title: Reconstructing Neural Dynamics Underlying Cognitive Flexibility Using Parameter-Evolving RNNs
creator: Thurm, Max Ingo
subject: ddc-000
subject: 000 Generalities, Science
subject: ddc-530
subject: 530 Physics
subject: ddc-570
subject: 570 Life sciences
description: Understanding the dynamic principles that enable the brain to flexibly adapt behavior in changing environments remains a central challenge in neuroscience. In  this thesis, I address this question through the lens of dynamical systems reconstruction. I use a reconstruction method, specifically targeted for non-autonomous  neural dynamics from multiple single-unit recordings in the rodent medial prefrontal  cortex (mPFC) during a probabilistic rule-learning task. To this end, I employ a  parameter-evolving piecewise-linear recurrent neural network (pePLRNN), which  explicitly incorporates time-dependent changes in the underlying dynamical system  (DS). This approach enables the reconstruction of non-autonomous DSs from nonstationary data to characterize of how the neural dynamics evolve across learning.  The approach was first validated on benchmark systems and task-trained RNNs,  where it successfully reconstructed the underlying DS. When trained on the hidden  state trajectories of RNNs solving artificial rule-learning tasks, the pePLRNN uncovered the dynamic mechanisms by which these networks implemented the learning  process.  Applied to electrophysiological recordings from the mPFC of rats, the model successfully reconstructed the non-stationary neural dynamics underlying rule learning.  The trained model-generated neural trajectories that exhibited the same decoding  properties as the original data. Change points (CP) detected in model-generated trajectories aligned with those observed in the recorded activity. Simulations of neural  trajectories under experimental conditions reproduced the behavioral distributions  of animals for both rule types.  Analyzing the trained pePLRNN as a functional surrogate model revealed that  both rules were implemented via a single stimulus-dependent attracting region that  guided neural transients toward the correct decision. During learning, this attracting region, along with the trial-specific parameters and latent neural trajectories,  exhibited abrupt changes that preceded the behavioral change point.  This work establishes a principled framework for reconstructing non-autonomous  DS directly from empirical data and demonstrates how their analysis as surrogate  models can reveal dynamic principles underlying the neural computations supporting  cognitive flexibility.
date: 2025
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserver/37427/1/Max_Ingo_Thurm_Thesis_ubpup.pdf
identifier: DOI:10.11588/heidok.00037427
identifier: urn:nbn:de:bsz:16-heidok-374271
identifier:   Thurm, Max Ingo  (2025) Reconstructing Neural Dynamics Underlying Cognitive Flexibility Using Parameter-Evolving RNNs.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/37427/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng