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portrait of Dr Dimitris Pinotsis

Dr Dimitris Pinotsis

Lecturer in Psychology

School of Arts and Social Sciences, Department of Psychology

Contact Information


Visit Dimitris Pinotsis

A229J, College Building

Postal Address

City, University of London
Northampton Square
United Kingdom



Dr Dimitris Pinotsis is a Lecturer (Assistant Professor) 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 January 2016 and January 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 the US Air Force Office of Scientific Research, UK Research Councils (EPSRC) 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

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


  1. PhD, Mathematics, University of Cambridge, United Kingdom
  2. MSc, Theoretical Physics, University of Cambridge, United Kingdom
  3. BSc, Physics, University of Athens, Greece


  1. Deputy Director, MSc Clinical and Cognitive Neuroscience, City, University London, Sep 2018 – present
  2. Research Affiliate, Massachusetts Institute of Technology, Feb 2018 – present
  3. Lecturer (Assistant Professor) in Theoretical Neuroscience, City, University London, Feb 2018 – present
  4. Research Scientist, Massachusetts Institute of Technology, Jan 2016 – Jan 2018
  5. Senior Research Associate, University College London, Oct 2009 – Dec 2015


Chapters (3)

  1. (2017). New approaches for studying cortical representations. In (pp. 613–615). ISBN 978-1-57735-779-7.
  2. 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.
  3. (2014). Neural fields, masses and Bayesian modelling. In Neural Fields: Theory and Applications (pp. 433–455). ISBN 978-3-642-54593-1.

Conference papers and proceedings (8)

  1. 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.
  2. 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.
  5. (2010). Moving boundary value problems for the wave equation.
  6. (2010). Quaternionic Analysis, elliptic problems and a physical application of the Dbar formalism.
  7. (2008). The Klein-Gordon equation on the half line: A Riemann-Hilbert approach.
  8. Pinotsis, D.A. (2006). A nonlocal formulation of the Dbar formalism and boundary value problems in two dimensions.

Journal articles (30)

  1. Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2018). Working Memory Load Modulates Neuronal Coupling. Cerebral cortex (New York, N.Y. : 1991). doi:10.1093/cercor/bhy065.
  2. 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.
  3. (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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Brunet, N., Bastos, A., Bosman, C.A., Litvak, V., Fries, P. and 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.
  13. (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.
  14. Pinotsis, D.A. (2014). Extracting novel information from neuroimaging data using neural fields. BMC neuroscience, 15(Suppl 1). doi:10.1186/1471-2202-15-S1-O4.
  15. 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.
  16. 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.
  17. 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.
  18. (2013). Dynamic causal modelling of lateral interactions in the visual cortex. NeuroImage, 66, pp. 563–576. doi:10.1016/j.neuroimage.2012.10.078.
  19. (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.
  20. (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.
  21. (2012). Dynamic causal modeling with neural fields. NeuroImage, 59(2), pp. 1261–1274. doi:10.1016/j.neuroimage.2011.08.020.
  22. (2011). Integral representations of displacements in linear elasticity. Applied Mathematics Letters, 24(10), pp. 1670–1675. doi:10.1016/j.aml.2011.04.014.
  23. 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.
  24. (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.
  25. (2010). The elliptic sine-Gordon equation in a half plane. Nonlinearity, 23(1), pp. 77–88. doi:10.1088/0951-7715/23/1/004.
  26. (2009). Boundary value problems for the N-wave interaction equations. Physics Letters, Section A: General, Atomic and Solid State Physics, 373(22), pp. 1940–1950. doi:10.1016/j.physleta.2009.03.064.
  27. (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.
  28. Saddy, D. and Potthast, R. (2008). Language processing with dynamic fields. Cognitive Neurodynamics, 2(2), pp. 78–88. doi:10.1007/s11571-008-9042-4.
  29. (2007). The Riemann-Hilbert formalism for certain linear and nonlinear integrable PDEs. Journal of Nonlinear Mathematical Physics, 14(3), pp. 466–485. doi:10.2991/jnmp.2007.14.3.12.
  30. (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.