A theory of cortical responses

By Karl J. Friston. 2005

4,459 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Representation & Self-Supervised Learning, Neuroscience

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Citations in the corpus

Representation Learning with Contrastive Predictive Coding (2018, methodology): “In neuroscience, predictive coding theories suggest that the brain predicts observations at various levels of abstraction [7, 8].”
Theory of Mind: A Neural Prediction Problem (2013, neutral): “By hypothesis, predictions codes are more precise, more computationally efficient, and less noisy than error codes (Friston, 2005; Jehee and Ballard, 2009; Rao and Ballard, 1999; Spratling, 2008).”
Assessing the Role of the ‘Unity Assumption’ on Multisensory Integration: A Review (2017, background): “This view suggests that human brain is proactive in predicting the state of the outside world and so the incoming sensory signals; the received sensory signals, in turn, provide sensory evidence to verify the predictions (e.g., Friston, 2005; Bar, 2007; Friston et al., 2015; Kok and de Lange, 2015).”
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning (2016, methodology): “Predictive coding posits that the brain is continually making predictions of incoming sensory stimuli [11, 13, 39].”
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations (2019, background): “Rather it involves a creative interplay between external stimulation and an active, internal model of the world (Rao & Ballard, 1999; Friston, 2005).”
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions. (2017, neutral): “Cortical function can be described in a representation framework that is based on the notion of the feature detector (Barlow, 1953; Marr, 1982) or in a predictive processing framework (Clark, 2013; Friston, 2005; Hawkins and Blakeslee, 2004).”
Repetition and the brain: neural models of stimulus-specific effects. (2006, methodology): “An example of such a dynamical, network model is sketched by Friston [ 69 ].”
A tutorial on the free-energy framework for modelling perception and learning (2017, methodology): “There are many articles which provide an intuition for the free-energy framework and discuss how it relates with other theories and experimental data (Friston, 2003, 2005, 2010; Friston et al., 2013).”
Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields (2012, background): “…ES a viable alternative explanation for the observed RS. Recent theoretical models of sensory processing inspire the hypothesis that RS and ES may be manifestations of prediction errors on different time scales (Friston, 2005; Kiebel et al., 2008; Garrido et al., 2009; Wacongne et al., 2012).”
Reinforcement Learning or Active Inference? (2009, methodology): “Under this specification, optimum behaviour emerges in agents that conform to a free-energy principle, which provides a principled basis for understanding both action and perception [9,10].”
Neural Elements for Predictive Coding (2016, methodology): “The generative model rests on empirical Bayes and is necessarily hierarchical (Friston, 2005).”
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale (2022, result): “This result can be connected to findings in neuroscience that suggested that the brain maximizes predictive information at an abstract level [20, 21].”
From motor to sensory processing in mirror neuron computational modelling (2010, background): “urations is a multi-valued function, thus assuming the form of an inverse ill-posed problem (Friston 2005; Fritsch 2007).”
Attention Reverses the Effect of Prediction in Silencing Sensory Signals (2011, neutral): “Predictive coding has become a highly influential theory of perceptual inference in the last decade (Rao and Ballard 1999; Friston 2005, 2009) but has been challenged by the observation that prediction enhances rather than reduces neural responses to task-relevant stimuli (Koch and Poggio 1999; for…”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, background): “…model has played a central role in numerous recent theories of brain function, both in visual neuroscience (Ballard, Hinton, & Sejnowski, 1983; Barlow, 1969; Lee & Mumford, 2003; Rao & Ballard, 1999) and elsewhere (Dayan et al., 1995; Friston, 2005; Knill & Pouget, 2004; Mumford, 1992, 1994).”
The Conscious Id (2013, methodology): “The starting point of the free-energy framework (Friston, 2005) is that the world is an uncertain place for self-organizing biological agents to survive.”
Reconciling Predictive Coding and Biased Competition Models of Cortical Function (2008, methodology): “The current analysis makes it clear that predictive coding is also compatible with suppression of the fMRI signal due to reﬁnement of the representation formed in the prediction nodes (as previously noted by Friston, 2005).”