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Relationship between traveling waves on the surface and neural activity inside the cat primary visual cortex
Agafonova, Anna ; Korvasová, Karolína (advisor) ; Leemburg, Swaantje Anna (referee)
Large population events such as up states, oscillations and travelling waves are ubiqui- tously observed in recordings from the surface of the brain such as electroencephalography (EEG) or electrocorticography (ECoG). However, our ability to interpret these events is limited by our lack of understanding of their cellular origin. For instance, it is not known whether the population events on the cortical surface influence the activity across the entire cortical column or whether their impact is limited to the upper cortical layers. Further, the surface population events may exhibit themselves in the form of oscillations at different frequency bands, possibly specific to certain cortical layers. The aim of this thesis is to describe neural activity in the entire cortical column during the surface events and investigate how it differs from the rest. Particularly, we investigated the level of local field potential cross-correlations and the structure of the power spectrum across the depth of the cortical column of a cat and compared the results during up states vs. down states as measured on the cortical surface. We found that the local field potential measured in the depth of the cortex is more correlated to the ECoG recording during down states compared to up states. During up states, the signals...
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Learning V1 targeting optogenetic stimulation protocol for inducing visual perception
Parada, Jakub ; Antolík, Ján (advisor) ; Korvasová, Karolína (referee)
The Optogenetic stimulation of neurons in the primary visual cortex (V1) is a novel and promising technique for vision restoration for people with acquired blindness. One of the challenges of such a technique is finding artificial stimuli which invoke desired cortical activities. This thesis explores whether neural networks and deep learning can be used for reverse engineering artificial stimuli patterns for optogenetic cortical implant prosthesis (LED) from cortical activity pattern recordings, assuming that similar cortical activity recordings are caused by similar visual stimuli. Various DNN architectures outperforming baseline solutions in stimulus reconstruction will be explored. Loss functions such as MSE and Structural similarity (SSIM) will be used. Questions such as if loss of information in the high-frequency domain of the reconstructed stimuli negatively affects correspondence between the desired cortical activity and the activity elicited by artificial stimuli patterns will be investigated. MSE evaluation metric will be used to determine the degree of similarity between the two types of cortical activities. Due to the limited availability of biological data, we use a model of V1 combined with a model of optogenetic cortical prosthesis (LED) and stimulation developed by Antolík et al. [2021] to...
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Node-attributed community detection
Vokálová, Kateřina ; Hartman, David (advisor) ; Korvasová, Karolína (referee)
Complex systems surround us in our everyday lives and their understanding can bring crucial insights into many fields. These systems consist of components (also known as communities) tied together. This thesis focuses on community detection in node- attributed networks, which are networks with some extra information about nodes. At first, we introduced the necessary terminology and provided an overview of node- attributed benchmarks used for testing. Then we studied the setting of the benchmark and algorithm parameters and discussed the obtained results. The analysis was made on synthetic networks and focused on the impact of the benchmark and algorithm parameters on the results, which we then discussed. In particular, we have found that the algorithms are less influenced by mixing parameter when the size of the network is bigger. We also confirmed our expectation, that results will be negatively influenced by higher mixing and noise parameters. 1
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Effect of inhibitory neuron subtypes on neural population dynamics
Habart, Daniel ; Korvasová, Karolína (advisor) ; Kaplický, Petr (referee)
Canonical models of neural circuits rarely differentiate between inhibitory population subtypes. Due to the recent development of optogenetic techniques, the effect of distinct inhibitory classes may now be analysed. This thesis adapts a neural population model of the mouse visual cortex and analyses the stability of equilibria with overall connectivity of the network as a bifurcation parameter. A stability result for small values of the parameter is obtained. 1
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Extraction of spontaneously occurring activity patterns from an electrophysiological signal
Voldřich, Matěj ; Korvasová, Karolína (advisor) ; Riedlová, Kamila (referee)
To be able to elicit desired percepts using a cortical visual prosthesis, is it essential to understand the functional structure of the neural network under the implant. However, in a blind individual the functional proper- ties of neurons cannot be measured by recording responses to visual stimuli and information can only be extracted from spontaneous activity. Spon- taneous activity in the primary visual cortex (V1) of anesthetized primates has been shown to encode information about local functional architecture of the neural network. Particularly, inference of the orientation-preference map from spontaneous activity has been achieved in non-human animals using in- vasive imaging techniques that are not intended for applications in humans. Whether the same inference is possible with spatially sparse electrophysiolo- gical recordings from a micro-electrode array that is currently used as a cortical visual prosthesis remains unknown. The aim of this thesis is to use automatic spatial pattern detection algorithm for orientation preference map inference from spontaneous activity and compare outcomes using four differ- ent variables extracted from signal recorded with intracranial micro-electrode arrays in awake macaque monkeys. 1
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Comparing two main community detection algorithms and their applications on human brains
Čížková, Kateřina ; Pokorná, Aneta (advisor) ; Korvasová, Karolína (referee)
Complex networks help us to understand complicated phenomena, including human brain. One of its key characteristics is its modular organization, also know as community structure. This thesis compares two main community detection algorithms, the Louvain algorithm and the label propagation algorithm. We show some differences in the rsults of the algorithms. On the other hand, we also discuss their common properties. The practical part of this thesis is devoted to community detection in human brain functional networks. It is known that the community structure of a human brain functional network changes during aging or due to some diseases. We compared the modularity and number of communities in functional networks of patients with the diagnosis of multiple sclerosis before and after a neurorehabilitation therapy. We did not find any significant change considering the whole dataset. However, the modularity changed in the functional net- works of the six patients with a primary progressive course of multiple sclerosis. We show that there might be other minor changes in correlation with fMRI protocol or patients' gender. 1
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Holling-Tanner model
Skybová, Kateřina ; Bárta, Tomáš (advisor) ; Korvasová, Karolína (referee)
The thesis deals with Holling-Tanner model which is one of predator-prey models. In the first chapter the Holling-Tanner model is formulated, its biological interpretation is explained and some ways are introduced how to modify the model. The second chapter introduces two useful methods which are used in qualitative analysis of Holling-Tanner model. The third chapter focuses on qualitative analysis directly. The qualitative analysis is processed exemplary on one of the modifications of system. The fourth chapter focuses on the Holling-Tanner model of the second type exclusively and tries to compare its particular modifications. 1
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