Currently, Artificial Intelligence is often at the center of many debates. Yet, how strongly different research disciplines are interconnected in AI is often unclear to many. At the borders between the neurosciences and the natural sciences, computational neuroscientists worldwide are exploring different questions and yet always come back to the best example: the natural brain. During the international Bernstein Conference on Computational Neuroscience from September 18-20 in Berlin, experts of deep learning, evolutionary brain research and applied AI will discuss current scientific topics.
All around the world children play hide and seek. But do animals do so too? In a recent study, scientists from the Bernstein Center Computational Neuroscience (BCCN) Berlin and the Humboldt University Berlin show that rats can quickly learn a rat-human version of the game and can easily switch between different roles – hiding and searching. The scientists suspect that hide and seek has its origins much earlier in evolution than previously thought.
Neuroscientists at TU Dresden were able to prove that speech recognition in humans begins in the sensory pathways from the ear to the cerebral cortex and not, as previously assumed, exclusively in the cerebral cortex itself.
BASF and Technische Universität Berlin (TU Berlin) have signed an agreement to cooperate closely in the area of machine learning. The aim of the Berlin-based Joint Lab for Machine Learning (BASLEARN) is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example from process or quantum chemistry. Both partners are jointly committed to this aim in the coming years. As an essential part of the cooperation, BASF will support the research work of Dr. Klaus Robert Müller, professor of machine learning and spokesperson for the Berlin Center for Machine Learning at TU Berlin, with a total of over €2.5 million over the coming five years.
The Academy of Sciences and Literature | Mainz has admitted six new members. The full members now include Markus Diesmann, neuroscientist, Elisabeth Rieken, linguist, and Judith Schalansky, author. New corresponding members are Lutz H. Gade, chemist, Wulfram Gerstner, neuroscientist, and Aleš Šteger, author.
Neuronal networks in the brain can process information particularly well when they are close to a critical point – or so brain researchers had assumed based on theoretical considerations. However, experimental investigations of brain activity revealed much fewer indicators of such critical states than expected. Scientists from Forschungszentrum Jülich and RWTH Aachen University have now proposed a possible explanation. They showed that neuronal networks can assume a second, previously unknown critical mode whose hidden dynamics are almost impossible to measure with conventional methods.
The long-lasting aftereffects of non-invasive transcranial direct current stimulation (tDCS) promise an alleviation of severe symptoms of diseases like depressive disorder or chronic pain. In a new modeling study, researchers from the Bernstein Center Freiburg suggest that the aftereffects observed in experiments may be a consequence of homeostatic network growth. Their model is based on the idea that the stimulation triggers a rearrangement of synaptic couplings among stimulated and unstimulated neurons, eventually leading to network remodeling and cell assembly formation.