Dr Dimitrios Pinotsis • City, University of London

Contact details

Address

Dr Dimitrios Pinotsis A229A , College Building [A]
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom

Personal links

About
Publications

About

Overview

www.pinotsislab.com Dr Dimitris Pinotsis is a Reader (Associate Professor II ) at the Centre for Mathematical Neuroscience and Psychology and the Department of Psychology at City, University of London.

He is also a Research Affiliate at the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT), where he worked between 2016 and 2018.

Dimitris has also been a Senior Research Fellow at UCL’s Wellcome Trust Center for Neuroimaging where he worked with Karl Friston and a senior Scientist at MIT where he worked with Earl Miller. He holds a PhD in Mathematics and an MSc in Theoretical Physics from the Department of Applied Mathematics and Theoretical Physics (DAMTP) of the University of Cambridge, UK.

Dimitris' research has been funded by US NIH, UKRI, US Air Force Office of Scientific Research, EPSRC, Gambling Commission and the Wellcome Trust. It spans diverse areas including machine learning, the analysis of big data in neuroimaging, theoretical neurobiology and nonlinear systems in mathematical physics.

In recent work, Dimitris exploits deep neural networks and hierarchical Bayesian inference to understand the causes of neurological and psychiatric disorders. He also exploits brain recordings to build better artificial intelligence algorithms.

Dimitris is an Expert Reviewer for US Air Force Office of Scientific Research, the Austrian Science Fund and the Italian Ministry of Health. He tweets at @dimitrispp and shares his work at researchgate.net

Interested students please get in touch. There are opportunities for theoretical and experimental projects in collaboration with colleagues at MIT, Harvard, UCSF, Toronto, UCL, King's and elsewhere.

Qualifications

PhD, Mathematics, University of Cambridge, United Kingdom
MSc, Theoretical Physics, University of Cambridge, United Kingdom
BSc, Physics, University of Athens, Greece

Administrative roles

SASS Global Engagement, Nov 2020 – present
Ethics Committee
Research Committee
AM Committee

Employment

Reader, City, University of London, Sep 2021 – present
Co-Chair Human Insights Council, British Interactive Media Association (BIMA), Feb 2020 – present
Senior Lecturer (Associate Professor I), City, University of London, Apr 2019 – Aug 2021
Deputy Director, MSc Clinical and Cognitive Neuroscience, City, University London, Sep 2018 – Sep 2019
Lecturer (Assistant Professor) in Theoretical Neuroscience, City, University London, Feb 2018 – Mar 2019
Research Affiliate, Massachusetts Institute of Technology, Feb 2018 – present
Research Scientist, Massachusetts Institute of Technology, Jan 2016 – Jan 2018
Senior Research Associate, University College London, Oct 2009 – Dec 2015

Languages

French.

Publications

Publications by category

Book

Pinotsis, D., Robinson, P., beim Graben, P. and Friston, K. (Eds.), Neural Masses and Fields: Modelling the Dynamics of Brain Activity. Frontiers Media SA. ISBN 978-2-88919-427-8.

Chapters (4)

Pinotsis, D.A. and Miller, E.K. (2017). New approaches for studying cortical representations. (pp. 613–615).
Li, B., Ahlfors, S.P., Pinotsis, D., Friston, K.J. and Mody, M. (2017). Causal Modeling: Methods and Their Application to Speech and Language. Innovations in Cognitive Neuroscience (pp. 155–174). Springer US. ISBN 978-1-4939-7323-1.
Marreiros, A.C., Pinotsis, D.A., Brown, P. and Friston, K.J. (2015). DCM, Conductance Based Models and Clinical Applications. Springer Series in Computational Neuroscience (pp. 43–70). Springer International Publishing. ISBN 978-3-319-20036-1.
Pinotsis, D.A. and Friston, K.J. (2014). Neural Fields, Masses and Bayesian Modelling. Neural Fields (pp. 433–455). Springer Berlin Heidelberg. ISBN 978-3-642-54592-4.

