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@PHDTHESIS{SchultetoBrinke:1018794,
author = {Schulte to Brinke, Tobias},
othercontributors = {Morrison, Abigail Joanna Rhodes and Kampa, Björn M.},
title = {{A}nalysis of information processing and memory
prerequisites for temporal difference learning in cortical
neural network models},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-07981},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2025},
abstract = {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.},
cin = {124920 / 120000},
ddc = {004},
cid = {$I:(DE-82)124920_20200227$ / $I:(DE-82)120000_20140620$},
pnm = {ACA - Advanced Computing Architectures (SO-092) / Impuls-
und Vernetzungsfonds},
pid = {G:(DE-HGF)SO-092 / G:(DE-HGF)IVF-20140101},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-07981},
url = {https://publications.rwth-aachen.de/record/1018794},
}
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