPTIVE FILTER MODEL OF THE CEREBELLUM
ADAPTIVE FILTER MODEL OF THE CEREBELLUM
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DOI：
10.1007/bf00336192
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发表时间：
1982-01-01
影响因子：
1.9
通讯作者：
FUJITA, M
中科院分区：
文献类型：
--
作者：
FUJITA, M
用线性系统分析方法重新建立了Marr-Albus小脑模型。小脑的自适应线性滤波模型执行具有学习能力的相位超前滞后补偿器的滤波作用，并将解释被称为小脑补偿的现象。高尔基体可以作为相位滞后元件，例如，作为时间常数约为几秒的漏积分器。在此假设下，苔藓纤维-颗粒-高尔基细胞输入网络作为相位超前滞后补偿器。高尔基颗粒细胞系统的输出信号，即平行纤维信号，通过可变突触连接聚集在一起，形成浦肯野细胞输出。从自适应线性滤波器的一般理论出发，导出了这些可修改连接的学习原理。通过这些学习原理，浦肯野细胞输出收敛到期望的响应，以最小化性能的均方误差。从更一般的意义上说，浦肯野细胞在多对输入信号和相应的期望输出信号的基础上获得滤波功能。描述了当输入信号为正弦信号时，输出信号的收敛模式。
The Marr-Albus model of the cerebellum was reformulated with linear system analysis. This adaptive linear filter model of the cerebellum performs a filtering action of a phase lead-lag compensator with learning capability and will give an account for the phenomena which have been termed cerebellar compensation. A Golgi cell may act as a phase lag element e.g., as a leaky integrator with time constant of about several seconds. Under this assumption, a mossy fiber-granule cell-Golgi cell input network functions as a phase lead-lag compensator. Output signals from Golgi-granule cell systems, i.e., parallel fiber signals, are gathered together through variable synaptic connections to form a Purkinje cell output. From a general theory of adaptive linear filters, learning principles for these modifiable connections are derived. By these learning principles, a Purkinje cell output converges to the desired response to minimize the mean square error of the performance. In a more general sense, a Purkinje cell acquires a filtering function on the basis of multiple pairs of input signals and corresponding desired output signals. The mode of convergence of the output signal is described when the input signal is sinusoidal.