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Brain-like Combinat...
Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition
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- Ravichandran, Naresh Balaji (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST),Computational Cognitive Brain Science Group
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- Lansner, Anders, Professor, 1949- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST),Stockholm Univ, Dept Math, Stockholm, Sweden.,Computational Cognitive Brain Science Group
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- Herman, Pawel, 1979- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST),Computational Cognitive Brain Science Group
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(creator_code:org_t)
- 2023-03-10
- 2023
- Engelska.
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer Nature. ; , s. 488-501
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Abstract
Ämnesord
Stäng
- Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. The resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. The associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.
Ämnesord
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Attractor
- Associative memory
- Unsupervised learning
- Hebbian learning
- Recurrent networks
- Feedforward networks
- Brain-like computing
- Datalogi
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)