Project Details
Projekt
Network Dynamics and Computational Mechanisms of Rule Learning II
Applicants
Dr. Florian Bähner; Professor Dr. Daniel Durstewitz; Dr. Thomas Theodor Gerd Hahn; Professor Dr. Wolfgang Kelsch
Subject Area
Cognitive, Systems and Behavioural Neurobiology
Term
from 2013 to 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 237823417
Animal learning is often conceived as a gradual process that develops over many trials and involves the incremental strengthening of associations among stimuli, responses, and outcomes, a view deeply rooted in behaviorist theory and inherent in most neural network learning models. However, in recent years there is mounting evidence that animal learning, even in apparently simple conditioning tasks, is better understood as an active inference and decision making process. This view comes from careful statistical examination of the individual, trial-by-trial learning progress, and from the observation of sudden transitions among neural ensemble states coding for different behavioral rules in prefrontal cortex (PFC) which accompany the learning process. In order to address conflicting hypotheses regarding the computational meaning of these neural ensemble transitions, and of the role of the dopaminergic system within them, we had started multiple single-unit recordings from the rat PFC during a newly designed probabilistic rule-shift task, and the effects of amphetamine or local optogenetic stimulation of dopamine release during this task. Preliminary results suggest that sudden neural transitions might reflect a change in choice criterion rather than other behavioral processes (like uncertainty), and that dopamine may also primarily affect the choice process rather than value updating. Building on these results and other observations, here we aim to further validate and extend our current understanding along three major directions, using a combination of multi-tetrode recordings, optogenetic manipulations, and advanced time series and computational model based analysis of the learning process:1) We will dissect in more detail the task periods during which dopamine input is most crucial, how dopamine neuron activity is coordinated with PFC activity as the task progresses, and how it impacts on various subcomponents of rule learning like action selection and value updating;2) we will address in more detail specific hypotheses regarding the neuro-dynamical mechanisms underlying the active inference process in various subdivisions of the rat PFC;3) through variations of the basic behavioral task design we will explore higher-level concepts supported by current observations like learning as structural inference and active information seeking.Thus, we will continue toward our goal of a comprehensive understanding of rule learning as active inference at the neurophysiological, neuro-dynamical, and neuro-computational levels.
DFG Programme
Priority Programmes
