In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation.

Prolonged wakefulness leads to a homeostatic response manifested in increased amplitude and number of electroencephalogram (EEG) slow waves during recovery sleep. Cortical networks show a slow oscillation when the excitatory inputs are reduced (during slow wave sleep, anesthesia), or absent (in vitro preparations). It was recently shown that a homeostatic response to electrical stimulation can be induced in cortical cultures. Here we used cortical cultures grown on microelectrode arrays and stimulated them with a cocktail of waking neuromodulators. We found that recovery from stimulation resulted in a dose-dependent homeostatic response. Specifically, the inter-burst intervals decreased, the burst duration increased, the network showed higher cross-correlation and strong phasic synchronized burst activity. Spectral power below <1.75 Hz significantly increased and the increase was related to steeper slopes of bursts. Computer simulation suggested that a small number of clustered neurons could potently drive the behavior of the network both at baseline and during recovery. Thus, this in vitro model appears valuable for dissecting network mechanisms of sleep homeostasis

Model-free reconstruction of neuronal network connectivity from calcium imaging signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically unfeasible even in dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct approximations to network structural connectivities from network activity monitored through calcium fluorescence imaging. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time-series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the effective network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (e.g., bursting or non-bursting). We thus demonstrate how conditioning with respect to the global mean activity improves the performance of our method. [...] Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good reconstruction of the network clustering coefficient, allowing to discriminate between weakly or strongly clustered topologies, whereas on the other hand an approach based on cross-correlations would invariantly detect artificially high levels of clustering. Finally, we present the applicability of our method to real recordings of in vitro cortical cultures. We demonstrate that these networks are characterized by an elevated level of clustering compared to a random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted for publicatio

Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte

Neural assemblies as core elements for modeling neural networks in the brain

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important role in information processing. Therefore, it is worthwhile to investigate modeling of neural networks. Experiments extract different kinds of datasets (ranging from pair-wise connectivity to membrane potential of individual neurons) and provide an understanding of neuronal activity. However, due to technical limitations of experiments, and complexity and variety of neural architectures, the experimental datasets do not yield a model of neural networks on their own. Roughly speaking, the experimental datasets are not enough for modeling neural networks. Therefore, in addition to these datasets, we have to utilize assumptions, hand-tuned features, parameter tuning and heuristic methods for modeling networks. In this thesis, we present different models of neural networks that are able to produce several behaviors observed in mammalian brain and cell cultures, e.g., up-state/down-state oscillations, different stimulus-evoked responses of cortical layers, activity propagation with tunable speed and several activity patterns of mice barrel cortex. An element which is embedded in all of these models is a network feature called neural assembly. A neural assembly is a group (also called population) of neurons with dense recurrent connectivity and strong internal synaptic weights. We study the dynamics of neural assemblies using analytical approaches and computer simulations. We show that network models containing assemblies exhibit dynamics similar to activity observed in the brain

Noise, coherent activity and network structure in neuronal cultures

In this thesis we apply a multidisciplinary approach, based on statistical physics and complex systems, to the study of neuronal dynamics. We focus on understanding, using theoretical and computational tools, how collective neuronal activity emerges in a controlled system, a neuronal culture. We show how the interplay between noise and network structure defines the emergent collective behavior of the system. We build, using theory and simulation, a framework that takes carefully describes spontaneous activity in neuronal cultures by taking into account the underlying network structure of neuronal cultures and use an accurate, yet simple, model for the individual neuronal dynamics. We show that the collective behavior of young cultures is dominated by the nucleation and propagations of activity fronts (bursts) throughout the system. These bursts nucleate at specific sites of the culture, called nucleation points, which result in a highly heterogeneous probability distribution of nucleation. We are able to explain the nucleation mechanism theoretically as a mechanism of noise propagation and amplification called noise focusing. We also explore the internal structure of activity avalanches by using well--defined regular networks, in which all the neurons have the same connectivity rules (motifs). Within these networks, we are able to associate to the avalanches an effective velocity and topological size and relate it to specific motifs. We also devise a continuum description of a neuronal culture at the mesoscale, i.e., we move away from the single neuron dynamics into a coarse--grained description that is able to capture most of the characteristic observables presented in previous chapters. This thesis also studies the spontaneous activity of neuronal cultures within the framework of quorum percolation. We study the effect of network structure within quorum percolation and propose a new model, called stochastic quorum percolation, that includes dynamics and the effect of internal noise. Finally, we use tools from information theory, namely transfer entropy, to show how to reliably infer the connectivity of a neuronal network from its activity, and how to distinguish between different excitatory and inhibitory connections purely from the activity, with no prior knowledge of the different neuronal types. The technique works directly on the fluorescence traces obtained in calcium imaging experiments, without the need to infer the underlying spike trains

