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Neuronal assembly formation and non-random recurrent connectivity induced by homeostatic structural plasticity
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science. Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany.
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Plasticity is usually classified into two distinct categories: Hebbian or homeostatic. Hebbian is driven by correlation in the activity of neurons, while homeostatic relies on a negative feedback signal to control neuronal activity. Since correlated activity leads to strengthened synaptic contacts and formation of cell assemblies, Hebbian plasticity is considered to be the basis of learning and memory. Stronger synapses, on the other hand, promote stronger correlation. This positive feedback loop can lead to instability and homeostatic plasticity is thought to play a role of stabilization. The experimentally observed time scales of homeostatic plasticity, however, are too slow to compensate for the fast Hebbian changes. Therefore, the exact way multiple types of plasticity interact in the brain remains to be elucidated. In this thesis, we will show that homeostatic plasticity can also have interesting effects on network structure. We will show that homeostatic structural plasticity has a Hebbian effect on the network level, and it comprises two separate time scales, a faster for learning and a slower for forgetting. Using a model of classical conditioning task, we will show that this rule can perform pattern completion, and that network response upon stimulation is gradual, reflecting the strength of the memory. Furthermore, we will show that networks grown with homeostatic structural plasticity and a broad distribution of target rates exhibit non-random features similar to some of those found in cortical networks. These include a broad distribution of in- and outdegrees, an over-abundance of bidirectional motifs and scaling of synaptic weights with the number of presynaptic partners. Overall, we will use simulations of spiking neural networks and mathematical tools to show that there is more to homeostatic plasticity than just controlling network stability. It remains an open question, however, the extent to which homeostatic plasticity can be accounted for structural features found in the brain.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. , p. 119
Series
TRITA-EECS-AVL ; 2020:18
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-269532ISBN: 978-91-7873-471-9 (print)OAI: oai:DiVA.org:kth-269532DiVA, id: diva2:1412927
Public defence
2020-06-08, 13:00 (English)
Opponent
Supervisors
Note

QC 20200313

Available from: 2020-03-16 Created: 2020-03-09 Last updated: 2020-05-20Bibliographically approved
List of papers
1. Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks
Open this publication in new window or tab >>Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks
2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, no 1, article id 3754Article in journal (Refereed) Published
Abstract [en]

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-269530 (URN)10.1038/s41598-018-22077-3 (DOI)000426262400007 ()29491474 (PubMedID)2-s2.0-85043273378 (Scopus ID)
Note

QC 20200313

Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-05-11Bibliographically approved
2. Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation
Open this publication in new window or tab >>Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation
2019 (English)In: Network Neuroscience, E-ISSN 2472-1751Article in journal (Refereed) Published
Abstract [en]

Transcranial direct current stimulation (tDCS) is a variant of noninvasive neuromodulation, which promises treatment for brain diseases like major depressive disorder. In experiments, long-lasting aftereffects were observed, suggesting that persistent plastic changes are induced. The mechanism underlying the emergence of lasting aftereffects, however, remains elusive. Here we propose a model, which assumes that tDCS triggers a homeostatic response of the network involving growth and decay of synapses. The cortical tissue exposed to tDCS is conceived as a recurrent network of excitatory and inhibitory neurons, with synapses subject to homeostatically regulated structural plasticity. We systematically tested various aspects of stimulation, including electrode size and montage, as well as stimulation intensity and duration. Our results suggest that transcranial stimulation perturbs the homeostatic equilibrium and leads to a pronounced growth response of the network. The stimulated population eventually eliminates excitatory synapses with the unstimulated population, and new synapses among stimulated neurons are grown to form a cell assembly. Strong focal stimulation tends to enhance the connectivity within new cell assemblies, and repetitive stimulation with well-chosen duty cycles can increase the impact of stimulation even further. One long-term goal of our work is to help in optimizing the use of tDCS in clinical applications. 

National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-269531 (URN)10.1162/netn_a_00097 (DOI)000489070300003 ()2-s2.0-85077173052 (Scopus ID)
Note

QC 20200313

Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-03-13Bibliographically approved
3. Homeostatic structural plasticity leads to the formation of memory engrams through synaptic rewiring in recurrent networks
Open this publication in new window or tab >>Homeostatic structural plasticity leads to the formation of memory engrams through synaptic rewiring in recurrent networks
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-271071 (URN)
Note

QC 20200527

Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-05-27Bibliographically approved
4. Non-random connectivity of networks subject to homeostatic structural plasticity
Open this publication in new window or tab >>Non-random connectivity of networks subject to homeostatic structural plasticity
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-271072 (URN)
Note

QC 20200527

Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-05-27Bibliographically approved

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