By Gustavo Deco, Dragan Obradovic
An in depth formula of neural networks from the information-theoretic point of view. The authors exhibit how this attitude presents new insights into the layout concept of neural networks. specifically they display how those tools could be utilized to the themes of supervised and unsupervised studying, together with function extraction, linear and non-linear autonomous part research, and Boltzmann machines. Readers are assumed to have a simple realizing of neural networks, yet all of the proper strategies from info conception are rigorously brought and defined. therefore, readers from assorted medical disciplines, particularly cognitive scientists, engineers, physicists, statisticians, and computing device scientists, will locate this an exceptionally precious advent to this subject.
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Extra resources for An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing)
Decorrelation, does not necessarily yield "statistical independence". Statistical independence implies that the probability distribution is factorizable. Decorrelation (diagonalization of the covariance matrix) and statistical independency are equivalent only in the Gaussian case (see Chapter 4 for more details about this fact). The next section presents an alternative derivation of PC A as the optimal linear compression method. 2 peA and Optimal Reconstruction This section focuses on reconstruction properties of PCA.
25) i= 1 where wji are the synaptic strengths. e. Ym = max (Yj) . e. Llw·. -w .. )8. 27) where 11 is a learning constant and 0nm is the Kronecker delta function. In this way the synapse updates encourage specialization of certain neurons to "win" certain input patterns without the direction of a teacher. In the next chapter we will see several variants of this heuristic paradigm that will be derived by incorporating information theoretic concepts and will extract the statistics of the environment.
28] recognized in experiments with cats that the visual cortex contains neurons which recognize specific patterns, such as vertical or horizontal lines. These neurons fire when specific receptor cells lying on a straight line in the retina of the eye are excited. Due to the fact that it is very unlikely that such synaptic structures are genetically determined, there should exist a biological mechanism that adjusts the synaptic values of the brain in an unsupervised fashion. 29] which is introduced in the next section.