Max Planck Institute for Brain Research

Max Planck Institute for Brain Research

No other organ is as complex as the human brain: each one of its nearly 100 billion nerve cells, or neurons, can connect with thousands of other neurons. And the brain’s “product” – e.g. behavior, action, perception, language, cognition – is extraordinarily varied and still mysterious. The Max Planck Institute for Brain Research is dedicated to the study of brain function on mechanistic and computational levels. The scientific focus of the Institute is on circuits, or networks of interacting parts, including molecules in a neuron, neurons in a local circuit and local circuits in larger brain systems. Scientists at the Institute strive to gain fundamental insights on brain function by studying mainly less complex nervous systems such as those of rodents, turtles or fish. They measure how nervous systems process sensory information, how memories are formed and stored, how circuits are structured, how sleep is produced, how adaptive behaviors are generated, while trying to understand the overarching computational principles governing these processes. The studies apply molecular, imaging, electron-microscopic, genetic, behavioral and electrophysiological methods, as well as numerical simulations and theory.


Max-von-Laue-Str. 4
60438 Frankfurt am Main
Phone: +49 69 850033-0
Fax: +49 69 850033-1599

PhD opportunities

This institute has an International Max Planck Research School (IMPRS):

IMPRS for Neural Circuits

In addition, there is the possibility of individual doctoral research. Please contact the directors or research group leaders at the Institute.

Max Planck scientist receives the world’s top prize in neuroscience for her pioneering work on molecular mechanisms of brain development and plasticity

The image shows a tile with pictures of 10 Max Planck researchers who were successful in the 2022 ERC Consolidator Grant award process. They are Annalisa Pillepich, MPI for Astronomy, Philip J.W. Moll, MPI for Structure and Dynamics of Matter, Simone Kuehn, MPI for Education Research, Joshua Wilde, MPI for Demographic Research Meritxell Huch, MPI for Molecular Cell Biology and Genetics, Dora Tang, MPI for Molecular Cell Biology and Genetics, Aljaz Godec, MPI for Multidisciplinary Natural Sciences, Stéphane Hacquard, MPI for Plant Breeding Research, Hiroshi Ito, MPI for Brain Research, and Daniel Schramek, MPI for Molecular Genetics.

This result puts Max Planck in second place in a Europe-wide comparison

The Australian bearded dragon Pogona vitticeps.

A molecular atlas of an Australian dragon’s brain sheds new light on over 300 million years of brain evolution


Scientists gain insights on how deprivation-induced synaptic changes affect excitatory and inhibitory firing rates in the sensory cortex

Human neuronal networks, mapped from different parts of the cerebral cortex. Connectomic comparison to mouse revealed massively expanded interneuron-to-interneuron networks in human.

Scientists map prominent differences in the neural circuits of mice, monkeys, and human

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The Kaiser Wilhelm Institute for Brain Research was founded in Berlin 100 years ago. The first Director was Oskar Vogt, an ambitious scientist who became famous when he investigated Lenin’s brain. His wife Cécile and he provided important findings on the structure of the cerebral cortex – and also labored under a misconception or two.

Research student assistant (m/f/d) | Department Synaptic Plasticity

Max Planck Institute for Brain Research, Frankfurt am Main May 05, 2023

Veterinarian (m/f/d)

Max Planck Institute for Brain Research, Frankfurt am Main March 31, 2023

Our visual ability to separate objects from background depends greatly on detecting local discontinuities of motion, color, contrast or texture. Computing the characteristics of a texture is surprisingly difficult, as confirmed by the hundreds of thousands of trials that neural networks require to “learn” them. Yet our brains segment and differentiate textures without apparent effort. Our research aims to understand how this is done, using cephalopods’s unique ability to camouflage.


Traces of learning in the cerebral cortex

2020 Helmstaedter, Moritz


The mammalian brain, with its immense number of neurons and extreme density of communication, is the most complex network we know. Methods for partial and sparse analysis of these networks exist for more than a hundred years. However, obtaining locally complete wiring maps of neuronal networks in the mammalian brain only became possible a few years ago. Our research team has now succeeded in mapping brain tissue from the mammalian brain and analyzing it for traces of previous learning processes.


Molecular tracks of learning and memory

2019 tom Dieck, Susanne; Hafner, Anne-Sophie; Donlin-Asp, Paul; Rangaraju, Vidhya; Schuman, Erin


Although learning and memory are tasks performed by our brain on multiple interconnected levels, we can trace them down to chemical reactions leaving molecular footprints. By visualizing these tiny footprints, we aim to build a molecular model of learning. One important factor seems to be the local production of new proteins near the site of information transfer between nerve cells. We have decoded a logistics principle connecting local protein assembly to increase or decrease of information transfer and clarified questions of energy supply for this process.


The Neocortex represents the largest and most powerful area of the human brain. Having expanded and differentiated the most during mammalian evolution, it mediates many capacities that distinguish humans from their closest relatives. It also plays a central role in many psychiatric disorders. In 2018 our research group has discovered fundamentally new mechanisms that enable neocortex to rapidly and flexibly adjust information processing to the behavioral requirements of the animal.


Can the various functions of the human brain be explained by a single model?

2018 Kraynyukova, Nataliya; Tchumatchenko, Tatjana


The neural networks in the brain are able to perform calculations such as normalization, information storage and rhythm generation. To date, various mathematical models have been established to imitate these individual calculations. We have used the stabilized supralinear network (SSN) as a basic model and found that it can perform several calculations simultaneously. This indicates the possibility of formulating a unified theory of cortical function.

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