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{{more footnotes|date=April 2014}}
'''Connectionism''' is an approach in the fields of [[cognitive science]] that hopes to explain [[mind|mental]] phenomena using [[artificial neural networks]] (ANN).<ref name=":0">{{cite book|url=https://plato.stanford.edu/archives/fall2018/entries/connectionism/|title=The Stanford Encyclopedia of Philosophy|first=James|last=Garson|editor-first=Edward N.|editor-last=Zalta|date=27 November 2018|publisher=Metaphysics Research Lab, Stanford University|via=Stanford Encyclopedia of Philosophy}}</ref> Connectionism presents a cognitive theory based on simultaneously occurring, distributed signal activity via connections that can be represented numerically, where learning occurs by modifying connection strengths based on experience.<ref name=":1">{{Cite journal|last=Smolensky|first=Paul|date=1999|title=Grammar-based Connectionist Approaches to Language|url=http://csjarchive.cogsci.rpi.edu/1999v23/i04/p0589p0613/MAIN.PDF|journal=Cognitive Science|volume=23|issue=4|pages=
[[File:Artificial_neural_network.svg|thumb|Connectionist (ANN) model with a hidden layer]]
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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/
===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
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 <ref name=":4" />.
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Though PDP is the dominant form of connectionism, other theoretical work should also be classified as connectionist.
Many connectionist principles can be traced to early work in [[psychology]], such as that of [[William James]].<ref>{{cite book |last1=Anderson |first1= James A.|last2=Rosenfeld |first2= Edward |date= 1989|title= Neurocomputing: Foundations of Research|chapter-url= |location= |publisher= A Bradford Book |page= 1|chapter = Chapter 1: (1890) William James ''Psychology (Brief Course)'' |isbn=978-0262510486 |accessdate= }}</ref> Psychological theories based on knowledge about the human brain were fashionable in the late 19th century. As early as 1869, the neurologist [[John Hughlings Jackson]] argued for multi-level, distributed systems. Following from this lead, [[Herbert Spencer]]'s ''Principles of Psychology'', 3rd edition (1872), and [[Sigmund Freud]]'s ''Project for a Scientific Psychology'' (composed 1895) propounded connectionist or proto-connectionist theories. These tended to be speculative theories. But by the early 20th century, [[Edward Thorndike]] was experimenting on learning that posited a connectionist type network.
[[Friedrich Hayek]] independently conceived the Hebbian synapse learning model in a paper presented in 1920 and developed that model into global brain theory constituted of networks Hebbian synapses building into larger systems of maps and memory network {{Citation needed|date=March 2015}}. Hayek's breakthrough work was cited by Frank Rosenblatt in his perceptron paper.
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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
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=1993/09|title=From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony
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=
The recent{{when|date=February 2016}} popularity of [[Cognitive Model#Dynamical systems|dynamical systems]] in [[philosophy of mind]] have added a new perspective on the debate; some authors{{which|date=February 2016}} now argue that any split between connectionism and computationalism is more conclusively characterized as a split between computationalism and [[Cognitive Model#Dynamical systems|dynamical systems]].
In 2014, [[Alex Graves (computer scientist)|Alex Graves]] and others from [[DeepMind]] published a series of papers describing a novel Deep Neural Network structure called the [[Neural Turing Machine]]<ref>{{cite arxiv|last1=Graves|first1=Alex|title=Neural Turing Machines|
==See also==
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