Conference papers and proceedings (8)

Van de Steen, F., Pinotsis, D., Devos, W., Friston, K. and Marinazzo, D. (2020). Modelling EEG alpha power in eyes-open and eyes-closed states using DCM. Organization Human Brain Mapping.
Karanasiou, A. and Pinotsis, D. (2017). Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning. The 16th International Conference on Articial Intelligence and Law 12-16 June, London, UK. doi:10.1145/3086512.3086524
Diez, A., Ranlund, S., Pinotsis, D., Calafato, S., Shaikh, M., Hall, M.-.H. … Bramon, E. (2017). ABNORMAL FRONTAL SYNAPTIC GAIN MEDIATING THE P300 IN PATIENTS WITH PSYCHOSIS AND THEIR UNAFFECTED RELATIVES.
Diez, A., Ranlund, S.M., Brown, H., Adams, R.A., Walshe, M., Murray, R.M. … Bramon, E. (2014). DYNAMIC CAUSAL MODELLING OF ABNORMAL FRONTAL EVOKED GAMMA BAND ACTIVITY IN PATIENTS WITH SCHIZOPHRENIA. doi:10.1016/S0920-9964(14)70459-1
Ranlund, S.M., Diez, A., Adams, R.A., Brown, H., Walshe, M., Murray, R.M. … Bramon, E. (2014). EFFECTIVE CONNECTIVITY IN SCHIZOPHRENIA - DYNAMIC CAUSAL MODELLING OF THE MISMATCH NEGATIVITY. doi:10.1016/S0920-9964(14)71006-0
Pinotsis, D.A. (2010). Segre quaternions, spectral analysis and a four-dimensional Laplace equation. Proceedings of the 7th International ISAAC Congress. doi:10.1142/9789814313179_0032
Pinotsis, D.A. (2010). Quaternionic Analysis, Elliptic Problems and a Physical Application of the Dbar Formalism. doi:10.1007/s00006-010-0224-x
PINOTSIS, D.A. (2006). A NONLOCAL FORMULATION OF THE DBAR FORMALISM AND BOUNDARY VALUE PROBLEMS IN TWO DIMENSIONS. Proceedings of the Seventh International Workshop. doi:10.1142/9789812773197_0021

Journal articles (52)

