440 research outputs found
A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings
Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Recommended from our members
29th Annual Computational Neuroscience Meeting: CNS*2020
Meeting abstracts
This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests.
Virtual | 18-22 July 202
Etude expérimentale des dynamiques temporelles du comportement normal et pathologique chez le rat et la souris
155 p.Modern neuroscience highlights the need for designing sophisticated behavioral readout of internal cognitive states. From a thorough analysis of classical behavioral test, my results supports the hypothesis that sensory ypersensitivity might be the cause of other behavioural deficits, and confirm the potassium channel BKCa as a potentially relevant molecular target for the development of drug medication against Fragile X Syndrome/Autism Spectrum Disorders. I have also used an innovative device, based on pressure sensors that can non-invasively detect the slightest animal movement with unprecedented sensitivity and time resolution, during spontaneous behaviour. Analysing this signal with sophisticated computational tools, I could demonstrate the outstanding potential of this methodology for behavioural phenotyping in general, and more specifically for the investigation of pain, fear or locomotion in normal mice and models of neurodevelopmental and neurodegenerative disorders
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
Linear and nonlinear approaches to unravel dynamics and connectivity in neuronal cultures
[eng] In the present thesis, we propose to explore neuronal circuits at the mesoscale, an approach in which one monitors small populations of few thousand neurons and concentrates in the emergence of collective behavior. In our case, we carried out such an exploration both experimentally and numerically, and by adopting an analysis perspective centered on time series analysis and dynamical systems. Experimentally, we used neuronal cultures and prepared more than 200 of them, which were monitored using fluorescence calcium imaging. By adjusting the experimental conditions, we could set two basic arrangements of neurons, namely homogeneous and aggregated. In the experiments, we carried out two major explorations, namely development and disintegration. In the former we investigated changes in network behavior as it matured; in the latter we applied a drug that reduced neuronal interconnectivity. All the subsequent analyses and modeling along the thesis are based on these experimental data. Numerically, the thesis comprised two aspects. The first one was oriented towards a simulation of neuronal connectivity and dynamics. The second one was oriented towards the development of linear and nonlinear analysis tools to unravel dynamic and connectivity aspects of the measured experimental networks. For the first aspect, we developed a sophisticated software package to simulate single neuronal dynamics using a quadratic integrate–and–fire model with adaptation and depression. This model was plug into a synthetic graph in which the nodes of the network are neurons, and the edges connections. The graph was created using spatial embedding and realistic biology. We carried out hundreds of simulations in which we tuned the density of neurons, their spatial arrangement and the characteristics of the fluorescence signal. As a key result, we observed that homogeneous networks required a substantial number of neurons to fire and exhibit collective dynamics, and that the presence of aggregation significantly reduced the number of required neurons. For the second aspect, data analysis, we analyzed experiments and simulations to tackle three major aspects: network dynamics reconstruction using linear descriptions, dynamics reconstruction using nonlinear descriptors, and the assessment of neuronal connectivity from solely activity data. For the linear study, we analyzed all experiments using the power spectrum density (PSD), and observed that it was sufficiently good to describe the development of the network or its disintegration. PSD also allowed us to distinguish between healthy and unhealthy networks, and revealed dynamical heterogeneities across the network. For the nonlinear study, we used techniques in the context of recurrence plots. We first characterized the embedding dimension m and the time delay δ for each experiment, built the respective recurrence plots, and extracted key information of the dynamics of the system through different descriptors. Experimental results were contrasted with numerical simulations. After analyzing about 400 time series, we concluded that the degree of dynamical complexity in neuronal cultures changes both during development and disintegration. We also observed that the healthier the culture, the higher its dynamic complexity. Finally, for the reconstruction study, we first used numerical simulations to determine the best measure of ‘statistical interdependence’ among any two neurons, and took Generalized Transfer Entropy. We then analyzed the experimental data. We concluded that young cultures have a weak connectivity that increases along maturation. Aggregation increases average connectivity, and more interesting, also the assortativity, i.e. the tendency of highly connected nodes to connect with other highly connected node. In turn, this assortativity may delineates important aspects of the dynamics of the network. Overall, the results show that spatial arrangement and neuronal dynamics are able to shape a very rich repertoire of dynamical states of varying complexity.[cat] L’habilitat dels teixits neuronals de processar i transmetre informació de forma eficient depèn de les propietats dinà miques intrÃnseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinà mica i connectivitat de xarxes neuronals corticals de rata embrionà ria. Experimentalment, la gravació de l’activitat espontà nia d’una població de neurones en cultiu, mitjançant una cà mera rà pida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, aixà com la modificació de la seva connectivitat. En conjunt, aquestes eines permeten estudiar el comportament col.lectiu emergent de la població neuronal. Amb l’objectiu de simular els patrons observats en el laboratori, hem implementat un model mètric aleatori de creixement neuronal per simular la xarxa fÃsica de connexions entre neurones, i un model quadrà tic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinà miques neuronals amb un cost computacional reduït. Hem caracteritzat la dinà mica global i individual de les neurones i l’hem correlacionat amb la seva estructura subjacent mitjançant tècniques lineals i no–lineals de series temporals. L’anà lisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, aixà com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinà mica subjacent mitjançant mètodes d’incrustació i l’ús de grà fics de recurrència ens ha permès detectar diferents transicions dinà miques amb el corresponent guany o pèrdua de la complexitat i riquesa dinà mica del cultiu durant els diferents estudis experimentals. Finalment, a fi de reconstruir la connectivitat interna hem testejat, mitjançant simulacions, diferents quantificadors per mesurar la dependència estadÃstica entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents parà metres. Malgrat presentar certs tres de xarxes tipus ‘petit món’, els nostres cultius mostren una distribució de grau ‘exponencial’ o ‘esbiaixada’ per, respectivament, cultius joves i madurs. Addicionalment, hem observat que les xarxes homogènies presenten la propietat de disassortativitat, mentre que xarxes amb un creixent nivell d’agregació espaial presenten assortativitat. Aquesta propietat impacta fortament en la transmissió, resistència i sincronització de la xarxa
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