--- _id: '12716' abstract: - lang: eng text: "The process of detecting and evaluating sensory information to guide behaviour is termed perceptual decision-making (PDM), and is critical for the ability of an organism to interact with its external world. Individuals with autism, a neurodevelopmental condition primarily characterised by social and communication difficulties, frequently exhibit altered sensory processing and PDM difficulties are widely reported. Recent technological advancements have pushed forward our understanding of the genetic changes accompanying this condition, however our understanding of how these mutations affect the function of specific neuronal circuits and bring about the corresponding behavioural changes remains limited. Here, we use an innate PDM task, the looming avoidance response (LAR) paradigm, to identify a convergent behavioural abnormality across three molecularly distinct genetic mouse models of autism (Cul3, Setd5 and Ptchd1). Although mutant mice can rapidly detect threatening visual stimuli, their responses are consistently delayed, requiring longer to initiate an appropriate response than their wild-type siblings. Mutant animals show abnormal adaptation in both their stimulus- evoked escape responses and exploratory dynamics following repeated stimulus presentations. Similarly delayed behavioural responses are observed in wild-type animals when faced with more ambiguous threats, suggesting the mutant phenotype could arise from a dysfunction in the flexible control of this PDM process.\r\nOur knowledge of the core neuronal circuitry mediating the LAR facilitated a detailed dissection of the neuronal mechanisms underlying the behavioural impairment. In vivo extracellular recording revealed that visual responses were unaffected within a key brain region for the rapid processing of visual threats, the superior colliculus (SC), indicating that the behavioural delay was unlikely to originate from sensory impairments. Delayed behavioural responses were recapitulated in the Setd5 model following optogenetic stimulation of the excitatory output neurons of the SC, which are known to mediate escape initiation through the activation of cells in the underlying dorsal periaqueductal grey (dPAG). In vitro patch-clamp recordings of dPAG cells uncovered a stark hypoexcitability phenotype in two out of the three genetic models investigated (Setd5 and Ptchd1), that in Setd5, is mediated by the misregulation of voltage-gated potassium channels. Overall, our results show that the ability to use visual information to drive efficient escape responses is impaired in three diverse genetic mouse models of autism and that, in one of the models studied, this behavioural delay likely originates from differences in the intrinsic excitability of a key subcortical node, the dPAG. Furthermore, this work showcases the use of an innate behavioural paradigm to mechanistically dissect PDM processes in autism." acknowledged_ssus: - _id: PreCl - _id: Bio - _id: LifeSc - _id: M-Shop - _id: CampIT alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Laura full_name: Burnett, Laura id: 3B717F68-F248-11E8-B48F-1D18A9856A87 last_name: Burnett orcid: 0000-0002-8937-410X citation: ama: Burnett L. To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism. 2023. doi:10.15479/at:ista:12716 apa: Burnett, L. (2023). To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12716 chicago: Burnett, Laura. “To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12716. ieee: L. Burnett, “To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism,” Institute of Science and Technology Austria, 2023. ista: Burnett L. 2023. To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism. Institute of Science and Technology Austria. mla: Burnett, Laura. To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12716. short: L. Burnett, To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism, Institute of Science and Technology Austria, 2023. date_created: 2023-03-08T15:19:45Z date_published: 2023-03-10T00:00:00Z date_updated: 2023-04-05T10:59:04Z day: '10' ddc: - '599' - '573' degree_awarded: PhD department: - _id: GradSch - _id: MaJö doi: 10.15479/at:ista:12716 ec_funded: 1 file: - access_level: closed checksum: 6c6d9cc2c4cdacb74e6b1047a34d7332 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: lburnett date_created: 2023-03-08T15:08:46Z date_updated: 2023-03-08T15:08:46Z file_id: '12717' file_name: Burnett_Thesis_2023.docx file_size: 23029260 relation: source_file - access_level: open_access checksum: cebc77705288bf4382db9b3541483cd0 content_type: application/pdf creator: lburnett date_created: 2023-03-08T15:08:46Z date_updated: 2023-03-08T15:08:46Z file_id: '12718' file_name: Burnett_Thesis_2023_pdfA.pdf file_size: 11959869 relation: main_file success: 1 file_date_updated: 2023-03-08T15:08:46Z has_accepted_license: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: '178' project: - _id: 2634E9D2-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '756502' name: Circuits of Visual Attention publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria status: public supervisor: - first_name: Maximilian A full_name: Jösch, Maximilian A id: 2BD278E6-F248-11E8-B48F-1D18A9856A87 last_name: Jösch orcid: 0000-0002-3937-1330 title: To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '12809' abstract: - lang: eng text: "Understanding the mechanisms of learning and memory formation has always been one of\r\nthe main goals in neuroscience. Already Pavlov (1927) in his early days has used his classic\r\nconditioning experiments to study the neural mechanisms governing behavioral adaptation.\r\nWhat was not known back then was that the part of the brain that is largely responsible for\r\nthis type of associative learning is the cerebellum.\r\nSince then, plenty of theories on cerebellar learning have emerged. Despite their differences,\r\none thing they all have in common is that learning relies on synaptic and intrinsic plasticity.\r\nThe goal of my PhD project was to unravel the molecular mechanisms underlying synaptic\r\nplasticity in two synapses that have been shown to be implicated in motor learning, in an\r\neffort to understand how learning and memory formation are processed in the cerebellum.\r\nOne of the earliest and most well-known cerebellar theories postulates that motor learning\r\nlargely depends on long-term depression at the parallel fiber-Purkinje cell (PC-PC) synapse.\r\nHowever, the discovery of other types of plasticity in the cerebellar circuitry, like long-term\r\npotentiation (LTP) at the PC-PC synapse, potentiation of molecular layer interneurons (MLIs),\r\nand plasticity transfer from the cortex to the cerebellar/ vestibular nuclei has increased the\r\npopularity of the idea that multiple sites of plasticity might be involved in learning.\r\nStill a lot remains unknown about the molecular mechanisms responsible for these types of\r\nplasticity and whether they occur during physiological learning.\r\nIn the first part of this thesis we have analyzed the variation and nanodistribution of voltagegated calcium channels (VGCCs) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid\r\ntype glutamate receptors (AMPARs) on the parallel fiber-Purkinje cell synapse after vestibuloocular reflex phase reversal adaptation, a behavior that has been suggested to rely on PF-PC\r\nLTP. We have found that on the last day of adaptation there is no learning trace in form of\r\nVGCCs nor AMPARs variation at the PF-PC synapse, but instead a decrease in the number of\r\nPF-PC synapses. These data seem to support the view that learning is only stored in the\r\ncerebellar cortex in an initial learning phase, being transferred later to the vestibular nuclei.\r\nNext, we have studied the role of MLIs in motor learning using a relatively simple and well characterized behavioral paradigm – horizontal optokinetic reflex (HOKR) adaptation. We\r\nhave found behavior-induced MLI potentiation in form of release probability increase that\r\ncould be explained by the increase of VGCCs at the presynaptic side. Our results strengthen\r\nthe idea of distributed cerebellar plasticity contributing to learning and provide a novel\r\nmechanism for release probability increase. " acknowledged_ssus: - _id: EM-Fac - _id: Bio - _id: PreCl alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Catarina full_name: Alcarva, Catarina id: 3A96634C-F248-11E8-B48F-1D18A9856A87 last_name: Alcarva citation: ama: 'Alcarva C. Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning. 2023. doi:10.15479/at:ista:12809' apa: 'Alcarva, C. (2023). Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12809' chicago: 'Alcarva, Catarina. “Plasticity in the Cerebellum: What Molecular Mechanisms Are behind Physiological Learning.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12809.' ieee: 'C. Alcarva, “Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning,” Institute of Science and Technology Austria, 2023.' ista: 'Alcarva C. 2023. Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning. Institute of Science and Technology Austria.' mla: 'Alcarva, Catarina. Plasticity in the Cerebellum: What Molecular Mechanisms Are behind Physiological Learning. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12809.' short: 'C. Alcarva, Plasticity in the Cerebellum: What Molecular Mechanisms Are behind Physiological Learning, Institute of Science and Technology Austria, 2023.' date_created: 2023-04-06T07:54:09Z date_published: 2023-04-06T00:00:00Z date_updated: 2023-04-26T12:16:56Z day: '06' ddc: - '570' degree_awarded: PhD department: - _id: GradSch - _id: RySh doi: 10.15479/at:ista:12809 file: - access_level: closed checksum: 35b5997d2b0acb461f9d33d073da0df5 content_type: application/pdf creator: cchlebak date_created: 2023-04-07T06:16:06Z date_updated: 2023-04-07T06:16:06Z embargo: 2024-04-07 embargo_to: open_access file_id: '12814' file_name: Thesis_CatarinaAlcarva_final pdfA.pdf file_size: 9881969 relation: main_file - access_level: closed checksum: 81198f63c294890f6d58e8b29782efdc content_type: application/pdf creator: cchlebak date_created: 2023-04-07T06:17:11Z date_updated: 2023-04-07T06:17:11Z file_id: '12815' file_name: Thesis_CatarinaAlcarva_final_for printing.pdf file_size: 44201583 relation: source_file - access_level: closed checksum: 0317bf7f457bb585f99d453ffa69eb53 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: cchlebak date_created: 2023-04-07T06:18:05Z date_updated: 2023-04-07T06:18:05Z file_id: '12816' file_name: Thesis_CatarinaAlcarva_final.docx file_size: 84731244 relation: source_file file_date_updated: 2023-04-07T06:18:05Z has_accepted_license: '1' language: - iso: eng month: '04' oa_version: Published Version page: '115' project: - _id: 267DFB90-B435-11E9-9278-68D0E5697425 name: 'Plasticity in the cerebellum: Which molecular mechanisms are behind physiological learning?' publication_identifier: issn: - 2663 - 337X publication_status: published publisher: Institute of Science and Technology Austria status: public supervisor: - first_name: Ryuichi full_name: Shigemoto, Ryuichi id: 499F3ABC-F248-11E8-B48F-1D18A9856A87 last_name: Shigemoto orcid: 0000-0001-8761-9444 title: 'Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning' type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '12826' abstract: - lang: eng text: "During navigation, animals can infer the structure of the environment by computing the optic flow cues elicited by their own movements, and subsequently use this information to instruct proper locomotor actions. These computations require a panoramic assessment of the visual environment in order to disambiguate similar sensory experiences that may require distinct behavioral responses. The estimation of the global motion patterns is therefore essential for successful navigation. Yet, our understanding of the algorithms and implementations that enable coherent panoramic visual perception remains scarce. Here I pursue this problem by dissecting the functional aspects of interneuronal communication in the lobula plate tangential cell network in Drosophila melanogaster. The results presented in the thesis demonstrate that the basis for effective interpretation of the optic flow in this circuit are stereotyped synaptic connections that mediate the formation of distinct subnetworks, each extracting a particular pattern of global motion. \r\nFirstly, I show that gap junctions are essential for a correct interpretation of binocular motion cues by horizontal motion-sensitive cells. HS cells form electrical synapses with contralateral H2 neurons that are involved in detecting yaw rotation and translation. I developed an FlpStop-mediated mutant of a gap junction protein ShakB that disrupts these electrical synapses. While the loss of electrical synapses does not affect the tuning of the direction selectivity in HS neurons, it severely alters their sensitivity to horizontal motion in the contralateral side. These physiological changes result in an inappropriate integration of binocular motion cues in walking animals. While wild-type flies form a binocular perception of visual motion by non-linear integration of monocular optic flow cues, the mutant flies sum the monocular inputs linearly. These results indicate that rather than averaging signals in neighboring neurons, gap-junctions operate in conjunction with chemical synapses to mediate complex non-linear optic flow computations.