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

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

211 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, background): “Gallistel, C. R., & King, A. P. (2010).”
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) and Marcus (Marcus, 2003) have argued that addressing must be implicated in 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).”
Problems of projection (2013, background): “It also may be, as Gallistel and King (2009) have 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.”
A Bayesian Framework for Information-Theoretic Probing (2021, background): “In fact, about the belief (2)This definition is found in both the cognitive sciences (Gallistel and King, 2011; Fan, 2014; Sayood, 2018) as well as in active learning (Houlsby et al.”
The Demise of the Synapse As the Locus of Memory: A Looming Paradigm Shift? (2016, background): “…cognitive scientists have always been critical of connectionist or network approaches to cognitive architecture (e.g., Fodor and Pylyshyn, 1988), and recent work on memory (e.g., Gallistel and King, 2009; Gallistel and Matzel, 2013; Gallistel and Balsam, 2014) has been adding fuel to the ﬁre.”
Real-time fMRI neurofeedback: Progress and challenges (2013, background): “…neuroscience theories of learning and memory are representational; learning mechanisms extract useful information from experience, while memory carries the acquired information forward in time in a computational form that could be retrieved and used by the subject (Gallistel and King, 2009).”
Language and Other Cognitive Systems. What Is Special About Language? (2011, background): “Randy Gallistel (2000) has observed that the biological norm is modular systems with special growth/learning mechanisms in different domains and in different species.”
Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, methodology): “Although spiking models have an important place in the computational literature, the models discussed here are nonspiking and do not capture dendritic computation, other processes within each neuron (e.g. Gallistel & King 2011), and distinct contributions from different types of neuron.”
Probabilistic brains: knowns and unknowns (2013, background): “Some authors have argued that this will require a computational architecture very similar to the one found in computers with, in particular, the ability to allocate and de-allocate memory resources on demand via the use of pointers 78,79 .”
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).”