Cecilia Romaro, Antonio Carlos Roque, Jose Roberto Castilho Piqueira
There is a strong nexus between the network size and the computational resources available, which may impede a neuroscience study. In the meantime, rescaling the network while maintaining its behavior is not a trivial mission. Additionally, modeling patterns of connections under topographic organization presents an extra challenge: to solve the network boundaries or mingled with an unwished behavior. This behavior, for example, could be an inset oscillation due to the torus solution; or a blend with/of unbalanced neurons due to a lack (or overdose) of connections. We detail the network rescaling method able to sustain behavior statistical utilized in Romaro et al. (2018) and present a boundary solution method based on the previous statistics recoup idea.
by Fernando Jorge da Paixão
The international assessment made in 2018, promoted by the OECD, PISA, was widely disseminated in December 2019. It is a three-year exam that consists of three subjects, reading, mathematics and science. 600,000 15-year-old participated, chosen as a sample representing the 32 million students from the 79 countries participating in the test.
Last month, Open Knowledge Foundation featured an article about the Neuroscience Experiments System Frictionless Tool, also known as NES, a tool developed by the Technology Transfer team of RIDC NeuroMat.
Iara Frondana, Rodrigo R.S. Carvalho, Florencia Leonardi
Epilepsy has been a central topic in computational neuroscience, and in silico models have shown to be excellent tools to integrate and evaluate findings from animal and clinical settings. Among the different languages and tools for computational modeling development, NEURON stands out as one of the most used and mature neurosimulators. However, despite the vast quantity of models developed with NEURON, a fragmentation problem is evident in the great majority of models related to the same type of cell or cell properties. This fragmentation causes a lack of interoperability between the models because of differences in parameters.
Cecilia Romaro, Fernando Araujo Najman, Morgan André
In this paper we present a numerical study of a mathematical model of spiking neurons introduced by Ferrari et al. in an article entitled Phase transition forinfinite systems of spiking neurons. In this model we have a countable number of neurons linked together in a network, each of them having a membrane potential taking value in the integers, and each of them spiking over time at a rate which depends on the membrane potential through some rate function ϕ. Beside being affected by a spike each neuron can also be affected by leaking. At each of these leak times, which occurs for a given neuron at a fixed rate γ, the membrane potential of the neuron concerned is spontaneously reset to 0.
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