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pro vyhledávání: '"Rossi, P. L."'
Autor:
Rossi, Kalel L., Budzinski, Roberto C., Medeiros, Everton S., Boaretto, Bruno R. R., Muller, Lyle, Feudel, Ulrike
Metastability, characterized by a variability of regimes in time, is a ubiquitous type of neural dynamics. It has been formulated in many different ways in the neuroscience literature, however, which may cause some confusion. In this Perspective, we
Externí odkaz:
http://arxiv.org/abs/2305.05328
Sampling methods such as Stratified Random Sampling can be used to select representative samples of schools for randomized controlled trials of educational interventions. However, these methods may still yield external validity bias when participatio
Externí odkaz:
http://arxiv.org/abs/2304.12237
Autor:
Roder, Mateus, Almeida, Jurandy, de Rosa, Gustavo H., Passos, Leandro A., Rossi, André L. D., Papa, João P.
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques,
Externí odkaz:
http://arxiv.org/abs/2211.17045
Autor:
Rossi, Kalel L., Budzinski, Roberto C., Boaretto, Bruno R. R., Muller, Lyle E., Feudel, Ulrike
Understanding the sensitivity of a system's behavior with respect to parameter changes is essential for many applications. This sensitivity may be desired - for instance in the brain, where a large repertoire of different dynamics, particularly diffe
Externí odkaz:
http://arxiv.org/abs/2208.02325
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to
Externí odkaz:
http://arxiv.org/abs/2107.02860
Autor:
Roder, Mateus, de Rosa, Gustavo H., de Albuquerque, Victor Hugo C., Rossi, André L. D., Papa, João P.
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing. Nevertheless, such models suffer from a common probl
Externí odkaz:
http://arxiv.org/abs/2101.06741
We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patte
Externí odkaz:
http://arxiv.org/abs/2006.03643
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