"Because of the 'all-or-none' character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time." (Warren S McCulloch & Walter Pitts, "A logical calculus of the ideas immanent in nervous activity" Vol. 5, 1943) [introduced the first mathematical model of an artificial neuron]
"Causality, which requires description of states and a law of necessary connection relating them, has appeared in several forms in several sciences, but never, except in statistics, has it been as irreciprocal as in this theory. Specification for any one time of afferent stimulation and of the activity of all constituent neurons, each an 'all-or-none' affair, determines the state. Specification of the nervous net provides the law of necessary connection whereby one can compute from the description of any state that of the succeeding state, but the inclusion of disjunctive relations prevents complete determination of the one before. Moreover, the regenerative activity of Constituent circles renders reference indefinite as to time past. Thus our knowledge of the world, including ourselves, is incomplete as to space and indefinite as to time." (Warren S McCulloch & Walter Pitts, "A logical calculus of the ideas immanent in nervous activity" Vol. 5, 1943)
"The phenomena of learning, which are of a character persisting over most physiological changes in nervous activity, seem to require the possibility of permanent alterations in the structure of nets. The simplest such alteration is the formation of new synapses or equivalent local depressions of threshold. We suppose that some axonal terminations cannot at first excite the succeeding neuron; but if at any time the neuron fires, and the axonal terminations are simultaneously excited, they become synapses of the ordinary kind, hence-forth capable of exciting the neuron. The loss of an inhibitory synapse gives an entirely equivalent result." (Warren S McCulloch & Walter Pitts, "A logical calculus of the ideas immanent in nervous activity" Vol. 5, 1943)
"There is no theory we may hold and no observation we can make that will retain so much as its old defective reference to the facts if the net be altered. Tinitus, paraestheaias, hallucinations, delusions, confusions and disorientations intervene. Thus empiry confirms that if our nets are undefined, our facts are undefined, and to the 'real' we can attribute not so much as one quality or 'form'. With determination of the net, the unknowable object of knowledge, the 'thing in itself', ceases to be unknowable." (Warren S McCulloch & Walter Pitts, "A logical calculus of the ideas immanent in nervous activity" Vol. 5, 1943)
"The first attempts to consider the behavior of so-called 'random neural nets' in a systematic way have led to a series of problems concerned with relations between the 'structure' and the 'function' of such nets. The 'structure' of a random net is not a clearly defined topological manifold such as could be used to describe a circuit with explicitly given connections. In a random neural net, one does not speak of 'this' neuron synapsing on 'that' one, but rather in terms of tendencies and probabilities associated with points or regions in the net." (Anatol Rapoport, "Cycle distributions in random nets", The Bulletin of Mathematical Biophysics 10(3), 1948)
"The terms 'black box' and 'white box' are convenient and figurative expressions of not very well determined usage. I shall understand by a black box a piece of apparatus, such as four-terminal networks with two input and two output terminals, which performs a definite operation on the present and past of the input potential, but for which we do not necessarily have any information of the structure by which this operation is performed. On the other hand, a white box will be similar network in which we have built in the relation between input and output potentials in accordance with a definite structural plan for securing a previously determined input-output relation." (Norbert Wiener, "Cybernetics: Or Control and Communication in the Animal and the Machine", 1948)
"If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: (1) How is information about the physical world sensed, or detected, by the biological system? (2) In what form is information stored, or remembered? (3) How does information contained in storage, or in memory, influence recognition and behavior?" (Frank Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain", Psychological Review 65(6), 1958)
"The theory to be presented here takes the empiricist, or 'connectionist' position with regard to these questions. The theory has been developed for a hypothetical nervous system, or machine, called a perceptron. The perceptron is designed to illustrate some of the fundamental properties of intelligent systems in general, without becoming too deeply enmeshed in the special, and frequently unknown, conditions which hold for particular bio-logical organisms." (Frank Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain", Psychological Review 65(6), 1958)
"[a neural network is] a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs." (Robert Hecht-Nielsen, cca. 1989) [in Maureen Caudill, "Neural Network Primer: Part I", AI Expert, 1989)
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