By Gustavo Deco, Dragan Obradovic
Neural networks supply a strong new know-how to version and regulate nonlinear and intricate platforms. during this booklet, the authors current a close formula of neural networks from the information-theoretic perspective. They express how this angle offers new insights into the layout concept of neural networks. specifically they exhibit how those tools will be utilized to the subjects of supervised and unsupervised studying together with characteristic extraction, linear and non-linear autonomous part research, and Boltzmann machines. Readers are assumed to have a uncomplicated knowing of neural networks, yet the entire correct innovations from info idea are conscientiously brought and defined. for that reason, readers from a number of diversified clinical disciplines, significantly cognitive scientists, engineers, physicists, statisticians, and desktop scientists, will locate this to be a truly worthwhile creation to this topic.
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Additional info for An Information-Theoretic Approach to Neural Computing
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. 29]. Hebb's results have motivated a number of artificial learning paradigms such as the one presented in the previous section. As originally postulated, the Hebbian learning rule states that the strength of a synaptic connection should be adjusted if its "level of activity" changes. An active synapse which repeatedly triggers the activation of its postsynaptic neuron will grow in strength.
A neural network is deterministic if the architecture is defined by interconnected detenninistic neurons. The neural architecture is called stochastic if it is composed of stochastic units. 1. (a) Deterministic neuron. (b) Stochastic neuron. A second classification of architectures is defined by the type of connections between the neurons. Principally two types of architecture are defined: feedforward and recurrent. e. there is no backcoupling between neurons. 2 (a). The neurons are arranged in layers.
14: Kraft inequality For any prefix code over an D-array alphabet, the codeword lengths 11' ... e. given a set of codeword lengths that satisfy this inequality, there exists a prefix code with these word lengths. o Optimal code: Let X be a discrete random variable with associated discrete probabilities Pi and let Ii be the code lengths of an associated prefix code. 55) r where a1 denotes the smallest integer greater than a. 1]). 15: First Shannon theorem Let 11' ... , Ik be the optimal codeword lengths for a discrete random variable X with associated distribution given by PI' ...