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===Biological realism===
Connectionist work in general does not need to be biologically realistic and therefore suffers from a lack of neuroscientific plausibility.<ref>{{Cite web|url=http://www.encephalos.gr/48-1-01e.htm|title=Encephalos Journal|website=www.encephalos.gr|access-date=2018-02-20}}</ref><ref>{{Cite book|url=https://books.google.com/?id=s-OCCwAAQBAJ&pg=PT18&lpg=PT18&dq=%22accurate%22#v=onepage&q=%22accurate%22&f=false|title=Neural Geographies: Feminism and the Microstructure of Cognition|last=Wilson|first=Elizabeth A.|date=2016-02-04|publisher=Routledge|isbn=9781317958765|language=en}}</ref><ref>{{Cite web|url=https://pdfs.semanticscholar.org/e953/59bc80e624a963a3d8c943e3b2898a397ef7.pdf|title=Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop|last=|first=|date=|website=|access-date=}}</ref><ref>{{Cite journal|last=Zorzi|first=Marco|last2=Testolin|first2=Alberto|last3=Stoianov|first3=Ivilin P.|date=2013-08-20|title=Modeling language and cognition with deep unsupervised learning: a tutorial overview|journal=Frontiers in Psychology|volume=4|pages=515|doi=10.3389/fpsyg.2013.00515|issn=1664-1078|pmc=3747356|pmid=23970869}}</ref><ref>{{Cite web|url=http://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1015&context=comparativephilosophy|title=ANALYTIC AND CONTINENTAL PHILOSOPHY|last=|first=|date=|website=|access-date=}}</ref><ref>{{Cite book|url=https://books.google.com/?id=uV9TZzOITMwC&pg=PA17&lpg=PA17&dq=%22biological%20plausibility%22#v=onepage&q=%22biological%20plausibility%22&f=false|title=Neural Network Perspectives on Cognition and Adaptive Robotics|last=Browne|first=A.|date=1997-01-01|publisher=CRC Press|isbn=9780750304559|language=en}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=7pPv0STSos8C&pg=PA63&lpg=PA63&dq=%22biological+realism%22#v=onepage&q=%22biological%20realism%22&f=false|title=Connectionism in Perspective|last=Pfeifer|first=R.|last2=Schreter|first2=Z.|last3=Fogelman-Soulié|first3=F.|last4=Steels|first4=L.|date=1989-08-23|publisher=Elsevier|isbn=9780444598769|location=|pages=|language=en}}</ref> However, the structure of neural networks is derived from that of biological [[
===Learning===
The weights in a neural network are adjusted according to some [[learning rule]] or algorithm, such as [[Hebbian theory|Hebbian learning]]. Thus, connectionists have created many sophisticated learning procedures for neural networks. Learning always involves modifying the connection weights. In general, these involve mathematical formulas to determine the change in weights when given sets of data consisting of activation vectors for some subset of the neural units. Several studies have been focused on designing teaching-learning methods based on connectionism.<ref>{{Cite journal|last=Novo|first=María-Luisa|last2=Alsina|first2=Ángel|last3=Marbán|first3=José-María|last4=Berciano|first4=Ainhoa|date=2017|title=Connective Intelligence for Childhood Mathematics Education|journal=Comunicar|language=es|volume=25|issue=52|pages=29–39|doi=10.3916/c52-2017-03|issn=1134-3478}}</ref>
By formalizing learning in such a way, connectionists have many tools. A very common strategy in connectionist learning methods is to incorporate [[gradient descent]] over an error surface in a space defined by the weight matrix. All gradient descent learning in connectionist models involves changing each weight by the [[partial derivative]] of the error surface with respect to the weight. [[Backpropagation]] (BP), first made popular in the 1980s, is probably the most commonly known connectionist gradient descent algorithm today
==History==
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* Computationalists often posit [[domain specificity|domain specific]] symbolic sub-systems designed to support learning in specific areas of cognition (e.g., language, intentionality, number), whereas connectionists posit one or a small set of very general learning-mechanisms.
Despite these differences, some theorists have proposed that the connectionist architecture is simply the manner in which organic brains happen to implement the symbol-manipulation system. This is logically possible, as it is well known that connectionist models can implement symbol-manipulation systems of the kind used in computationalist models,<ref name=":3">{{Cite journal|last=Chang|first=Franklin|date=2002|title=Symbolically speaking: a connectionist model of sentence production|journal=Cognitive Science|language=en|volume=26|issue=5|pages=609–651|doi=10.1207/s15516709cog2605_3|issn=1551-6709}}</ref>
The debate was largely centred on logical arguments about whether connectionist networks could produce the syntactic structure observed in this sort of reasoning. This was later achieved although using fast-variable binding abilities outside of those standardly assumed in connectionist models.<ref name=":3" /><ref>{{Cite journal|last=Shastri|first=Lokendra|last2=Ajjanagadde|first2=Venkat|date=September 1993
Part of the appeal of computational descriptions is that they are relatively easy to interpret, and thus may be seen as contributing to our understanding of particular mental processes, whereas connectionist models are in general more opaque, to the extent that they may be describable only in very general terms (such as specifying the learning algorithm, the number of units, etc.), or in unhelpfully low-level terms. In this sense connectionist models may instantiate, and thereby provide evidence for, a broad theory of cognition (i.e., connectionism), without representing a helpful theory of the particular process that is being modelled. In this sense the debate might be considered as to some extent reflecting a mere difference in the level of analysis in which particular theories are framed. Some researchers suggest that the analysis gap is the consequence of connectionist mechanisms giving rise to [[Emergence|emergent phenomena]] that may be describable in computational terms.<ref>{{Cite journal|last=Ellis|first=Nick C.|date=1998|title=Emergentism, Connectionism and Language Learning|url=http://www-personal.umich.edu/~ncellis/NickEllis/Publications_files/Emergentism.pdf|journal=Language Learning|volume=48:4|pages=631–664|via=}}</ref>
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