Karl J. Friston

Last updated

Karl Friston
Born
Karl John Friston

(1959-07-12) 12 July 1959 (age 60) [1]
York, England
NationalityBritish
Alma mater Gonville and Caius College, Cambridge (BA, 1980)
Known for Statistical parametric mapping, Voxel-based morphometry, Dynamic causal modelling, Free energy principle
Spouse(s)Ann Elisabeth Leonard [1]
Awards
Scientific career
Fields Neuroscience
Institutions University College London [3]
Influences Geoffrey Hinton [ citation needed ], Donald O. Hebb
Website www.fil.ion.ucl.ac.uk/~karl

Karl John Friston FRS, FMedSci, FRSB, is a British neuroscientist at University College London and an authority on brain imaging. [3] [4] [5] [6] [7] [8] [9] [10]

Contents

Education

Friston studied natural sciences (physics and psychology) at the University of Cambridge in 1980, and completed his medical studies at King's College Hospital, London. [1]

Career

Friston subsequently qualified under the Oxford University Rotational Training Scheme in Psychiatry, and is now a Professor of Neuroscience at University College London. [11] He is currently a Wellcome Trust Principal Fellow and Scientific Director of the Wellcome Trust Centre for Neuroimaging. [12] [13] He also holds an honorary consultant post at the National Hospital for Neurology and Neurosurgery. He invented statistical parametric mapping: SPM is an international standard for analysing imaging data and rests on the general linear model and random field theory (developed with Keith Worsley). In 1994 his group developed voxel-based morphometry. [14] VBM detects differences in neuroanatomy and is used clinically and as a surrogate in genetic studies.

These technical contributions were motivated by schizophrenia research and theoretical studies of value-learning (with Gerry Edelman). In 1995, this work was formulated as the disconnection hypothesis of schizophrenia (with Chris Frith). In 2003, he invented dynamic causal modelling (DCM), which is used to infer the architecture of distributed systems like the brain. Mathematical contributions include variational (generalised) filtering and dynamic expectation maximisation (DEM), which are Variational Bayesian methods for time-series analysis. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a variational Free energy principle [15] (active inference in the Bayesian brain [16] ). According to Google Scholar Karl Friston's h-index is 232. [3]

Awards and achievements

In 1996, Friston received the first Young Investigators Award in Human Brain Mapping, and was elected a Fellow of the Academy of Medical Sciences (1999) in recognition of contributions to the bio-medical sciences. In 2000 he was President of the international Organization for Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006 and received a Collège de France Medal in 2008. In 2011 he received an Honorary Doctorate from the University of York and became a Fellow of the Society of Biology. His nomination for the Royal Society reads

Karl Friston pioneered and developed the single most powerful technique for analysing the results of brain imaging studies and unravelling the patterns of cortical activity and the relationship of different cortical areas to one another. Currently over 90% of papers published in brain imaging use his method (SPM or Statistical Parametric Mapping) and this approach is now finding more diverse applications, for example, in the analysis of EEG and MEG data. His method has revolutionised studies of the human brain and given us profound insights into its operations. None has had as major an influence as Friston on the development of human brain studies in the past twenty-five years. [2]

In 2016 he was ranked No. 1 by Semantic Scholar in the list of top 10 most influential neuroscientists. [17]

Related Research Articles

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Savant syndrome is a condition in which someone with significant mental disabilities demonstrates certain abilities far in excess of average. The skills at which savants excel are generally related to memory. This may include rapid calculation, artistic ability, map making, or musical ability. Usually just one special skill is present.

Claustrum structure in the brain, gray matter lamina located underneath the inner neocortex

The claustrum is a thin, bilateral structure which connects to cortical and subcortical regions of the brain. It is located between the insula laterally and the putamen medially, separated by the extreme and external capsules respectively. The blood supply to the claustrum is fulfilled via the middle cerebral artery. It is considered to be the most densely connected structure in the brain allowing for integration of various cortical inputs into one experience rather than singular events. The claustrum is difficult to study given the limited number of individuals with claustral lesions and the poor resolution of neuroimaging.

Antonio Damasio neuroscientist and professor at the University of Southern California

Antonio Damasio is a Portuguese-American neuroscientist. He is currently the David Dornsife Chair in Neuroscience, as well as Professor of Psychology, Philosophy, and Neurology, at the University of Southern California, and, additionally, an adjunct professor at the Salk Institute. He was previously the chair of neurology at the University of Iowa for 20 years. Damasio heads the Brain and Creativity Institute, and has authored several books: his most recent work, Self Comes to Mind: Constructing the Conscious Brain (2010), explores the relationship between the brain and consciousness. Damasio's research in neuroscience has shown that emotions play a central role in social cognition and decision-making.

Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.

Retinotopy Mapping of visual input from the retina to neurons

Retinotopy is the mapping of visual input from the retina to neurons, particularly those neurons within the visual stream. For clarity, 'retinotopy' can be replaced with 'retinal mapping', and 'retinotopic' with 'retinally mapped'.

Brain mapping is a set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the brain resulting in maps.

BrainMaps NIH-funded interactive zoomable high-resolution digital brain atlas and virtual microscope

BrainMaps is an NIH-funded interactive zoomable high-resolution digital brain atlas and virtual microscope that is based on more than 140 million megapixels of scanned images of serial sections of both primate and non-primate brains and that is integrated with a high-speed database for querying and retrieving data about brain structure and function over the internet.

Voxel-based morphometry set of methods for image analysis

Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images.

Connectome Comprehensive map of neural connections in the brain

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Bayesian model reduction is a method for computing the evidence and posterior over the parameters of Bayesian models that differ in their priors. A full model is fitted to data using standard approaches. Hypotheses are then tested by defining one or more 'reduced' models with alternative priors, which usually – in the limit – switch off certain parameters. The evidence and parameters of the reduced models can then be computed from the evidence and estimated (posterior) parameters of the full model using Bayesian model reduction. If the priors and posteriors are normally distributed, then there is an analytic solution which can be computed rapidly. This has multiple scientific and engineering applications: these include scoring the evidence for large numbers of models very quickly and facilitating the estimation of hierarchical models.

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References

  1. 1 2 3 "FRISTON, Prof. Karl John". Who's Who 2014, A & C Black, an imprint of Bloomsbury Publishing plc, 2014; online edn, Oxford University Press.(subscription required)
  2. 1 2 "EC/2006/16: Friston, Karl John". London: The Royal Society. Archived from the original on 19 July 2014.
  3. 1 2 3 Karl J. Friston publications indexed by Google Scholar
  4. Friston, K (2003). "Learning and inference in the brain". Neural Networks. 16 (9): 1325–52. CiteSeerX   10.1.1.160.2313 . doi:10.1016/j.neunet.2003.06.005. PMID   14622888.
  5. Friston, K (2002). "Functional integration and inference in the brain". Progress in Neurobiology. 68 (2): 113–43. doi:10.1016/s0301-0082(02)00076-x. PMID   12450490.
  6. Friston, K (2005). "A theory of cortical responses". Philosophical Transactions of the Royal Society B: Biological Sciences. 360 (1456): 815–36. doi:10.1098/rstb.2005.1622. PMC   1569488 . PMID   15937014.
  7. Karl J. Friston's publications indexed by the Scopus bibliographic database. (subscription required)
  8. Penny, W; Ghahramani, Z; Friston, K (2005). "Bilinear dynamical systems". Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences . 360 (1457): 983–93. doi:10.1098/rstb.2005.1642. PMC   1854926 . PMID   16087442. Open Access logo PLoS transparent.svg
  9. Harrison, L. M.; David, O; Friston, K. J. (2005). "Stochastic models of neuronal dynamics". Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 360 (1457): 1075–91. doi:10.1098/rstb.2005.1648. PMC   1854931 . PMID   16087449.
  10. David, O; Harrison, L; Friston, K. J. (2005). "Modelling event-related responses in the brain". NeuroImage. 25 (3): 756–70. doi:10.1016/j.neuroimage.2004.12.030. PMID   15808977.
  11. "Iris View Profile". University College London. Retrieved 20 July 2014.
  12. "Professor Karl Friston – Selected papers".Cite journal requires |journal= (help)
  13. Brown, Harriet (2012). "Free-Energy and Illusions: The Cornsweet Effect". Frontiers in Psychology. 3: 43. doi:10.3389/fpsyg.2012.00043. PMC   3289982 . PMID   22393327.
  14. Wright, I.C. (1995). "A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia". NeuroImage. 2 (4): 244–252. doi:10.1006/nimg.1995.1032. PMID   9343609.
  15. Raviv, Shaun (13 November 2018). "The Genius Neuroscientist Who Might Hold the Key to True AI". WIRED. Retrieved 16 November 2018.
  16. Friston, Karl (2018). "Of woodlice and men: A Bayesian account of cognition, life and consciousness. An interview with Karl Friston (by Martin Fortier & Daniel Friedman)". ALIUS Bulletin. 2: 17–43.
  17. Bohannon, John (11 November 2016). "A computer program just ranked the most influential brain scientists of the modern era". sciencemag.org. Retrieved 5 January 2017.