Computational Properties of Retinal Rod Photoreceptors View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1995

AUTHORS

G. M. Ratto , G. Di Schino , L. Cervetto

ABSTRACT

The interest for neural networks can be traced back to the early 40’s with the mathematical models of neurones by McCulloch & Pitts [1943], Hebb [1949] and to continue with the work of Rosenblatt [1959] and Widrow & Posch [quoted by Lippmann, 1987], up to the more recent contributions by Hopfield [1982, 1984], Hopfield & Tank [1986], Grossberg [1986]. A renewed interest for the neural networks coincides with the introduction of new powerful optimisation methods inspired by the physics of spin-glasses [cfr. Kirkpatrick & Toulouse, 1985]; with the development of new techniques of VLSI analog implementation and with important advances in neurosciences as well. The recently renewed interest for neural networks has stimulated the convergence of goals and expertise from different fields including electrical engineering, physics, artificial intelligence and neurophysiology, generating for the first time a common language. The interaction and collaboration between groups with different cultural and technical background offers exciting perspectives in both developing new machines and getting deeper insights into the nervous system. The future applications of ideas generated by an improved knowledge of the natural neural networks rests upon the possibilities offered by a further development of microelectronics [Baccarani et al. 1990] and of hardware circuits possessing enough computational power to tackle optimisation problems that are not solvable by serial computations with complete polynomials. It is perhaps important to note that there are aspects of sensory functions such as vision which can be formulated as optimisation problems [Poggio et al. 1985]. More... »

PAGES

63-71

References to SciGraph publications

Book

TITLE

From Neural Networks and Biomolecular Engineering to Bioelectronics

ISBN

978-1-4899-1090-5
978-1-4899-1088-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4899-1088-2_5

DOI

http://dx.doi.org/10.1007/978-1-4899-1088-2_5

DIMENSIONS

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