1,720,956 research outputs found
Modellizzazione computazionale dell'attività cerebrale: dai singoli neuroni ai circuiti su larga scala
No full textIl presente lavoro di tesi intende descrivere il processo scientifico e metodologico che ha portato allo sviluppo di modelli matematici di neurone e di circuiti cerebrali sfruttando l’approccio metodologico delle neuroscienze computazionali. L’attività di ricerca, in particolare, ha riguardato la messa a punto di varie strategie di modellizzazione abbracciando molteplici scale spazio-temporali su diversi livelli di complessità. I risultati di questa attività scientifica sottolineano l'importanza degli strumenti computazionali da un lato nel processo di comprensione del funzionamento dei circuiti neurali e dall’altro per lo sviluppo di applicazioni ingegneristiche innovative. In questo lavoro intendo infatti mostrare come, utilizzando dati sperimentali, le neuroscienze computazionali favoriscano il progresso scientifico e tecnologico.
Il processo di modellizzazione è stato affrontato partendo dal livello di scala del singolo neurone in cui un modello biologicamente realistico basato sulla modellizzazione di Hodgkin-Huxley è stato utilizzato per studiare dettagliatamente le dinamiche dell’eccitabilità e della plasticità neuronale. Sono stati poi modellizzati microcircuiti spiking su larga scala basati su singoli neuroni del tipo “integrate and fire” in quanto possono essere utilizzati per la generazione di gemelli digitali (digital twins) di regioni cerebrali estese. Se opportunamente calibrati su dati sperimentali, questi strumenti computazionali consentono di esplorare condizioni fisiologiche e patologiche non testabili sperimentalmente, offrendo uno strumento digitale innovativo contro le patologie a carico del sistema nervoso. Infine, in un processo di astrazione basato sulle neuroscienze teoriche, singoli neuroni inferenziali sono stati utilizzati per riprodurre la funzionalità di reti estese con un numero limitato di elementi computazionali al fine di sviluppare microprocessori neuromorfi ad elevata efficienza energetica.
Sottolineando la profonda interconnessione tra modelli teorici, computazionali ed indagini sperimentali, questa ricerca evidenzia l’importanza di una efficace collaborazione tra la raccolta di dati sperimentali ed il processo di modellizzazione che da sempre è alla base della ricerca scientifica ed ha sempre accompagnato e assistito la neurofisiologia. Inoltre, suggerisce l'importanza dello sviluppo di tecnologie innovative che traggano ispirazione dall'efficienza e dalla robustezza dei meccanismi computazionali del cervello.This work of thesis presents the scientific and methodological process that underpin the development of mathematical models of neurons and brain circuits. Employing the methodological approach of computational neuroscience, the research activity, in particular, was focused on the refinement of various modeling strategies spanning multiple spatio-temporal scales and managing different levels of complexity. The results emphasize the importance of computational tools not only in expanding the knowledge about the functioning of neural circuits but also for the development of novel engineering applications. This work demonstrates how computational neuroscience, driven by experimental data, promotes both scientific and technological advancements.
The modeling of brain activity was firstly addressed at the scale of single neurons, where a biologically realistic model based on Hodgkin-Huxley modeling strategy was used to study the dynamics of excitability and neural plasticity in detail. Subsequently, large-scale spiking microcircuits were modeled exploiting "integrate and fire" neuron models. These models aimed at creating digital twins of extended brain regions. When appropriately calibrated with experimental data, these computational tools allow exploration of physiological and pathological conditions that are not experimentally testable, providing an innovative digital tool against nervous system diseases. Finally, in an abstraction process based on theoretical neuroscience, we employed individual inferential neurons to replicate the functionality of extended networks with a limited number of computational elements with the aim of developing energy-efficient neuromorphic microprocessors.
This research accentuates the profound interconnection between theoretical models, computational methodologies, and empirical investigations. It emphasizes the importance of an effective interchange between experimental data collection and the modelling process, a synergy that has always been fundamental to scientific research and neurophysiology. Moreover, it suggests the importance of applying this knowledge to the development of innovative technologies inspired by the efficiency and robustness of the brain's computational mechanisms
Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders
Get PDFThe investigation of synaptic functions remains one of the most fascinating challenges in
the field of neuroscience and a large number of experimental methods have been tuned to dissect the
mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the
insights of neurological disorders originating from alterations in neurotransmission often requires the
development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological
approaches. In the last decades, additional tools to explore neurological diseases have been provided
to the scientific community. A wide range of computational models in fact have been developed to
explore the alterations of the mechanisms involved in neurotransmission following the emergence
of neurological pathologies. Here, we review some of the advancements in the development of
computational methods employed to investigate neuronal circuits with a particular focus on the
application to the most diffuse neurological disorders
Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer
Get PDFA central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