Bricault, S., Dawson, M., Lee, J., Desai, M., Schwalm, M., Chung, K.S. … Jasanoff, A. (2024). Peripheral contributions to resting state brain dynamics. Nature Communications, 15(1). doi:10.1038/s41467-024-55064-6.
[publisher’s website]
Pinotsis, D.A. and Miller, E.K. (2023). In vivo ephaptic coupling allows memory network formation. Cerebral Cortex, 33(17), pp. 9877–9895. doi:10.1093/cercor/bhad251.
Pinotsis, D.A., Fridman, G. and Miller, E.K. (2023). Cytoelectric coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure. Progress in Neurobiology, 226, pp. 102465–102465. doi:10.1016/j.pneurobio.2023.102465.
[publisher’s website]
Van de Steen, F., Pinotsis, D., Devos, W., Colenbier, N., Bassez, I., Friston, K. … Marinazzo, D. (2022). Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex. PLOS Computational Biology, 18(12). doi:10.1371/journal.pcbi.1009988.
Pinotsis, D.A., Fitzgerald, S., See, C., Sementsova, A. and Widge, A.S. (2022). Toward biophysical markers of depression vulnerability. Frontiers in Psychiatry, 13. doi:10.3389/fpsyt.2022.938694.
Adams, R.A., Pinotsis, D., Tsirlis, K., Unruh, L., Mahajan, A., Horas, A.M. … Anticevic, A. (2022). Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia. Biological Psychiatry, 91(2), pp. 202–215. doi:10.1016/j.biopsych.2021.07.024.
Pinotsis, D.A. and Miller, E.K. (2022). Beyond dimension reduction: Stable electric fields emerge from and allow representational drift. NeuroImage, 253. doi:10.1016/j.neuroimage.2022.119058.
Adams, R., Pinotsis, D., Tsirlis, K., Ji, J.L., Repovs, G., Murray, J. … Anticevic, A. (2021). Computational Modelling of EEG and fMRI Paradigms Reveals a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia. BIOLOGICAL PSYCHIATRY, 89(9).
Pinotsis, D.A. (2020). Statistical decision theory and multiscale analyses of human brain data. Journal of Neuroscience Methods, 346, pp. 108912–108912. doi:10.1016/j.jneumeth.2020.108912.
[publisher’s website]
Pinotsis, D.A. and Miller, E.K. (2020). Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity. Communications Biology, 3(1). doi:10.1038/s42003-020-01438-7.
[publisher’s website]
Min, B.-.K., Kim, H.S., Pinotsis, D.A. and Pantazis, D. (2020). Thalamocortical inhibitory dynamics support conscious perception. NeuroImage, 220, pp. 117066–117066. doi:10.1016/j.neuroimage.2020.117066.
[publisher’s website]
Ruffini, G., Sanchez-Todo, R., Dubreuil, L., Salvador, R., Pinotsis, D., Miller, E.K. … Bastos, A. (2020). P118 A Biophysically realistic Laminar Neural Mass Modeling framework for transcranial Current Stimulation. Clinical Neurophysiology, 131(4). doi:10.1016/j.clinph.2019.12.229.
[publisher’s website]
Pinotsis, D.A., Siegel, M. and Miller, E.K. (2019). Sensory processing and categorization in cortical and deep neural networks. NeuroImage, 202. doi:10.1016/j.neuroimage.2019.116118.
Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2019). Working Memory Load Modulates Neuronal Coupling. Cerebral Cortex, 29(4), pp. 1670–1681. doi:10.1093/cercor/bhy065.
[publisher’s website]
Pinotsis, D.A., Brincat, S.L. and Miller, E.K. (2017). On memories, neural ensembles and mental flexibility. NeuroImage, 157, pp. 297–313. doi:10.1016/j.neuroimage.2017.05.068.
Díez, Á., Ranlund, S., Pinotsis, D., Calafato, S., Shaikh, M., Hall, M. … Bramon, E. (2017). Abnormal frontoparietal synaptic gain mediating the P300 in patients with psychotic disorder and their unaffected relatives. Human Brain Mapping, 38(6), pp. 3262–3276. doi:10.1002/hbm.23588.
[publisher’s website]
Karanasiou, A.P. and Pinotsis, D.A. (2017). A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning. International Review of Law, Computers and Technology, 31(2), pp. 170–187. doi:10.1080/13600869.2017.1298499.
