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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.


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.


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".

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Original publication

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

Kraynyukova Nataliya and Tchumatchenko Tatjana
Proceedings of the National Academy of Sciences
Published in 2018

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

Tchumatchenko Tatjana and Herfurth Tim
Current Opinion in Neurobiology
Published in 2017