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@PHDTHESIS{Jordan:763385,
author = {Jordan, Jakob},
othercontributors = {Diesmann, Markus and Leibe, Bastian},
title = {{P}robabilistic neural computation and neural simulation
technology},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2019-06298},
pages = {1 Online-Ressource (xiii, 253 Seiten) : Illustrationen,
Diagramme},
year = {2018},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2019; Dissertation, RWTH Aachen University, 2018},
abstract = {Deciphering the working principles of brain function is of
major importance from at least two perspectives. From the
clinical viewpoint, a deeper understanding of our brains
will lead to better treatments for psychological and
neurodegenerative diseases. The technological perspective
promises smart machines that rival our ability to perceive,
learn and act in the real world. It is generally believed
that the relevant physical processes can be understood in
terms of large, plastic networks of nerve cells. Over the
last decade, probability theory has gained popularity as a
normative model of brain function, since it offers a
unifying view for many behavioural phenomena. To connect
this high-level description to low-level implementations in
neural substrates, computational models provide effective
means to investigate specific hypotheses. Many neural
network models for probabilistic inference employ
approximation algorithms, relying on stochasticity to
achieve a sample-based representation of probability
distributions. In the first part of this thesis, we
investigate biophysically plausible sources of stochasticity
for models of approximate inference in neural substrates. We
consider network models derived from a popular model class
for sample-based inference, Boltzmann machines, but equipped
with a natural source of stochasticity: synaptic input from
other neurons. We demonstrate that stochasticity generated
by recurrent neural networks can outperform other approaches
based on independent random number streams if resources are
limited. We further show that the essential effect required
for this approach is also present in networks emulated in a
mixed-signal neuromorphic system with strong temporal and
spatial heterogeneities. Subsequently we consider network
models with alternative sources of stochasticity, including
variability in the coupling strength between neurons. Our
results show that while these networks are able to represent
well-defined probability distributions, probabilistic
inference cannot be implemented using straightforward
methods familiar from Boltzmann machines. The human brain
contains approximately one hundred billion individual nerve
cells connected by about one hundred trillion synapses: This
complexity poses a significant computational challenge for
the numerical simulation of such systems. To support
simulations of large-scale network models at cellular
resolution, researchers can rely on well-tested
high-performance simulation software that makes efficient
use of the largest supercomputers available today with tens
of thousands of compute nodes. However, it has an inherent
scalability bottleneck arising from the memory consumption
of the connection infrastructure and the undirected
communication algorithms typically employed. In the second
part of this thesis, we introduce a new two-tier connection
infrastructure employing directed communication across
compute nodes. By implementing this new technology in NEST,
a widely-used simulator for large-scale neural network
models, we demonstrate that it solves previous scalability
bottlenecks in terms of memory consumption. In addition, we
show that on current supercomputers, the directed
communication between processes significantly improves the
simulation performance, and at the same time maintains high
efficiency in small-scale simulations executed on laptops
and small clusters. Simulation software is however only one
part required for neuroscientific modeling: for functional
networks, providing a realistic environment that interacts
with the models is equally important. Researchers should not
have to rely on handcrafted solutions as this makes models
difficult to evaluate and compare objectively. To tackle
these issues, we develop a toolchain that enables
closed-loop interactions between neural-network models in
established simulators and reinforcement-learning
environments from machine-learning toolkits. In conclusion,
this thesis brings models for approximate inference in
neural circuits further into the biophysically plausible
domain, with direct applications for neuromorphic
implementations, and presents new simulation technology
preparing software tools for the simulation of human cortex
at the resolution of individual neurons and synapses on
future HPC systems.},
cin = {535000-7 / 120000},
ddc = {004},
cid = {$I:(DE-82)535000-7_20140620$ / $I:(DE-82)120000_20140620$},
pnm = {SMHB - Supercomputing and Modelling for the Human Brain
(HGF-SMHB-2013-2017) / BRAINSCALES - Brain-inspired
multiscale computation in neuromorphic hybrid systems
(269921) / HBP - The Human Brain Project (604102) / HBP SGA1
- Human Brain Project Specific Grant Agreement 1 (720270) /
RL-BRD-J - Neural network mechanisms of reinforcement
learning (BMBF-01GQ1343) / EUROSPIN - European Consortium on
Synaptic Protein Networks in Neurological and Psychiatric
Diseases (241498)},
pid = {G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)269921 /
G:(EU-Grant)604102 / G:(EU-Grant)720270 /
G:(DE-Juel1)BMBF-01GQ1343 / G:(EU-Grant)241498},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2019-06298},
url = {https://publications.rwth-aachen.de/record/763385},
}
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