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Connectionist networks are too simple
ArgumentOpposé
1
#874
Real neural networks in living organisms exhibit complex dynamics and chaotic activity that connectionist networks lack. These complex dynamics seem too messy from an engineering standpoint but are critical to an understanding of neural dynamics.
Christine Skarda & Walter Freeman, 1987.
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Artificial Intelligence »
Artificial Intelligence
Artificial Intelligence☜A collaboratively editable version of Robert Horns brilliant and pioneering debate map Can Computers Think?—exploring 50 years of philosophical argument about the possibility of computer thought.☜F1CEB7
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Can computers think? [1] »
Can computers think? [1]
Can computers think? [1]☜Can a computational system possess all important elements of human thinking or understanding? ☜FFB597
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Yes: connectionist networks can think [5a] »
Yes: connectionist networks can think [5a]
Yes: connectionist networks can think [5a]☜Connectionist networks can possess all important elements of human thinking or understanding.☜59C6EF
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The Connectionist Biological Assumption »
The Connectionist Biological Assumption
The Connectionist Biological Assumption ☜Connectionist networks are similar to real neural networks.☜98CE71
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Connectionist networks are too simple
Connectionist networks are too simple☜Real neural networks in living organisms exhibit complex dynamics and chaotic activity that connectionist networks lack. These complex dynamics seem too messy from an engineering standpoint but are critical to an understanding of neural dynamics.☜EF597B
●
Simplicity has advantages over biological accuracy »
Simplicity has advantages over biological accuracy
Simplicity has advantages over biological accuracy☜Skarda and Freeman fail to show that their dynamic models have any real advantages. Connectionist except simplifications in order to get useful scientific results.☜EF597B
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Entrée par:
David Price
NodeID:
#874
Node type:
OpposingArgument
Date d'entrée (GMT):
8/14/2006 6:29:00 PM
Date de la dernière modification (Heure GMT):
12/12/2007 9:21:00 PM
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