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Portrait Shervin Safavi

The overarching research theme of the CMC lab is understanding the computational machinery of cognitive processes. Cognition spans a wide range of functions (from perception to planning), and it is one of the most remarkable capabilities of the brain. In CMC lab, we want to understand the computations underlying cognitive processes, and the biophysical machinery that implement these computations. Imagine we go to a restaurant and want to order a dish. For such decisions brain needs to take into account several factors: how much we like each option, how much we want to explore new options, how pricey they are, … There is much computation going on in our brain to sort out each of those questions. And, in the end, all is happening in a piece of wet machinery (brain). We want to understand how our brain does those computations and how they are implemented (biophysics of computations).

We do biophysical and normative modeling (with the goal of combining them) to understand the cognitive functions; we will test these models with neural and behavioral data (in collaboration with experimental labs); and we will develop machine learning methods for multi- and cross-scale analysis of neural data to first better understand the multi-scale machinery of the brain, and second better capture the neural markers of underlying cognitive (sub-)processes and ultimately connect them to underlying computations.

At the moment, we are pursuing our key goal through the following research program:

  1. CMPD: Computational Machinery of Perceptual Decision-making
  2. MNBD: Multi-scale analysis of Neural and Behavioral Data
  3. CFDN: Criticality in Functional and Dysfunctional Neural Systems
  4. HNNC: Hybrid Neural Networks for Cognitive Neuroscience

Future Projects and Goals

In CMC we want to understand the computations that support our cognition, and also the multi-scale machinery (genes, neurons, …, large-scale networks) that implements these computations. We closely collaborate with experimental and computational labs to develop new tools, computational models, and experimental paradigms to understand the computational machinery of cognition.

Methodological and Technical Expertise

  • Computational Neuroscience
  • Neural data science
  • Computational psychiatry
  • Signal processing
  • Machine learning


Since 2023
Assistant professor (W1) of Computational Neuroscience, TU Dresden, Faculty of Medicine

Since 2023
Visiting scientist, Max Planck Institute for Biological Cybernetics, Department of Computational Neuroscience, Tübingen, Germany

Postdoctoral work jointly at the Max Planck Institute for Biological Cybernetics (Department of Computational Neuroscience) and Tübingen AI Center, Tübingen, Germany

PhD in Neuroscience, Max Planck Institute for Biological Cybernetics (Department of Physiology of Cognitive Processes), International Max Planck Research School for Cognitive and Systems Neuroscience (School of Neural Information Processing), Tübingen, Germany

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Selected Publications

Shervin Safavi, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F. Ramirez-Villegas, Nikos K. Logothetis, Michel Besserve
Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis
PLoS Computational Biology (2023)

Shervin Safavi, Peter Dayan
Multistability, Perceptual Value and Internal Foraging
Neuron 110:19, 2076–3090 (2022)

Vishal Kapoor, Abhilash Dwarakanath, Shervin Safavi, Joachim Werner, Michel Besserve, Theofanis I. Panagiotaropoulos, Nikos K. Logothetis
Decoding internally generated transitions of conscious contents in the prefrontal cortex without subjective reports
Nature Communication 13, 1535 (2022)

Shervin Safavi, Nikos K. Logothetis, Michel Besserve
From Univariate to Multivariate Coupling between Continuous Signals and Point Processes: A Mathematical Framework
Neural Computation 33 (7): 1751–1817 (2021)

Shervin Safavi, Abhilash Dwarakanath, Vishal Kapoor, Joachim Werner, Nicholas G. Hatsopoulos, Nikos K. Logothetis, Theofanis I. Panagiotaropoulos
Nonmonotonic Spatial Structure of Interneuronal Correlations in Prefrontal Microcircuits
PNAS 115 (15) E3539–E3548 (2018)