Concept

Cerebellar model articulation controller

The cerebellar model arithmetic computer (CMAC) is a type of neural network based on a model of the mammalian cerebellum. It is also known as the cerebellar model articulation controller. It is a type of associative memory. The CMAC was first proposed as a function modeler for robotic controllers by James Albus in 1975 (hence the name), but has been extensively used in reinforcement learning and also as for automated classification in the machine learning community. The CMAC is an extension of the perceptron model. It computes a function for input dimensions. The input space is divided up into hyper-rectangles, each of which is associated with a memory cell. The contents of the memory cells are the weights, which are adjusted during training. Usually, more than one quantisation of input space is used, so that any point in input space is associated with a number of hyper-rectangles, and therefore with a number of memory cells. The output of a CMAC is the algebraic sum of the weights in all the memory cells activated by the input point. A change of value of the input point results in a change in the set of activated hyper-rectangles, and therefore a change in the set of memory cells participating in the CMAC output. The CMAC output is therefore stored in a distributed fashion, such that the output corresponding to any point in input space is derived from the value stored in a number of memory cells (hence the name associative memory). This provides generalisation. In the adjacent image, there are two inputs to the CMAC, represented as a 2D space. Two quantising functions have been used to divide this space with two overlapping grids (one shown in heavier lines). A single input is shown near the middle, and this has activated two memory cells, corresponding to the shaded area. If another point occurs close to the one shown, it will share some of the same memory cells, providing generalisation. The CMAC is trained by presenting pairs of input points and output values, and adjusting the weights in the activated cells by a proportion of the error observed at the output.

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Official source

https://en.wikipedia.org/wiki/Cerebellar_model_articulation_controller

About this result

Related courses (1)

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Moser C., Havermeyer F,.Ultra-narrowband tunable laserline notch filter, Appl. Phys. B, 95 (3), pp 597-601, 2009.

Moser C. ,Ho L., Havermeyer F,. Self-aligned Non-dispersive External Cavity Tunable Laser, 16 (21), 16691-16696 Optics Express, 2008.

Steckman, GJ , Moser C. et al. Volume holographic grating wavelength stabilized laser diodes, IEEE J. Of Selected Topics in Quantum Electronics, (13), 672-678, 2007.

Buse K, Havermeyer F, Liu W., Moser C, Psaltis D. Holographic Filters , Book Chapter Photorefractive Materials and their Applications, 2005.

Havermeyer, F; Liu, WH; Moser, C, et al. Volume holographic grating-based continuously tunable optical filter , Opt. Eng. 43 (9), 2017-2021, 2004.

Moser C., Maravic I., Schupp B., Adibi A, Psaltis D, Diffraction efficiency of localized holograms in doubly doped LiNbO3 crystals, Opt. Lett. 25: (17),1243-1245, 2000.