Self-referential forces are sufficient to explain different dendritic morphologies

© 2013 Memelli, Torben-Nielsen and Kozloski. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etcDendritic morphology constrains brain activity, as it determines first which neuronal circuits are possible and second which dendritic computations can be performed over a neuron's inputs. It is known that a range of chemical cues can influence the final shape of dendrites during development. Here, we investigate the extent to which self-referential influences, cues generated by the neuron itself, might influence morphology. To this end, we developed a phenomenological model and algorithm to generate virtual morphologies, which are then compared to experimentally reconstructed morphologies. In the model, branching probability follows a Galton-Watson process, while the geometry is determined by "homotypic forces" exerting influence on the direction of random growth in a constrained space. We model three such homotypic forces, namely an inertial force based on membrane stiffness, a soma-oriented tropism, and a force of self-avoidance, as directional biases in the growth algorithm. With computer simulations we explored how each bias shapes neuronal morphologies. We show that based on these principles, we can generate realistic morphologies of several distinct neuronal types. We discuss the extent to which homotypic forces might influence real dendritic morphologies, and speculate about the influence of other environmental cues on neuronal shape and circuitry.Peer reviewe

Modeling brain structures from hundreds of hypoglossal motoneurons to millions of cerebellar cells

Computational neuroscience is a rapidly growing field in the quest to discover how the human brain works. Mathematical modeling and computer simulations increasingly help neuroscientists test hypotheses and explore neuronal mechanisms from the level of single cells to billions of neurons. In this PhD thesis, we have implemented and analyzed computational models of two mammalian brain structures: the hypoglossal nucleus and the cerebellum. As a first project, we have developed a detailed computational model for a network of Hypoglossal Motoneurons (HMs). HMs are located in the brainstem and exhibit synchronous firing activity. They are coupled by gap junctions, direct electrical links between neighboring neurons. We have simulated HM networks with hundreds of neurons for a quantitative analysis of changes in their synchronized behavior under different conditions. Some of the conditions and mechanisms analyzed include: simulated gap junction uncoupling, changes in premotor excitatory input current strength, modulation of HM firing frequency, and the emergence of different firing groups. A major ongoing project in our lab is the building of a unified efficient system for creation, simulation, and visualization of large-scale models of brain structures. These models are morphologically representative neuronal networks which include neurons and synapses of different types. We have used this system to create models of the cerebellum, the little brain that coordinates complex motor activities. The cerebellum large-scale models consist of millions of neurons and billions of synapses. We have run numerous simulations on PCs and on Blue Gene supercomputers to analyze firing activity in the cerebellar circuits. The approaches for the two projects are somewhat different. The first project focuses on the biophysical details of the model and the resulting biological interpretations of specific cellular mechanisms. The second project emphasizes performance and simulations of very large networks of different cell types. Both projects provide useful insight into various mechanisms in the respective simulated networks. | 113 page

Memory and metaphysics: a joint reading of Time and Being and What is metaphysics

Abstract The article is a reading, in conjunction with one-another, of Time and Being and What is metaphysics. Its scope is that of raising questions on certain Heideggerian topics that are here formulated as thesis. Namely, first that the turn in Heidegger’s thinking is not a change in his process of thinking, but rather an essential trait of what Heidegger calls the matter at hand (Sachverhalt). Secondly, that this turn of the matter at hand is in itself memory in a twofold way: as metaphysics in its relation to being and time, and as questioning of this metaphysical relation. And finally, that this turn is a technical one, in the sense of technique as this latter is, in Heidegger’s thinking, the original determination of Being as Anwesenheit

PRODUÇÃO DE ESTRUVITA (MgNH4PO4.6H2O) A PARTIR DA URINA HUMANA ATRAVÉS DE PRECIPITAÇÃO INDUZIDA POR ÍONS DE MAGNÉSIO

O processo físico-químico da precipitação de estruvita é uma técnica que apresenta elevado potencial na remoção de nutrientes, a partir de diferentes tipologias de efluentes. O presente estudo teve por objetivo a avaliação da produção de estruvita (MgNH4PO4.6H2O) a partir da urina humana através de precipitação induzida por íons de magnésio para uso como fertilizante alternativo na agricultura, comparando dois compostos comerciais com elevado teor de magnésio (MgO e MgCl2) e um composto alternativo de baixo custo, salmoura marinha, como fonte reagente alternativa de magnésio. O experimento foi realizado em escala laboratorial utilizando Jar Test. Foram realizados ensaios variando a concentração de magnésio, velocidade de rotação e tempo de agitação, buscando as melhores condições de precipitação. A urina humana armazenada apresentou concentrações de fósforo e nitrogênio de 0,7 e 5,6 g/L, respectivamente. A precipitação contou com monitoramento do pH (9,2-9,7) e temperatura (21 25,7°C), atingiu considerável remoção de fósforo de 98,9%. Constatou-se qualidade dos cristais gerados, com a identificação do mineral de interesse estruvita utilizando-se a difração de raio-X. Os ensaios com melhores resultados foram de concentração de 0,45 gMg/L, 100 rpm, 10 minutos de agitação e adição de MgO. Os sólidos recuperados apresentaram pureza de 77% (MgO), 60% (Salmoura) e 52% (MgCl2) de estruvita. A salmoura apresentou o custo mais baixo, de R$ 13,96 por quilograma de estruvita

Emergent Central Pattern Generator Behavior in Gap-Junction-Coupled Hodgkin-Huxley Style Neuron Model

Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents (I AHP ) to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus

Context-aware modeling of neuronal morphologies

© 2014 Torben-Nielsen and De Schutter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsNEURONAL MORPHOLOGIES ARE PIVOTAL FOR BRAIN FUNCTIONING: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.Peer reviewe

ARNALDO ANTUNES: OS NOMES DO HOMEM

Discussão de questões relativas à produção poética e à tecnologia. A relação entre música e imagem e o tratamento da palavra em NOME - objeto de linguagem formado de livro, vídeo e CD