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Full-Text Articles in Computational Neuroscience
A Defense Of Pure Connectionism, Alex B. Kiefer
A Defense Of Pure Connectionism, Alex B. Kiefer
Dissertations, Theses, and Capstone Projects
Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming …
Where Do I Know That? A Distributed Multimodal Model Of Semantic Knowledge, Kevin M. Stubbs
Where Do I Know That? A Distributed Multimodal Model Of Semantic Knowledge, Kevin M. Stubbs
Undergraduate Honors Theses
As computers have grown more and more powerful, computational modeling has become an increasingly valuable tool for evaluating real world findings. Likewise, brain imaging has become increasingly powerful as is evidenced by recent fMRI findings which support the exciting possibility that semantic memory is segregated by modality in the brain (Goldberg et al., 2006b). The present study utilizes connectionist modeling to put the distributed multi-modal framework of semantic memory to the test, and represents the next step forward in the line of sensory-functional models. This model, based around the McRae et al. (2005) feature production norms, includes individual implementations of …