Tatjana Tchumatchenko Can a Single Model Explain Different Functions of the Human Brain?

Tatjana Tchumatchenko is Research Group Leader at the Max Planck Institute for Brain Research. She is also Faculty Member of the International Max Planck Research School Graduate School for Neural Circuits. The long-term goal of her research group, ‘Theory of Neural Dynamics’, is to uncover how the neural code works and what computational strategies neurons have at their disposal. She is Review Editor of a number of journals, including PLOS Computational Biology and the Journal of Computational Neuroscience. For her scientific work, she has received several awards, for instance the Dollwet Foundation Award in 2016.

Area of Research

Neural Dynamics

since 2013

Independent Research Group Leader 'Theory of Neural Dynamics'

Max Planck Society (more details)

Max Planck Institute for Brain Research

since 2012

Faculty Member

Max Planck Society (more details)

International Max Planck Research School, Neural Circuits


Postdoctoral Fellow

Columbia University, New York

Center for Theoretical Neuroscience


Postdoctoral Fellow

Max Planck Society (more details)

Max Planck Institute for Dynamics and Self-Organization


PhD in Computational Neuroscience

University of Göttingen (Georg-August-Universität Göttingen)


Diploma in Physics

Technische Universität Darmstadt

- Science Magazine

- Physical Review Letters

- PLOS Computational Biology

- Frontiers in Computational Neuroscience

- Neurocomputing

- Journal of Computational Neuroscience

- Society for Computational Neuroscience


- Dollwet Foundation Award (2016)

- Heinz-Maier-Leibnitz Prize of the german Research Foundation (2016)

- Behrens-Weise Foundation Award (2012-2015)

- ”AcademiaNet excellence” membership, upon nomination by the Volkswagen Foundation (2012)


- Interdisciplinary Center for Neuroscience, Frankfurt am Main (2013)

- Computational Sciences fellowship at the Volkswagen Foundation (2011-2013)

© Maximilian Dörrbecker

Max Planck Society

"The Max Planck Society is Germany's most successful research organization. Since its establishment in 1948, no fewer than 18 Nobel laureates have emerged from the ranks of its scientists, putting it on a par with the best and most prestigious research institutions worldwide. The more than 15,000 publications each year in internationally renowned scientific journals are proof of the outstanding research work conducted at Max Planck Institutes – and many of those articles are among the most-cited publications in the relevant field." (Source)


Max Planck Institute for Brain Research

'The Max Planck Institute for Brain Research is a fundamental research and scientific training institution focused on understanding the brain. The human brain is a formidably complex machine, composed of about one hundred billion neurons and trillions of connections, or synapses between them. Out of such a system, as if magically, arise perception, behavior and thought. The brain is often described as the "most complex machine in the known universe".

Brains are products of evolution, a response of biological organisms to selection pressure. Consequently, brains solve many complex, yet specialized problems: find food, identify and avoid danger, learn and recognize kin, learn from past associations, predict the near future, communicate, and in a few species, transmit knowledge. This all seems so simple. Yet we know that these problems are complex because our attempts at solving them with artificial machines have been disappointing so far. Today's computers are getting better at solving pure-computation problems (chess for instance). But they are still poor at solving object-, character- or face-recognition tasks, operations that our brains carry out effortlessly. And brains work with very little power (about 30W in humans). They are a triumph of efficiency.

Studying and understanding the brain is important for many reasons. First, it is a fascinating scientific challenge. Because of the diversity and complexity of the fundamental problems we face, modern neuroscience is an interdisciplinary science par excellence, involving (among others) molecular biologists, biochemists, geneticists, electrophysiologists, ethologists, psychologists, physicists, computer scientists, engineers and mathematicians. Understanding the brain requires reductionist approaches as well as synthetic ones. Simply put, it is a formidable and interesting challenge for scientists with a passion for fundamental research.

Second, understanding the brain is of paramount importance for medicine. Data from the World Health Organization show that psychiatric and neurological diseases are among the main causes of disability and disease. Indeed, in 2005, brain disorders accounted for 35% of the economic burden of all diseases on the European continent. While our institute is not a medical institution, the knowledge we produce (e.g., on mechanisms of neural development, synaptic plasticity or brain dynamics) is of fundamental relevance for applied neurological research (e.g., neurodegenerative diseases, psychiatric disorders).' (source)


The human brain has many functions; for instance, it allows people to focus on particular objects and ignore others, or to remember events in the past. TATJANA TCHUMATCHENKO uses mathematical equations in order to understand how our brain achieves this. Previous research in this area has developed models that explain single questions, such as ‘How does memory work?’. However, as she describes in this video, the brain has a set of hardware parameters that can be used to synthesize many different functions. Tchumatchenko’s research group has therefore focused on three of these: attention, memory, and contrast invariance. Their aim was to answer whether all three have the same underlying basic principles and can be explained by a single model. Their experimental results confirm this hypothesis and they found that the way to control the three functions is to control the neurons. These findings have implications for pharmaceutical research on drugs that act on the neuronal channel.

LT Video Publication DOI: https://doi.org/10.21036/LTPUB10618

Stabilized Supralinear Network Can Give Rise to Bistable, Oscillatory, and Persistent Activity

  • Nataliya Kraynyukova and Tatjana Tchumatchenko
  • Proceedings of the National Academy of Sciences
  • Published in 2018
Nataliya Kraynyukova and Tatjana Tchumatchenko. "Stabilized Supralinear Network Can Give Rise to Bistable, Oscillatory, and Persistent Activity." Proceedings of the National Academy of Sciences 115 (2018): 3464–3469.

How Linear Response Shaped Models of Neural Circuits and the Quest for Alternatives

  • Tim Herfurth and Tatjana Tchumatchenko
  • Current Opinion in Neurobiology
  • Published in 2017
Tim Herfurth and Tatjana Tchumatchenko. "How Linear Response Shaped Models of Neural Circuits and the Quest for Alternatives." Current Opinion in Neurobiology 46 (2017): 234–240. doi:10.1016/j.conb.2017.09.001.