connectionism

E899009

Connectionism is a cognitive science and artificial intelligence approach that models mental processes using networks of simple, interconnected units whose learning and behavior emerge from patterns of activation and weight adjustment.

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Statements (51)

Predicate Object
instanceOf approach in artificial intelligence
computational paradigm
model of cognition
theoretical approach in cognitive science
alsoKnownAs PDP NERFINISHED
neural network approach
parallel distributed processing NERFINISHED
associatedWork Parallel Distributed Processing volumes NERFINISHED
contrastsWith classical computationalism
rule-based models of cognition
symbolic AI
coreIdea cognition arises from distributed representations
knowledge is stored in patterns of connection weights
mental processes are modeled as emergent from networks of simple units
processing occurs in parallel across many units
emergedIn 1980s
fieldOfStudy artificial intelligence
cognitive science
computational neuroscience NERFINISHED
influenced computational models of language
deep learning
models of memory
models of perception
modern neural network research
influencedBy neuroscience
psychology
statistical learning theory
learningMechanism Hebbian learning
backpropagation
error-driven learning
weight adjustment based on experience
models language processing
learning and generalization
memory retrieval
pattern recognition
notableProponent David E. Rumelhart NERFINISHED
Geoffrey Hinton NERFINISHED
James L. McClelland NERFINISHED
philosophicalIssue explanatory adequacy for higher-level cognition
relationship between distributed and symbolic representations
typicalArchitecture autoassociative network
feedforward network
recurrent network
typicalUnit simple neuron-like processing unit
usesConcept activation pattern
artificial neural network
connection weight
content-addressable memory
distributed representation
graceful degradation
learning rule

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Donald Hebb influenced connectionism