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Identifier 000427700
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Title Computational neuroscience modeling of adult neurogenesis in dentate gyrus and its impact in pattern separation
Alternative Title Υπολογιστική μοντελοποίηση της ενήλικης νευρογένεσης στην οδοντωτή έλικα και η επίδραση της στο διαχωρισμό μοτίβων
Author Καρατσώλη, Μαρία
Thesis advisor Ποϊράζη, Παναγιώτα
Reviewer Καραγωγέως, Δόμνα
Νικολάου, Χριστόφορος
Abstract Hippocampus is engaged in memory processes, like episodic and spatial memory. Hippocampal Dentate Gyrus (DG) is one of the two regions where adult neurogenesis occurs in mammals, and has been suggested to underlie pattern separation, i.e., the ability to formulate distinct memories of similar episodes. Principal neurons of the DG, granule cells (GCs), are considered to perform pattern separation through sparsifying and orthogonalizing their inputs. We investigate the role of newborn GCs in pattern separation using a simple computational, yet, biophysically relevant, spiking neural network. The DG network consists of 2,000 GCs (1,800 developmentally-born GCs (dbGCs >8 weeks-old), 100 mature adult-born (mab) GCs (6-8 weeks-old) and 100 immature (iab) GCs (4 weeks-old)), 100 GABAergic basket cells, 80 glutamatergic mossy cells, and 40 HIPP interneurons. Each neuronal type is simulated as a point neuron, using the adaptive exponential integrate-and-fire (AdEx) model. GCs are simulated as multicompartmental point neurons, consisting of a somatic compartment connected with 12- (dbGCs) or 3-dendrites (mabGCs and iabGCs). Five different networks were used: two control networks A,B (1900 dbGCs, 50 mabGCs, 50 iabGCs and 1800 dbGCs, 100 mabGCs, 100 iabGCs for networks A,B respectively), a network C with equal percentages of each GC subpopulation (33.3%), one network D with 50% dbGCs, 25% mabGCs and 25% iabGCs and a network Ε without adult neurogenesis (2000 dbGCs). Moreover, we simulated two additional networks; network B without abGC-BC synapses that lead to over-excitation of abGC population (network F) and network B without abGC-MC synapses (network G) that did not lead to over-excitation. Study’s results showed that GC activity was highest in the network with the highest percentage of abGCs (66% abGCs) populations (mean ± std: 3.39 ± 0.67), followed by the 50-50% network (2.97 ± 0.61), which was in turn higher than the control networks (1.38 ± 0.41 & 1.57 ± 0.38 for networks A,B respectively). Complete lack of adult neurogenesis resulted in a network with the lowest GC population activity. These simulations indicate that as the population of abGCs grows, while keeping the total GC population the same, the excitability of the DG network increases. This is because abGCs are more active than the overall GC population, irrespectively of the network’s composition. Another set of simulations examined DG network’s capacity of performing pattern separation in the above networks for EC Layer II inputs that shared a degree of similarity (60%, 70%, 80% or 90%). The results indicated that the f1 scores of output patterns were decreased as the pattern separation task became more and more difficult and that conclusion was valid for DG networks C,D,E. Hence, we deduced that the presence of abGCs seems to aim pattern separation efficiency for easy tasks (f1(input) = 0.4, 0.3) but does not contribute significantly for more complex tasks (f1(input) = 0.2, 0.1). Networks F,G exhibit pattern separation but not better than control network B.
Language English
Subject Διαχωρισμός μοτίβων
Ενήλικη νευρογένεση
Issue date 2020-03-24
Collection   Faculty/Department--School of Medicine--Department of Medicine--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/7/8/5/metadata-dlib-1589793586-381390-20500.tkl Bookmark and Share
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