Diez, A., Ranlund, S., Pinotsis, D., Calafato, S., Shaikh, M., Hall, M.-.H. … Bramon, E. (2017). SU72. Abnormal Frontal Synaptic Gain Mediating the P300 in Patients With Psychosis and Their Unaffected Relatives. Schizophrenia Bulletin, 43(suppl_1). doi:10.1093/schbul/sbx024.070.
[publisher’s website]
Pinotsis, D.A., Geerts, J.P., Pinto, L., FitzGerald, T.H.B., Litvak, V., Auksztulewicz, R. … Friston, K.J. (2017). Linking canonical microcircuits and neuronal activity: Dynamic causal modelling of laminar recordings. NeuroImage, 146, pp. 355–366. doi:10.1016/j.neuroimage.2016.11.041.
Pinotsis, D.A., Perry, G., Litvak, V., Singh, K.D. and Friston, K.J. (2016). Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields. Human Brain Mapping, 37(12), pp. 4597–4614. doi:10.1002/hbm.23331.
Pinotsis, D.A., Loonis, R., Bastos, A.M., Miller, E.K. and Friston, K.J. (2016). Bayesian Modelling of Induced Responses and Neuronal Rhythms. Brain Topography pp. 1–14. doi:10.1007/s10548-016-0526-y.
Adams, R.A., Bauer, M., Pinotsis, D. and Friston, K.J. (2016). Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG. NeuroImage, 132, pp. 175–189. doi:10.1016/j.neuroimage.2016.02.055.
Ranlund, S., Adams, R.A., Díez, Á., Constante, M., Dutt, A., Hall, M.H. … Bramon, E. (2015). Impaired prefrontal synaptic gain in people with psychosis and their relatives during the mismatch negativity. Human Brain Mapping, 37(1), pp. 351–365. doi:10.1002/hbm.23035.
Friston, K.J., Bastos, A.M., Pinotsis, D. and Litvak, V. (2015). LFP and oscillations-what do they tell us? Current Opinion in Neurobiology, 31, pp. 1–6. doi:10.1016/j.conb.2014.05.004.
Pinotsis, D.A. and Friston, K.J. (2014). Extracting novel information from neuroimaging data using neural fields. EPJ Nonlinear Biomedical Physics, 2(1). doi:10.1140/epjnbp18.
[publisher’s website]
Pinotsis, D.A. (2014). Extracting novel information from neuroimaging data using neural fields. BMC Neuroscience, 15(S1). doi:10.1186/1471-2202-15-s1-o4.
[publisher’s website]
Pinotsis, D.A., Brunet, N., Bastos, A., Bosman, C.A., Litvak, V., Fries, P. … Friston, K.J. (2014). Contrast gain control and horizontal interactions in V1: A DCM study. NeuroImage, 92, pp. 143–155. doi:10.1016/j.neuroimage.2014.01.047.
[publisher’s website]
Ranlund, S.M., Diez, A., Adams, R.A., Brown, H., Walshe, M., Murray, R.M. … Bramon, E. (2014). Poster #T190 EFFECTIVE CONNECTIVITY IN SCHIZOPHRENIA – DYNAMIC CAUSAL MODELLING OF THE MISMATCH NEGATIVITY. Schizophrenia Research, 153. doi:10.1016/s0920-9964(14)71006-0.
[publisher’s website]
Diez, A., Ranlund, S.M., Brown, H., Adams, R.A., Walshe, M., Murray, R.M. … Bramon, E. (2014). Poster #S180 DYNAMIC CAUSAL MODELLING OF ABNORMAL FRONTAL EVOKED GAMMA BAND ACTIVITY IN PATIENTS WITH SCHIZOPHRENIA. Schizophrenia Research, 153. doi:10.1016/s0920-9964(14)70459-1.
[publisher’s website]
Pinotsis, D. and Friston, K. (2014). Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure. AIMS Neuroscience, 1(1), pp. 18–38. doi:10.3934/neuroscience.2014.1.18.
[publisher’s website]
Dimitris, P. and Karl, F. (2014). Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits. Frontiers in Systems Neuroscience, 8. doi:10.3389/conf.fnsys.2014.05.00024.
[publisher’s website]
Pinotsis, D., Robinson, P., Graben, P.B. and Friston, K. (2013). Neural masses and fields: Modeling the dynamics of brain activity. Frontiers in Computational Neuroscience, 8. doi:10.3389/fncom.2014.00149.
Pinotsis, D.A., Leite, M. and Friston, K.J. (2013). On conductance-based neural field models. Frontiers in Computational Neuroscience, 7. doi:10.3389/fncom.2013.00158.
Moran, R., Pinotsis, D.A. and Friston, K. (2013). Neural masses and fields in dynamic causal modelling. Frontiers in Computational Neuroscience, 7. doi:10.3389/fncom.2013.00057.