DEVELOPMENT OF A CEREBELLAR MEAN FIELD MODEL: THE THEORETICAL FRAMEWORK, THE IMPLEMENTATION AND THE FIRST APPLICATION

Brain modeling constantly evolves to improve the accuracy of the simulated brain dynamics with the ambitious aim to build a digital twin of the brain. Specific models tuned on brain regions specific features empower the brain simulations introducing bottom-up physiology properties into data-driven simulators. Despite the cerebellum contains 80 % of the neurons and is deeply involved in a wide range of functions, from sensorimotor to cognitive ones, a specific cerebellar model is still missing. Furthermore, its quasi-crystalline multi-layer circuitry deeply differs from the cerebral cortical one, therefore is hard to imagine a unique general model suitable for the realistic simulation of both cerebellar and cerebral cortex. The present thesis tackles the challenge of developing a specific model for the cerebellum. Specifically, multi-neuron multi-layer mean field (MF) model of the cerebellar network, including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells, was implemented, and validated against experimental data and the corresponding spiking neural network microcircuit model. The cerebellar MF model was built using a system of interdependent equations, where the single neuronal populations and topological parameters were captured by neuron-specific inter- dependent Transfer Functions. The model time resolution was optimized using Local Field Potentials recorded experimentally with high-density multielectrode array from acute mouse cerebellar slices. The present MF model satisfactorily captured the average discharge of different microcircuit neuronal populations in response to various input patterns and was able to predict the changes in Purkinje Cells firing patterns occurring in specific behavioral conditions: cortical plasticity mapping, which drives learning in associative tasks, and Molecular Layer Interneurons feed-forward inhibition, which controls Purkinje Cells activity patterns. The cerebellar multi-layer MF model thus provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in health and pathological conditions. Furthermore, preliminary attempts to simulate a pathological cerebellum were done in the perspective of introducing our multi-layer cerebellar MF model in whole-brain simulators to realize patient-specific treatments, moving ahead towards personalized medicine. Two preliminary works assessed the relevant impact of the cerebellum on whole-brain dynamics and its role in modulating complex responses in causal connected cerebral regions, confirming that a specific model is required to further investigate the cerebellum-on- cerebrum influence. The framework presented in this thesis allows to develop a multi-layer MF model depicting the features of a specific brain region (e.g., cerebellum, basal ganglia), in order to define a general strategy to build up a pool of biology grounded MF models for computationally feasible simulations. Interconnected bottom-up MF models integrated in large-scale simulators would capture specific features of different brain regions, while the applications of a virtual brain would have a substantial impact on the reality ranging from the characterization of neurobiological processes, subject-specific preoperative plans, and development of neuro-prosthetic devices

Modelling emergent rhythmic activity in the cerebal cortex

A la portada consta: IDIBAPS Institut d'Investigacions Biomèdiques August Pi i SunyerThe brain, a natural adaptive system, can generate a rich dynamic repertoire of spontaneous activity even in the absence of stimulation. The spatiotemporal pattern of this spontaneous activity is determined by the brain state, which can range from highly synchronized to desynchronized states. During slow wave sleep, for example, the cortex operates in synchrony, defined by low-frequency fluctuations, known as slow oscillations (<1Hz). Conversely, during wakefulness, the cortex is characterized mainly by desynchronized activity, where low-frequency fluctuations are suppressed. Thus, an inherent property of the cerebral cortex is to transit between different states characterized by distinct spatiotemporal complexity patterns, varying in a large spectrum between synchronized and desynchronized activity. All these complex emergent patterns are the product of the interaction between tens of billions of neurons endowed with diverse ionic channels with complex biophysical properties. Nevertheless, what are the mechanisms behind these transitions? In this thesis, we sought to understand the mechanisms and properties behind slow oscillations, their modulation and their transitions towards wakefulness by employing experimental data analysis and computational models. We reveal the relevance of specific ionic channels and synaptic properties to maintaining the cortical state and also get out of it, and its spatiotemporal dynamics. Using a mean-field model, we also propose bridging neuronal spiking dynamics to a population description.El cerebro, un sistema adaptativo natural, es capaz de generar un amplio repertorio dinámico de actividad espontánea, incluso en ausencia de estímulos. La patrón espacio-temporal de esta actividad espontánea viene determinada por el estado cerebral, el cual puede variar de estados altamente sincronizados hasta estados muy desincronizados. Cuando en el sueño se entra en la fase de ondas lentas, por ejemplo, la corteza opera en sincronía, cuya actividad es definida por fluctuaciones de baja frecuencia, conocidas como oscilaciones lentas (<1Hz). En cambio, durante la vigilia, el córtex se caracteriza principalmente por tener una actividad desincronizada, donde las fluctuaciones de baja frecuencia desaparecen. Por lo tanto, una propiedad inherente de la corteza cerebral es transitar entre diferentes estados caracterizados por distintos patrones de complejidad espacio-temporal, los cuales se sitúan dentro del amplio espectro marcado por la actividad sincronizada y la desincronizada. Estos patrones emergentes son el producto de la interacción entre decenas de miles de millones de neuronas dotadas de múltiples y distintos canales iónicos con complejas propiedades biofísicas. Sin embargo, ¿cuáles son los mecanismos que regulan estas transiciones? En esta tesis tratamos de entender los mecanismos, propiedades y sus transiciones hacia la vigilia, que están detrás de las oscilaciones lentas a través del uso y análisis de datos experimentales y modelos computacionales. En ella describimos la importancia de los canales iónicos específicos y sus propiedades sinápticas tanto para mantener el estado cortical como para salir de él, estudiando así su dinámica espacio-temporal. Además, mediante el uso de un modelo de campo medio, proponemos establecer un puente que pueda describir la dinámica de disparos neuronales con una descripción general de la población neuronal.Postprint (published version

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 dinàmiques intrínseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinàmica i connectivitat de xarxes neuronals corticals de rata embrionària. Experimentalment, la gravació de l’activitat espontània d’una població de neurones en cultiu, mitjançant una càmera ràpida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, així 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 física de connexions entre neurones, i un model quadràtic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinàmiques neuronals amb un cost computacional reduït. Hem caracteritzat la dinàmica 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’anàlisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, així com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinàmica subjacent mitjançant mètodes d’incrustació i l’ús de gràfics de recurrència ens ha permès detectar diferents transicions dinàmiques amb el corresponent guany o pèrdua de la complexitat i riquesa dinàmica 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 estadística entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents paràmetres. 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