Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/76588
Title: On some methods in neuromathematics (or the development of mathematical methods for the description of structure and function in neurons)
Authors: Moreno Díaz, Roberto 
Leibovic, Knicholas
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 1995
Publisher: Springer
Journal: Lecture Notes in Computer Science 
Conference: International Workshop on Artificial Neural Networks 
Abstract: Success in exploring neural circuitry and its functions depends critically on the availability of data. This determines what kinds of questions can be asked and what analytical tools can most appropriately be used. Biophysical studies have relied heavily on statistics -e.g. as applied to neuronal spike trains- and differential equations and matrix algebra-e.g. as applied to the Hodgkin/Huxley axon and in modeling some networks. Some other approaches have been relatively neglected. These include the search for optimality criteria in relating structure and function and the decomposition of informational processes into simple units.In this paper we describe how a particular optimaliy criterion has led to new insights and to the classifications of one type of neural cell; and are describe a new family of filters with interesting properties, which serve as simple information processing units and which can be concatenated to provide both high level and low level descriptions. Both methods were developed in connection with visual processing in the retina. But they can be extended with appropriate reformulations to other areas of the nervous system.
URI: http://hdl.handle.net/10553/76588
ISSN: 0302-9743
DOI: 10.1007/3-540-59497-3_177
Source: Lecture Notes in Computer Science [ISSN 0302-9743], v. 930, p. 209-214, (1995)
Appears in Collections:Actas de congresos
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