NDLI: Ecological Neural Networks for Object Recognition and Generalization

Content Provider	Springer Nature Link
Author	Calabretta, Raffaele Di Ferdinando, Andrea Parisi, Domenico
Copyright Year	2004
Abstract	Generalization is a critical capacity for organisms. Modeling the behavior of organisms with neural networks, some type of generalizations appear to be accessible to neural networks but other types do not. In this paper we present two simulations. In the first simulation we show that while neural networks can recognize where an object is located in the retina even if they have never experienced that object in that position ('where' generalization subtask), they have difficulty in recognizing the identity of a familiar object in a new position ('what' generalization subtask). In the second simulation we explore the hypothesis that organisms find another solution to the problem of recognizing objects in different positions on their retina: they move their eyes so that objects are always seen in the same position in the retina. This strategy emerges spontaneously in ecological neural networks that are allowed to move their 'eye' in order to bring different portions of the visible world in the central portion of their retina.
Starting Page	37
Ending Page	48
Page Count	12
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	19
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2004-01-01
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Artificial Intelligence (incl. Robotics) Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks Electronic and Computer Engineering Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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