Effects of self-organization on variability in neural circuits

  • The brain is a highly dynamic and variable system: when the same stimulus is presented to the same animal on the same day multiple times, the neural responses show high trial-to-trial variability. In addition, even in the absence of sensory stimulation neural recordings spontaneously show seemingly random activity patterns. Evoked and spontaneous neural variability is not restricted to activity but is also found in structure: most synapses do not survive for longer than two weeks and even those that do show high fluctuations in their efficacy. Both forms of variability are further affected by stochastic components of neural processing such as frequent transmission failure. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here, we will investigate how the self-organizational processes of neural circuits affect the high variability in two different directions: First, we will show that recurrent dynamics of self-organizing neural networks can account for key features of neural variability. This is achieved in the absence of any intrinsic noise sources by the neural network models learning a predictive model of their environment with sampling-like dynamics. Second, we will show that the same self-organizational processes can compensate for intrinsic noise sources. For this, an analytical model and more biologically plausible models are established to explain the alignment of parallel synapses in the presence of synaptic failure. Both modeling studies predict properties of neural variability, of which two are subsequently tested on a synapse database from a dense electron microscopy reconstruction from mouse somatosensory cortex and on multi-unit recordings from the visual cortex of macaque monkeys during a passive viewing task. While both analyses yield interesting results, the predicted properties were not confirmed, guiding the next iteration of experiments and modeling studies.

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Metadaten
Author:Christoph Hartmann
URN:urn:nbn:de:hebis:30:3-423872
Place of publication:Frankfurt am Main
Referee:Jochen TrieschORCiD, Matthias KaschubeORCiDGND
Advisor:Jochen Triesch
Document Type:Doctoral Thesis
Language:English
Date of Publication (online):2016/12/09
Year of first Publication:2016
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2016/11/25
Release Date:2016/12/09
Tag:SORN; STDP; neural networks; plasticity; synaptic normalization; variability
Page Number:172
HeBIS-PPN:39649062X
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht