Cognitive computational neuroscience

By Nikolaus Kriegeskorte, Pamela K. Douglas. Nature Neuroscience, 2018

438 citations recorded by the source corpus.

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data. The authors review recent work at the intersection of cognitive science, computational neuroscience and artificial intelligence that develops and tests computational models mimicking neural and cognitive function during a wide range of tasks.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fe65133015723d49f8090e0aec099e7b61b8a54b

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Citations in the corpus

Neural network models and deep learning - a primer for biologists (2019, background): “For neuroscientists, these models offer a way of specifying mechanistic hypotheses on how cognitive functions may be carried out by brains (Kriegeskorte and Douglas, 2018; Kietzmann et al., 2019; Storrs and Kriegeskorte, in press).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “The human brain is one of the most complex networks in the world, and studies on its static and dynamic properties have undergone explosive growth in recent years (Bullmore and Sporns, 2012; Sporns, 2013b; Kriegeskorte and Douglas, 2018).”
Deep Neural Networks as Scientific Models. (2019, neutral): “They see promise for a new convergence of arti ﬁ cial intelligence (AI) and neuroscience/psychology, and a new framework for relating brain function and complex real-world behaviours [48,79,81,82].”
The Notorious Difficulty of Comparing Human and Machine Perception (2020, positive): “…the enticing possibility that we may learn from one system by studying the other (Hassabis, Kumaran, Summerﬁeld, & Botvinick, 2017; Jozwik, Kriegeskorte, Cichy, & Mur, 2018; Kar, Kubilius, Schmidt, Issa, & DiCarlo, 2019; Kriegeskorte & Douglas, 2018; Lake, Ullman, Tenenbaum, & Gershman, 2017).”