\r\nSecondly, I show that stochastic manipulation of neuronal activity in the lobula plate tangential cell network is a powerful approach to study the neuronal implementation of optic flow-based navigation in flies. Tangential neurons form multiple subnetworks, each mediating course-stabilizing response to a particular global pattern of visual motion. Application of genetic mosaic techniques can provide sparse optogenetic activation of HS cells in numerous combinations. These distinct combinations of activated neurons drive an array of distinct behavioral responses, providing important insights into how visuomotor transformation is performed in the lobula plate tangential cell network. This approach can be complemented by stochastic silencing of tangential neurons, enabling direct assessment of the functional role of individual tangential neurons in the processing of specific visual motion patterns.\r\n\tTaken together, the findings presented in this thesis suggest that establishing specific activity patterns of tangential cells via stereotyped synaptic connectivity is a key to efficient optic flow-based navigation in Drosophila melanogaster." acknowledged_ssus: - _id: Bio - _id: LifeSc alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Victoria full_name: Pokusaeva, Victoria id: 3184041C-F248-11E8-B48F-1D18A9856A87 last_name: Pokusaeva orcid: 0000-0001-7660-444X citation: ama: Pokusaeva V. Neural control of optic flow-based navigation in Drosophila melanogaster. 2023. doi:10.15479/at:ista:12826 apa: Pokusaeva, V. (2023). Neural control of optic flow-based navigation in Drosophila melanogaster. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12826 chicago: Pokusaeva, Victoria. “Neural Control of Optic Flow-Based Navigation in Drosophila Melanogaster.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12826. ieee: V. Pokusaeva, “Neural control of optic flow-based navigation in Drosophila melanogaster,” Institute of Science and Technology Austria, 2023. ista: Pokusaeva V. 2023. Neural control of optic flow-based navigation in Drosophila melanogaster. Institute of Science and Technology Austria. mla: Pokusaeva, Victoria. Neural Control of Optic Flow-Based Navigation in Drosophila Melanogaster. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12826. short: V. Pokusaeva, Neural Control of Optic Flow-Based Navigation in Drosophila Melanogaster, Institute of Science and Technology Austria, 2023. date_created: 2023-04-14T14:56:04Z date_published: 2023-04-18T00:00:00Z date_updated: 2023-06-23T09:47:36Z day: '18' ddc: - '570' - '571' degree_awarded: PhD department: - _id: MaJö - _id: GradSch doi: 10.15479/at:ista:12826 ec_funded: 1 file: - access_level: closed checksum: 5f589a9af025f7eeebfd0c186209913e content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: vpokusae date_created: 2023-04-20T09:14:38Z date_updated: 2023-04-20T09:26:51Z file_id: '12857' file_name: Thesis_Pokusaeva.docx file_size: 14507243 relation: source_file - access_level: open_access checksum: bbeed76db45a996b4c91a9abe12ce0ec content_type: application/pdf creator: vpokusae date_created: 2023-04-20T09:14:44Z date_updated: 2023-04-20T09:14:44Z file_id: '12858' file_name: Thesis_Pokusaeva.pdf file_size: 10090711 relation: main_file success: 1 file_date_updated: 2023-04-20T09:26:51Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '04' oa: 1 oa_version: Published Version page: '106' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: issn: - 2663 - 337X publication_status: published publisher: Institute of Science and Technology Austria status: public supervisor: - first_name: Maximilian A full_name: Jösch, Maximilian A id: 2BD278E6-F248-11E8-B48F-1D18A9856A87 last_name: Jösch orcid: 0000-0002-3937-1330 title: Neural control of optic flow-based navigation in Drosophila melanogaster tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '12781' abstract: - lang: eng text: "Most energy in humans is produced in form of ATP by the mitochondrial respiratory chain consisting of several protein assemblies embedded into lipid membrane (complexes I-V). Complex I is the first and the largest enzyme of the respiratory chain which is essential for energy production. It couples the transfer of two electrons from NADH to ubiquinone with proton translocation across bacterial or inner mitochondrial membrane. The coupling mechanism between electron transfer and proton translocation is one of the biggest enigma in bioenergetics and structural biology. Even though the enzyme has been studied for decades, only recent technological advances in cryo-EM allowed its extensive structural investigation. \r\n\r\nComplex I from E.coli appears to be of special importance because it is a perfect model system with a rich mutant library, however the structure of the entire complex was unknown. In this thesis I have resolved structures of the minimal complex I version from E. coli in