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TY - THES AU - Schulte to Brinke, Tobias TI - Analysis of information processing and memory prerequisites for temporal difference learning in cortical neural network models PB - RWTH Aachen University VL - Dissertation CY - Aachen M1 - RWTH-2025-07981 SP - 1 Online-Ressource : Illustrationen PY - 2025 N1 - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University N1 - Dissertation, RWTH Aachen University, 2025 AB - This doctoral thesis delves into the computational intricacies of the human brain, exploring the capabilities of cortical microcircuit models, the extent of their information processing capacity, and their role in memory and temporal difference learning through the use of cortico-striatal populations. Central to this exploration is the study of spiking neural networks (SNN). This research aims to gain a deeper understanding of the structural and neuronal influences on information processing in these networks and at the same time to provide a guideline for their analysis. In the first part, a network model of a cortical column introduced in a previous paper is reproduced and extended. These analyses show that the specific, data-based connectivity improves computational performance by sharpening the clarity of internal representations rather than increasing the duration of information retention as previously described. Moving beyond traditional task-based evaluations, the second part introduces a novel application of the information processing capacity (IPC) metric to SNNs. This approach provides a comprehensive profile of the functions computed by SNNs, encompassing memory and nonlinear processing. The study methodically examines various encoding mechanisms and their impact on the IPC and shows that the metric is indicative of the performance in tasks with different demands of nonlinear processing and memory. This exploration not only extends the utility of the IPC metric to more complex neural networks but also offers a deeper insight into their computational capabilities. The third part of the thesis tests a hypothesis about the computation of temporal difference errors in the brain, focusing on two distinct populations of cortical layer 5 neurons: the crossed corticostriatal (CCS) and corticopontine (CPn) cells. By implementing network models based on these populations and evaluating their memory capabilities through the lens of the IPC, the research supports, at least for continuous rate networks, the proposed role of these neurons in the computation of temporal difference errors. However, the spiking network models pose a greater challenge and exhibit little ability to memorize previous inputs in our experiments. In summary, this work not only confirms and extends existing research results, but also develops new methods for analyzing SNNs. It lays a solid foundation for future studies of the brain's computational processes and enriches the field of computational neuroscience with advanced tools and methods for exploring the intricate workings of biologically inspired neural network models. LB - PUB:(DE-HGF)11 DO - DOI:10.18154/RWTH-2025-07981 UR - https://publications.rwth-aachen.de/record/1018794 ER -