Prof. Dr. Daniel Durstewitz
RG Computational Neuroscience
School for Computing and Mathematics,
Plymouth University, Plymouth, UK
General research interests and goals:
Our group at the Department of Theoretical Neuroscience, Central Institute of Mental Health, follows two main, closely related research objectives:
1) We construct highly data-driven neuro-computational/ mathematical models of the neuronal dynamics underlying higher brain functions, like rule learning, working memory, or cognitive flexibility, and the distortions of these processes in psychiatric conditions like schizophrenia or depression. ‘Highly data-driven’ in this context means that these models are systematically derived or inferred from in-vitro and in-vivo experimental data in a statistically principled way (e.g., by the method of maximum-likelihood; see Durstewitz et al., 2016). These models are then used to investigate the dynamical underpinnings and mechanisms of psychiatric disorders.
2) We develop novel statistical and machine learning approaches for neural data analysis, in particular the analysis of high-dimensional (multivariate) time series as generated by neuroimaging or multiple spike train recording techniques. These methods are designed to either reconstruct from the observed time series the underlying neuronal dynamics (e.g., Balaguer-Ballester et al. 2011; Lapish et al. 2015), i.e. properties like attractor states or phase transitions, or to dig for spatio-temporal patterns within the time series data across multiple time scales (e.g., Russo & Durstewitz 2017).
B5 D1 D2
Current group members:
Charmaine Demanuele / Loreen Hertäg / Sven Berberich / Tatiana Golovko / Florian Baehner / Dr. Emili Balaguer-Ballester / Dr. Eleonora Russo / Dr. Thomas Enkel / Dr. Georgia Koppe / Dr. Hazem Toutounji / Joachim Hass
Sebastian Claudio Quiroga Lombard / Grant Sutcliffe / Olga Kornienko / Carla Filosa / Dominik Schmidt
Joachim Hass / PD. Thomas Hahn
Russo E, Durstewitz D (2017) Cell assemblies at multiple time scales with arbitrary lag constellations.
Durstewitz D, Koppe G, Toutounji H (2016) Computational models as statistical tools.
Current Opinion in Behavioral Sciences 11:93–99
(2016) A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity.
PLoS Computational Biology 12(5): e1004930
Lapish CC, Balaguer-Ballester E, Seamans, JK, Phillips AG, Durstewitz D (2015) (2015) Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory.
J. Neurosci. 35: 10172–10187
(2012) An approximation to the adaptive exponential integrate-and-fire neuron model allows fast and predictive fitting to physiological data.
Frontiers in Computational Neuroscience 6: Article 62
Hyman JM, Ma L, Balaguer-Ballester E, Durstewitz D, Seamans JK equal contribution (2012) Contextual encoding by ensembles of medial prefrontal cortex neurons.
Proc. Natl. Acad. Sci. USA. 109: 5086-91
Balaguer-Ballester E, Lapish CC, Seamans JK, Durstewitz D shared first-authorship (2011) Predictive Attractor Dynamics of Cortical Populations During Memory-Guided Decision-Making.
PLoS Computational Biology 7(5): e1002057
Durstewitz D , Vittoz NM , Floresco SB, Seamans JK shared first-authorship (2010) Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning
Neuron 66: 438-48
Lapish CC, Durstewitz D, Chandler LJ, Seamans JK shared first-authorship (2008) Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex.
Proc. Natl. Acad. Sci. USA 105: 11963-8
Durstewitz D, Seamans JK (2008) The dual-state theory of prefrontal cortex dopamine function with relevance to COMT genotypes and schizophrenia.
Biological Psychiatry 64: 739-749
Board of Directors
Scientific Advisory Board
Teaching & Training
Downloads and resources
Bernstein Center Heidelberg / Mannheim