Opening for Web-based and mobile application design Scholarship

The Research, Innovation and Dissemination Center on Neuromathematics (NeuroMat) is offering a FAPESP scholarship for information technology professionals interested in being part of a breakthrough and innovative scientific project. The recipient will interact with researchers from USP and other NeuroMat collaborating institutions in activities of development, customization, maintenance and deployment of open software related to the scientific goals of the center.

An ANOVA approach for statistical comparisons of brain networks

Daniel Fraiman and Ricardo Fraiman

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

High, diverse impact for NeuroMat research

Scientific articles produced by members of the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) were cited 1,371 times since 2013. In 2014, articles from this center were cited 45 times; in 2018, citations rose to 509. Since its inception, the RIDC NeuroMat has collectively produced 272 publications, roughly 55 items per year.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

César Rennó-Costa, Ana Cláudia Costa da Silva, Wilfredo Blanco and Sidarta Ribeiro

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings – and therefore the theories – are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

Estimating the interaction graph of stochastic neural dynamics

Aline Duarte, Antonio Galves, Eva Löcherbach and Guilherme Ost

In this paper, we address the question of statistical model selection for a class of stochastic models of biological neural nets. Models in this class are systems of interacting chains with memory of variable length. Each chain describes the activity of a single neuron, indicating whether it spikes or not at a given time. The spiking probability of a given neuron depends on the time evolution of its presynaptic neurons since its last spike time. When a neuron spikes, its potential is reset to a resting level and postsynaptic current pulses are generated, modifying the membrane potential of all its postsynaptic neurons. The relationship between a neuron and its pre- and postsynaptic neurons defines an oriented graph, the interaction graph of the model. The goal of this paper is to estimate this graph based on the observation of the spike activity of a finite set of neurons over a finite time. We provide explicit exponential upper bounds for the probabilities of under- and overestimating the interaction graph restricted to the observed set and obtain the strong consistency of the estimator. Our result does not require stationarity nor uniqueness of the invariant measure of the process.




The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).


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