Theory of cortical function

By David J. Heeger. 2017

214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Neuroscience

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “Evidence on cortical function has shown that neural activity in multiple brain areas results from the combination of bottom-up sensory drive, top-down feedback, and prior knowledge and expectations (Heeger, 2017).”
The hardware lottery (2020, methodology): “What we see is largely virtual reality computed from memory (Eagleman & Sejnowski, 2000; Bubic et al., 2010; Heeger, 2017).”
Predictive Processing: A Canonical Cortical Computation (2018, unknown): “Interestingly, a model that has been proposed recently as an alternative to hierarchical predictive processing is a variant of a predictive processing architecture in which the flow of signals is reversed, predictions are sent up the and errors down the hierarchy (Heeger, 2017).”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “This resonates with the notion of ‘‘predictive coding’’ in neuroscience, which has gained momentum over the past two decades (Rao and Ballard, 1999) and is a central pillar of recent, optimization-oriented theories of brain function (Friston, 2010; Clark, 2013; Heeger, 2017).”
Switching between internal and external modes: A multiscale learning principle (2017, background): “Secondly, switching is an important ingredient in algorithms that converge toward accurate high-dimensional internal models (Bengio et al., 2015; Heeger, 2017; O’Reilly, 1996).”
Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction (2018, background): “It is assumed that the predictive ability of sensory perception interpolates inevitable neural delay and that human behaviors are adapted to the world that is progressing in real time (Nijhawan, 2008; Heeger, 2017).”
Generative Predictive Codes by Multiplexed Hippocampal Neuronal Tuplets. (2018, unknown): “A possible framework for optimizing speed and capacity of encoding is provided by the predictive coding theory, which proposes that the brain is a Bayesian interpreter that largely computes the difference between external stimuli and a prior, unconscious, internal model of the world (Bialek et al., ”