Ecological neural networks for object recognition and generalization

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.

Publication type: 
Articolo
Author or Creator: 
Calabretta R.
Di Ferdinando A.
Parisi, D.
Publisher: 
D facto s.a.., Bruxelles, Belgio
Source: 
Neural Processing Letters 19 (2004): 37–48. doi:10.1023/B:NEPL.0000016846.74699.1a
info:cnr-pdr/source/autori:Calabretta R.; Di Ferdinando A.; Parisi, D./titolo:Ecological neural networks for object recognition and generalization/doi:10.1023/B:NEPL.0000016846.74699.1a/rivista:Neural Processing Letters/anno:2004/pagina_da:37/pagina_a:48/
Date: 
2004
Resource Identifier: 
http://www.cnr.it/prodotto/i/46762
https://dx.doi.org/10.1023/B:NEPL.0000016846.74699.1a
info:doi:10.1023/B:NEPL.0000016846.74699.1a
ISTC Author: 
Raffaele Calabretta's picture