Learning and inference in the brain

  title={Learning and inference in the brain},
  author={Karl J. Friston},
  journal={Neural networks : the official journal of the International Neural Network Society},
  volume={16 9},
  • Karl J. Friston
  • Published 1 November 2003
  • Psychology
  • Neural networks : the official journal of the International Neural Network Society

A theory of cortical responses

  • Karl J. Friston
  • Biology, Psychology
    Philosophical Transactions of the Royal Society B: Biological Sciences
  • 2005
The aims of this article are to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective and to provide a principled way to understand many aspects of cortical organization and responses.

A free energy principle for the brain

A Bayesian and Emergent View of the Brain

Although the theory accounts for the automatic, infraconscious side of the processing of information in the brain, it is in good accord with Roger Sperry’s theory of consciousness as a theory of strong emergence and the solidity of the law of “minimization of free energy” proposed by Friston is too soon to evaluate.

Prediction error dependent changes in brain connectivity during associative learning

The work presented in this thesis constitutes the first direct report that prediction error activity in the amygdala exerts a modulatory influence on visuo-striatal connections.

Neural surprise in somatosensory Bayesian learning

The cortical dynamics of the somatosensory learning system is described to investigate both the form of the generative model as well as its neural surprise signatures, and to provide a dissociation of the neural correlates of belief inadequacy and belief updating.

Predictive coding under the free-energy principle

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states.

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

It is argued that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

Causal modelling of evoked brain responses.

This thesis discusses the generation of the MMN in the light of predictive coding, and shows the usefulness of DCM in explaining how cortical activity is expressed at the scalp level and exploits the potential ofDCM for testing hierarchical models underlying the MMn.

The hypothesis testing brain: Some philosophical applications

According to one theory, the brain is a sophisticated hypothesis tester: perception is Bayesian unconscious inference where the brain actively uses predictions to test, and then refine, models about



Functional integration and inference in the brain

Beyond phrenology: what can neuroimaging tell us about distributed circuitry?

It will be shown that the conjoint manipulation of bottom-up and top-up inputs to an area can be used to test for interactions between them, in elaborating cortical responses, and the prevalence of top-down influences and the plausibility of generative models of sensory brain function are pointed to.

Principal component analysis learning algorithms: a neurobiological analysis

By using simulations, it is demonstrated that PCA-like mechanisms can eliminate afferent connections whose signals are unrelated to the prevalent pattern of afferent activity and may be instrumental in refining extrinsic cortico-cortical connections that underlie functional segregation.

Towards a network theory of cognition

Cortical plasticity: from synapses to maps.

The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an emphasis on the work that attempts to unite both fields, to highlight the gaps in the understanding of synaptic and cellular mechanisms underlying cortical representational plasticity.

On the computational architecture of the neocortex. II. The role of cortico-cortical loops.

A hypothesis on the role of the reciprocal, topographic pathways between two cortical areas, one often a 'higher' area dealing with more abstract information about the world, the other 'lower', deals with more concrete data, is put forward.

A measure for brain complexity: relating functional segregation and integration in the nervous system.

A measure, called neural complexity (CN), that captures the interplay between functional segregation and functional integration in brains of higher vertebrates and may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.

On the computational architecture of the neocortex

  • D. Mumford
  • Computer Science
    Biological Cybernetics
  • 2004
A hypothesis on the role of the reciprocal, topographic pathways between two cortical areas, one often a ‘higher’ area dealing with more abstract information about the world, the other ‘lower’, deals with more concrete data is put forward.

Completing the Corticofugal Loop: A Visual Role for the Corticogeniculate Type 1 Metabotropic Glutamate Receptor

This work shows that in adult cats the cortex uses a synaptic drive mediated by type 1 metabotropic glutamate receptors (mGluR1) specifically to enhance the excitatory center of the thalamic receptive field.

Entropy and cortical activity: information theory and PET findings.

This article shows that the antisymmetric arrangement of functional activity in convergent and divergent connections underlying functional segregation is exactly that predicted by the principle of maximum preservation of information, considered in the context of axonal bifurcation.