different states including reduced, inhibited, under reaction turnover and several others. Extensive structural analyses of these structures and comparison to structures from other species allowed to derive general features of conformational dynamics and propose a universal coupling mechanism. The mechanism is straightforward, robust and consistent with decades of experimental data available for complex I from different species. \r\n\r\nCyanobacterial NDH (cyanobacterial complex I) is a part of broad complex I superfamily and was studied as well in this thesis. It plays an important role in cyclic electron transfer (CET), during which electrons are cycled within PSI through ferredoxin and plastoquinone to generate proton gradient without NADPH production. Here, I solved structure of NDH and revealed additional state, which was not observed before. The novel “resting” state allowed to propose the mechanism of CET regulation. Moreover, conformational dynamics of NDH resembles one in complex I which suggest more broad universality of the proposed coupling mechanism.\r\n\r\nIn summary, results presented here helped to interpret decades of experimental data for complex I and contributed to fundamental mechanistic understanding of protein function.\r\n" acknowledged_ssus: - _id: EM-Fac alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Vladyslav full_name: Kravchuk, Vladyslav id: 4D62F2A6-F248-11E8-B48F-1D18A9856A87 last_name: Kravchuk citation: ama: Kravchuk V. Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog. 2023. doi:10.15479/at:ista:12781 apa: Kravchuk, V. (2023). Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12781 chicago: Kravchuk, Vladyslav. “Structural and Mechanistic Study of Bacterial Complex I and Its Cyanobacterial Ortholog.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:12781. ieee: V. Kravchuk, “Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog,” Institute of Science and Technology Austria, 2023. ista: Kravchuk V. 2023. Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog. Institute of Science and Technology Austria. mla: Kravchuk, Vladyslav. Structural and Mechanistic Study of Bacterial Complex I and Its Cyanobacterial Ortholog. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:12781. short: V. Kravchuk, Structural and Mechanistic Study of Bacterial Complex I and Its Cyanobacterial Ortholog, Institute of Science and Technology Austria, 2023. date_created: 2023-03-31T12:24:42Z date_published: 2023-03-23T00:00:00Z date_updated: 2023-08-04T08:54:51Z day: '23' ddc: - '570' - '572' degree_awarded: PhD department: - _id: GradSch - _id: LeSa doi: 10.15479/at:ista:12781 ec_funded: 1 file: - access_level: closed checksum: 5ebb6345cb4119f93460c81310265a6d content_type: application/pdf creator: vkravchu date_created: 2023-04-19T14:33:41Z date_updated: 2023-04-19T14:33:41Z embargo: 2024-04-20 embargo_to: local file_id: '12852' file_name: VladyslavKravchuk_PhD_Thesis_PostSub_Final_1.pdf file_size: 6071553 relation: main_file - access_level: closed checksum: c12055c48411d030d2afa51de2166221 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: vkravchu date_created: 2023-04-19T14:33:52Z date_updated: 2023-04-20T07:02:59Z embargo: 2024-04-20 embargo_to: local file_id: '12853' file_name: VladyslavKravchuk_PhD_Thesis_PostSub_Final.docx file_size: 19468766 relation: source_file file_date_updated: 2023-04-20T07:02:59Z has_accepted_license: '1' language: - iso: eng month: '03' oa_version: Published Version page: '127' project: - _id: 238A0A5A-32DE-11EA-91FC-C7463DDC885E grant_number: '25541' name: 'Structural characterization of E. coli complex I: an important mechanistic model' - _id: 627abdeb-2b32-11ec-9570-ec31a97243d3 call_identifier: H2020 grant_number: '101020697' name: Structure and mechanism of respiratory chain molecular machines publication_identifier: isbn: - 978-3-99078-029-9 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '12138' relation: part_of_dissertation status: public status: public supervisor: - first_name: Leonid A full_name: Sazanov, Leonid A id: 338D39FE-F248-11E8-B48F-1D18A9856A87 last_name: Sazanov orcid: 0000-0002-0977-7989 title: Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '13074' abstract: - lang: eng text: "Deep learning has become an integral part of a large number of important applications, and many of the recent breakthroughs have been enabled by the ability to train very large models, capable to capture complex patterns and relationships from the data. At the same time, the massive sizes of modern deep learning models have made their deployment to smaller devices more challenging; this is particularly important, as in many applications the users rely on accurate deep learning predictions, but they only have access to devices with limited memory and compute power. One solution to this problem is to prune neural networks, by setting as many of their parameters as possible to zero, to obtain accurate sparse models with lower memory footprint. Despite the great research progress in obtaining sparse models that preserve accuracy, while satisfying memory and computational constraints, there are still many challenges associated with efficiently training sparse models, as well as understanding their generalization properties.