Pinotsis, D.A., Schwarzkopf, D.S., Litvak, V., Rees, G., Barnes, G. and Friston, K.J. (2013). Dynamic causal modelling of lateral interactions in the visual cortex. NeuroImage, 66, pp. 563–576. doi:10.1016/j.neuroimage.2012.10.078.
[publisher’s website]
Pinotsis, D.A., Hansen, E., Friston, K.J. and Jirsa, V.K. (2013). Anatomical connectivity and the resting state activity of large cortical networks. NeuroImage, 65, pp. 127–138. doi:10.1016/j.neuroimage.2012.10.016.
[publisher’s website]
Pinotsis, D.A. (2012). Commutative quaternions, spectral analysis and boundary value problems. Complex Variables and Elliptic Equations, 57(9), pp. 953–966. doi:10.1080/17476933.2010.534148.
[publisher’s website]
Pinotsis, D.A., Moran, R.J. and Friston, K.J. (2012). Dynamic causal modeling with neural fields. NeuroImage, 59(2), pp. 1261–1274. doi:10.1016/j.neuroimage.2011.08.020.
[publisher’s website]
Pinotsis, D.A. (2011). Integral representations of displacements in linear elasticity. Applied Mathematics Letters, 24(10), pp. 1670–1675. doi:10.1016/j.aml.2011.04.014.
[publisher’s website]
Friston, K.J., Li, B., Daunizeau, J. and Stephan, K.E. (2011). Network discovery with DCM. NeuroImage, 56(3), pp. 1202–1221. doi:10.1016/j.neuroimage.2010.12.039.
[publisher’s website]
Pinotsis, D.A. and Friston, K.J. (2011). Neural fields, spectral responses and lateral connections. NeuroImage, 55(1), pp. 39–48. doi:10.1016/j.neuroimage.2010.11.081.
[publisher’s website]
Grindrod, P. and Pinotsis, D.A. (2011). On the spectra of certain integro-differential-delay problems with applications in neurodynamics. Physica D: Nonlinear Phenomena, 240(1), pp. 13–20. doi:10.1016/j.physd.2010.08.002.
[publisher’s website]
Pelloni, B. and Pinotsis, D.A. (2010). Moving boundary value problems for the wave equation. Journal of Computational and Applied Mathematics, 234(6), pp. 1685–1691. doi:10.1016/j.cam.2009.08.016.
[publisher’s website]
Pelloni, B. and Pinotsis, D.A. (2010). The elliptic sine-Gordon equation in a half plane. Nonlinearity, 23(1), pp. 77–88. doi:10.1088/0951-7715/23/1/004.
[publisher’s website]
Pelloni, B. and Pinotsis, D.A. (2009). Boundary value problems for the N-wave interaction equations. Physics Letters A, 373(22), pp. 1940–1950. doi:10.1016/j.physleta.2009.03.064.
[publisher’s website]
Pelloni, B. and Pinotsis, D.A. (2008). The Klein–Gordon Equation in a Domain with Time‐Dependent Boundary. Studies in Applied Mathematics, 121(3), pp. 291–312. doi:10.1111/j.1467-9590.2008.00416.x.
[publisher’s website]
beim Graben, P., Pinotsis, D., Saddy, D. and Potthast, R. (2008). Language processing with dynamic fields. Cognitive Neurodynamics, 2(2), pp. 79–88. doi:10.1007/s11571-008-9042-4.
[publisher’s website]
Pelloni, B. and Pinotsis, D.A. (2008). The Klein-Gordon Equation on the Half Line: a Riemann-Hilbert Approach. Journal of Nonlinear Mathematical Physics, 15(supplement 3), pp. 334–334. doi:10.2991/jnmp.2008.15.s3.32.
[publisher’s website]
Pinotsis, D.A. (2007). Quaternions and applications. PAMM, 7(1), pp. 2040057–2040058. doi:10.1002/pamm.200700736.
[publisher’s website]
Fokas, A.S. and Pinotsis, D.A. (2007). Quaternions, Evaluation of Integrals and Boundary Value Problems. Computational Methods and Function Theory, 7(2), pp. 443–476. doi:10.1007/bf03321657.
[publisher’s website]
Pinotsis, D.A. (2007). The Riemann-Hilbert Formalism For Certain Linear and Nonlinear Integrable PDEs. Journal of Nonlinear Mathematical Physics, 14(3), pp. 474–474. doi:10.2991/jnmp.2007.14.3.12.
[publisher’s website]
FOKAS, A.S. and PINOTSIS, D.A. (2006). The Dbar formalism for certain linear non-homogeneous elliptic PDEs in two dimensions. European Journal of Applied Mathematics, 17(3), pp. 323–346. doi:10.1017/s0956792506006607.
[publisher’s website]