Model architecture for associative memory in a neural network of spiking neurons

AbstractA synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb’s hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons’ responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace

Evolution signatures in genome network properties

Genomes maybe organized as networks where protein-protein association plays the role of network links. The resulting networks are far from being random and their topological properties are a consequence of the underlying mechanisms for genome evolution. Considering data on protein-protein association networks from STRING database, we present experimental evidence that degree distribution is not scale free, presenting an increased probability for high degree nodes. We also show that the degree distribution approaches a scale invariant state as the number of genes in the network increases, although real genomes still present finite size effects. Based on the experimental evidence unveiled by these data analyses, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated, with a duplication probability that linearly grows with gene degree and decreases with its clustering coefficient. The results show that topological distributions are better described than in previous genome evolution models. This model correctly predicts that, in order to produce protein-protein association networks with number of links and number of nodes in the observed range, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. If this scenario is true, it implies a universal mechanism for genome evolution

Rod Bala : arranjos de forró eletrônico

O objetivo do presente trabalho é analisar as músicas arranjadas e produzidas por Rod Bala, que ficou conhecido nacionalmente por seu trabalho com o cantor Wesley Safadão, tanto como baterista da banda de apoio quanto como arranjador. O grupo se apresenta por todo o país em grandes festivais, ganhando notabilidade através da performance diferenciada da banda em questão de arranjos e texturas. O trabalho é dividido em três partes: primeiro, uma apresentação de conceitos sobre o estudo de música da América Latina e sobre a música popular de massas, buscando valorizar um nicho musical que aparece pouco em trabalhos de Musicologia. Segundo, uma contextualização histórica do forró eletrônico e seu desenvolvimento nos últimos anos, assim como a história do artista que será o foco do trabalho. A terceira parte consiste em uma análise comparativa dos arranjos, com o auxílio de algumas transcrições próprias, buscando analisar os arranjos de músicas que originalmente eram de outros estilos da música brasileira e foram modificadas para entrar no repertório dos shows do cantor

Heterogeneous individual motility biases group composition in a model of aggregating cells

Aggregative life cycles are characterized by alternating phases of unicellular growth and multicellular development. Their multiple, independent evolutionary emergence suggests that they may have coopted pervasive properties of single-celled ancestors. Primitive multicellular aggregates, where coordination mechanisms were less efficient than in extant aggregative microbes, must have faced high levels of conflict between different co-aggregating populations. Such conflicts within a multicellular body manifest in the differential reproductive output of cells of different types. Here, we study how heterogeneity in cell motility affects the aggregation process and creates a mismatch between the composition of the population and that of self-organized groups of active adhesive particles. We model cells as self-propelled particles and describe aggregation in a plane starting from a dispersed configuration. Inspired by the life cycle of aggregative model organisms such as Dictyostelium discoideum or Myxococcus xanthus, whose cells interact for a fixed duration before the onset of chimeric multicellular development, we study finite-time configurations for identical particles and in binary mixes. We show that co-aggregation results in three different types of frequency-dependent biases, one of which is associated to evolutionarily stable coexistence of particles with different motility. We propose a heuristic explanation of such observations, based on the competition between delayed aggregation of slower particles and detachment of faster particles. Unexpectedly, despite the complexity and non-linearity of the system, biases can be largely predicted from the behavior of the two corresponding homogenous populations. This model points to differential motility as a possibly important factor in driving the evolutionary emergence of facultatively multicellular life-cycles