Kurzfassung

Towards a better Understanding of Cortical Columnar Processing: Probing the Predictability and Latency of the Brain as a "Predictive Machine" (CoCoPro)

Understanding the neuronal code is one of the biggest challenges in basic life sciences. To provide benchmark physiological data for large-scale neuronal networks, simultaneous recordings from many neurons with highest temporal resolution in a defined cortical column of an awake animal performing a defined behavioural task are required. Exactly...Towards a better Understanding of Cortical Columnar Processing: Probing the Predictability and Latency of the Brain as a "Predictive Machine" (CoCoPro)

Understanding the neuronal code is one of the biggest challenges in basic life sciences. To provide benchmark physiological data for large-scale neuronal networks, simultaneous recordings from many neurons with highest temporal resolution in a defined cortical column of an awake animal performing a defined behavioural task are required. Exactly this type of interdisciplinary experiment has been performed in a collaborative effort of the Luhmann and Stüttgen lab.

The central goal of this project is to access the quality and performance of the existent and emerging machine learning (ML) and artificial intelligence (AI) approaches with respect to their ability to describe, to explain and to predict the neuronal behaviour on the basis of these data. More common ML and AI approaches (hidden Markov models, shallow and reinforced learning, machine learning) will be compared to the very recently-developed Scalable Probabilistic Approximation approaches (Gerber et al., Sci. Adv. 2020) and to the entropy-driven approaches. Results of these comparison will aim at identifying the simplest possible (but not simpler then necessary) models that provide the most adequate lab-data descriptions. Identification of such models will enhance our understanding of emergence in the neuronal activity and provide a guidance for further experiments.» weiterlesen» einklappen