Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience

By Charles R. Gallistel, A. P. King. 2009

254 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Neuroscience

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Citations in the corpus

Neural Turing Machines (2014, background): “Addressing, fundamental to our work, is usually left out from computational models in neuroscience, though it deserves to be mentioned that Gallistel and King (Gallistel and King, 2009) have argued that addressing must be fundamental to the operation of the brain.”
The hardware lottery (2020, background): “Our brains are able to ﬁne-tune and retain skills across our lifetime (Benna & Fusi, 2016; Bremner et al., 2013; Stein et al., 2004; Tani & Press, 2016; Gallistel & King, 2009; Tulving, 2002; Barnett & Ceci, 2002).”
Plato's Camera: How the Physical Brain Captures a Landscape of Abstract Universals (2012, background): “On the contrary, many of the kinds of data that are thought to support it derive from the study of nonhuman animals (Gallistel, 1990; Gallistel and King, 2009).”
The Demise of the Synapse As the Locus of Memory: A Looming Paradigm Shift? (2016, background): “This associative aspect of Hebbian learning has an intuitive appeal, as Gallistel and King (2009) recount tongue-in-cheek: How else could you explain that if you hear “salt” you will probably also think “pepper?””
Brain plasticity through the life span: learning to learn and action video games. (2012, background): “The literature has countless examples wherein learning algorithms, such as gradient descent, are implemented within neural networks to tune weights to enhance performance on the trained task (Rumelhart et al. 1987, but see Gallistel & King 2009 and Gallistel 2008 for a different view on learning).”
The neuroscience of learning: beyond the Hebbian synapse. (2013, background): “Symbols carry information forward in time (Gallistel & King 2009).”
Neuroscience-Inspired Artificial Intelligence. (2017, background): “…reinforcement-based mechanisms, which allow the value of stimuli and actions to be learned incrementally and through repeated experience, but also instance-based mechanisms, which allow experiences to be encoded rapidly (in ‘‘one shot’’) in a content-addressable store (Gallistel and King, 2009).”
Language and Other Cognitive Systems. What Is Special About Language? (2011, background): “It may be, as Randy Gallistel (Gallistel & King, 2009) has argued, that a fundamental reorientation of centuries of study of the brain will be necessary to discover the neural roots of the computational capacities not only of human language but also even of insects, where they are indeed astonishing”
Learning Long-Distance Phonotactics (2010, background): “Not only does this idea have proponents who study learning from a biological perspective (Gallistel and King 2009:Chap. 13), it is a natural interpretation of significant results from formal learning theories. Formal learning theories show that learning is impossible unless the range of hypotheses t”