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The Connectionist Biological Assumption
Connectionist networks are similar to real neural networks.
Note
: Also see the:
"
Is the brain a computer
?" arguments on Map 1.
"
Is the relation between hardware and software similar to that between human brains and minds
?" arguments on Map 3.
RELATED ARTICLES
Explain
⌅
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
⌃
Can computers think? [1]
Can computers think? [1]☜Can a computational system possess all important elements of human thinking or understanding? ☜FFB597
⌃
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
■
The Connectionist Biological Assumption
The Connectionist Biological Assumption ☜Connectionist networks are similar to real neural networks.☜98CE71
↳
Brain-style modelling can be misleading
Brain-style modelling can be misleading ☜Basing psychological theories on facts about the brain can be misleading. Neural inspiration seems useful, but its led to a revival of such weak psychological theories as: associationism; microfeature analysis; and statistically based learning.☜EF597B
↳
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
↳
Facts about the brain may be irrelevant
Facts about the brain may be irrelevant☜Structures at different levels of organisation are often dissimilar. Thinking may have little in common with the neural structures its implemented in. Basing a theory of cognitive architecture on a theory about the brain requires care.☜EF597B
⇥
The Biological Assumption
The Biological Assumption☜The brain is the hardware (or wetware) on which the software of the mind is run. Thinking is a symbolic process that is implemented in the neurons of the brain and that can also be implemented in the circuits of a digital computer.☜FFFACD
□
Connectionist networks can think without following rules
Connectionist networks can think without following rules☜Like humans, connectionist networks exhibit fluid, intelligent behaviour without following rigid, explicit rules. In general, they are trained to exhibit intelligent behaviour rather than being programmed with rules. ☜98CE71
□
The Connectionist Dilemma
The Connectionist Dilemma☜The connectionist approach to cognitive science is impaled on the horns of a dilemma: it is either inadequate as a theory of mind, or else it is an implementation of the classical architecture (see detailed text).☜98CE71
□
The Subsymbolic Paradigm
The Subsymbolic Paradigm☜The fundamental level of analysis for studying the mind is the subconceptual level, which describes fine-grained subsymbolic activity in a connectionist network. ☜98CE71
□
Connectionist computers lack commonsense
Connectionist computers lack commonsense☜Connectionist networks are unable to make generalisations and classifications in the way human beings do, because they lack our commonsense understanding of the world.☜EF597B
□
Connectionist networks are formal systems
Connectionist networks are formal systems☜Arguments used against the formal character of symbol manipulators apply equally well to connectionist networks [CNs]. Functions computed on a CN can also be computed on a serial machine and CNs can implement classical serial processing.☜EF597B
□
Connectionists fall into a computational mindset
Connectionists fall into a computational mindset☜Connectionists take steps in the right direction, but fall into a computational mindset—eg substituting activation patterns for symbols. They should focus on a dynamical systems approach; viewing the mind as a complex system evolving through time.☜EF597B
□
One-layer perceptrons can’t compute certain functions
One-layer perceptrons can’t compute certain functions☜E.g. they cant compute: Parity—whether an odd or even no. of units in the input layer are active—and Connectedness—whether all active units in the input layer are connected to all other active units, either directly, or via other active units.☜EF597B
□
Graph of this discussion
Graph of this discussion☜Click this to see the whole debate, excluding comments, in graphical form☜dcdcdc
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Entered by:-
David Price
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#873
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Entry date (GMT):
8/14/2006 6:26:00 PM
Last edit date (GMT):
10/2/2009 8:58:00 AM
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