\r\n\r\nThe focus of this thesis is to investigate how the training process of sparse models can be made more efficient, and to understand the differences between sparse and dense models in terms of how well they can generalize to changes in the data distribution. We first study a method for co-training sparse and dense models, at a lower cost compared to regular training. With our method we can obtain very accurate sparse networks, and dense models that can recover the baseline accuracy. Furthermore, we are able to more easily analyze the differences, at prediction level, between the sparse-dense model pairs. Next, we investigate the generalization properties of sparse neural networks in more detail, by studying how well different sparse models trained on a larger task can adapt to smaller, more specialized tasks, in a transfer learning scenario. Our analysis across multiple pruning methods and sparsity levels reveals that sparse models provide features that can transfer similarly to or better than the dense baseline. However, the choice of the pruning method plays an important role, and can influence the results when the features are fixed (linear finetuning), or when they are allowed to adapt to the new task (full finetuning). Using sparse models with fixed masks for finetuning on new tasks has an important practical advantage, as it enables training neural networks on smaller devices. However, one drawback of current pruning methods is that the entire training cycle has to be repeated to obtain the initial sparse model, for every sparsity target; in consequence, the entire training process is costly and also multiple models need to be stored. In the last part of the thesis we propose a method that can train accurate dense models that are compressible in a single step, to multiple sparsity levels, without additional finetuning. Our method results in sparse models that can be competitive with existing pruning methods, and which can also successfully generalize to new tasks." acknowledged_ssus: - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Elena-Alexandra full_name: Peste, Elena-Alexandra id: 32D78294-F248-11E8-B48F-1D18A9856A87 last_name: Peste citation: ama: Peste E-A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074 apa: Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074 chicago: Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074. ieee: E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023. ista: Peste E-A. 2023. Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. mla: Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074. short: E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023. date_created: 2023-05-23T17:07:53Z date_published: 2023-05-23T00:00:00Z date_updated: 2023-08-04T10:33:27Z day: '23' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: DaAl - _id: ChLa doi: 10.15479/at:ista:13074 ec_funded: 1 file: - access_level: open_access checksum: 6b3354968403cb9d48cc5a83611fb571 content_type: application/pdf creator: epeste date_created: 2023-05-24T16:11:16Z date_updated: 2023-05-24T16:11:16Z file_id: '13087' file_name: PhD_Thesis_Alexandra_Peste_final.pdf file_size: 2152072 relation: main_file success: 1 - access_level: closed checksum: 8d0df94bbcf4db72c991f22503b3fd60 content_type: application/zip creator: epeste date_created: 2023-05-24T16:12:59Z date_updated: 2023-05-24T16:12:59Z file_id: '13088' file_name: PhD_Thesis_APeste.zip file_size: 1658293 relation: source_file file_date_updated: 2023-05-24T16:12:59Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '147' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '11458' relation: part_of_dissertation status: public - id: '13053' relation: part_of_dissertation status: public - id: '12299' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X title: Efficiency and generalization of sparse neural networks type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ...