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The random search-related self-organization in a neural complex follows therefore the method of cybernetics. Its self-control activity is perceived through the entity of information. Further, as well known, the notion of information is based on the concepts of randomness and probability; or the self-control process of cybernetics in the neural system is dialectically united with the stochastical aspects of the associated activities. The cybernetic basis of the neural system stems from a structured logic of details as portrayed in Figure 1.1 pertinent to the central theme of cybernetics as applied to a neural assembly. It refers to a process of control and self-control primarily from the viewpoint of neuronal information its collection, transmission, storage, and retrieval.
In the design of information processing systems, abstract simulation of a real (biological) neural system should comply with or mimic the cybernetic aspects depicted in Figure 1.1. Structurally, a neural complex could be modeled by a set of units communicating with each other via axonal links resembling the axons and dendrites of a biological neural assembly. Further, the information processing in the neural network should correspond to the self-organizing and self-adaptive (or self-behavioral monitoring) capabilities of the cybernetics associated with the biological neural complex and it activities. The organized search process pertinent to interconnected biological neurons which enables a dichotomous potential state to a cellular unit corresponds to a binary threshold logic in an information processing artificial (neural) network. Classically, McCulloch and Pitts in 1943 [7] presented a computational model of nervous activity in terms of a dichotomous (binary) threshold logic. Subsequently, the process of random search in pursuit of an information selection while seeking a normal state (as governed by the self-organizing cybernetic principles) was incorporated implicitly in the artificial networks by Hebb [19]. He postulated the principle of connectivity (of interconnections between the cells). He surmised that the connectivity depicts a self-organizing protocol strengthening the pathway of connections between the neurons adaptively, confirming thereby a cybernetic mode of search procedure. The state-transitional representation of neurons, together with the connectivity concept inculcate a computational power in the artificial neural network constituting an information processing unit. Such computational power stems from a one-to-one correspondence of the associated cybernetics in the real and artificial neurons. In the construction of artificial neural networks two strategies are pursued. The first one refers to a biomime, strictly imitating the biological neural assembly. The second type is application-based with an architecture dictated by ad hoc requirements of specific applications. In many situations, such ad hoc versions may not replicate faithfully the neuromimetic considerations. In essence however, both the real neural complex as well as the artificial neural network can be regarded as machines that learn. Fortifying this dogma, Wiener observed that the concept of learning machines is applicable not only to those machines which we have made ourselves, but also is relevant to those living machines which we call animals, so that we have the possibility of throwing a new light on biological cybernetics. Further, devoting attention to those feedbacks which maintain the working level of the nervous system, Stanley-Jones [20] also considered the prospects of kybernetic principles as applied to the neural complex; and as rightly forecast by Wiener neurocybernetics has become a field of activity which is expected to become much more alive in the (near) future. The basis of cybernetics vis-a-vis neural complex has the following major underlain considerations:
Copyright © CRC Press LLC
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