By Gustavo Deco, Dragan Obradovic
Neural networks offer a robust new expertise to version and keep an eye on nonlinear and complicated platforms. during this booklet, the authors current a close formula of neural networks from the information-theoretic point of view. They convey how this angle presents new insights into the layout concept of neural networks. specifically they convey how those equipment might be utilized to the themes of supervised and unsupervised studying together with characteristic extraction, linear and non-linear self sustaining part research, and Boltzmann machines. Readers are assumed to have a uncomplicated realizing of neural networks, yet all of the proper innovations from details idea are rigorously brought and defined. accordingly, readers from a number of diverse medical disciplines, particularly cognitive scientists, engineers, physicists, statisticians, and machine scientists, will locate this to be a really important creation to this topic.
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Extra resources for An Information-Theoretic Approach to Neural Computing
In particular, the first studies addressed the nerve centers that process optical patterns. 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.
There is no backcoupling between neurons. 2 (a). The neurons are arranged in layers. e. all connections are allowed in this case. 2 (b). Recurrent architectures are usually used for the learning of dynamical phenomena since the backcoupling can contain delays. 12]. Within a neural network we distinguish between two different types of neurons, namely visible and invisible (or hidden) neurons. The visible neurons process inputs or outputs of the whole neural network and, hence, are divided into the input and output neurons respectively.
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.