ProjectEntwicklung funktional leistungsfähiger und biologisch realistischer Modelle der neuronalen Signalverarbeitung

Basic data

Entwicklung funktional leistungsfähiger und biologisch realistischer Modelle der neuronalen Signalverarbeitung
01/01/2016 to 31/12/2017
Abstract / short description:
How cells in the brain represent, combine and process information is still an open question. One major hurdle in answering this question is the stochasticity of neural responses: even identical stimulation causes different activity patterns. The source of this noise and its effects are difficult to assess experimentally. In addition, simplified theoretical models have so far been unable to provide a consistent picture, and so the interrelation between noise and perceptual performance is still unknown. One major shortcoming of previous models is their ignorance with respect to the hierarchical structure and performance-optimized connectivity of the neocortex. New ansatzes that take these features into account will open new possibilities to gain a complete and robust understanding of neural signal encoding and its effects on perceptual performance.

In this project we aim to exploit recent breakthroughs in machine learning and self-organised neural networks to build new population models. To this end we generalise a functional, hierarchical neural network (with exceptional perceptual performance like object recognition) and compare important information theoretic analysis with the results of established models. In a subsequent step, we adapt this model to the biophysical constraints of the neocortex, and compare model predictions with experimental measurements.
rauschkorrelation (noise correlation)
barrel cortex (barrel cortex)
signal processing
neural networks
neuronale Netze

Involved staff


Brendel, Wieland
University Department of Neurology
Hospitals and clinical institutes, Faculty of Medicine

Contact persons

Faculty of Science
University of Tübingen
Institute for Theoretical Physics (ITP)
Department of Physics, Faculty of Science
CRC 1233 - Robust Vision — Inference Principles and Neural Mechanisms
Collaborative research centers and transregios
Bernstein Center for Computational Neuroscience Tübingen (BCCN)
Interfaculty Institutes

Local organizational units

Werner Reichardt Center for Integrative Neuroscience (CIN)
Centers or interfaculty scientific institutions
University of Tübingen


Stuttgart, Baden-Württemberg, Germany

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