Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Paolucci, Pier Stanislao"'
Autor:
Pastorelli, Elena, Yegenoglu, Alper, Kolodziej, Nicole, Wybo, Willem, Simula, Francesco, Diaz, Sandra, Storm, Johan Frederik, Paolucci, Pier Stanislao
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sens
Externí odkaz:
http://arxiv.org/abs/2311.06074
Autor:
Ammendola, Roberto, Biagioni, Andrea, Chiarini, Carlotta, Ciardiello, Andrea, Cretaro, Paolo, Frezza, Ottorino, Cicero, Francesca Lo, Lonardo, Alessandro, Martinelli, Michele, Paolucci, Pier Stanislao, Rossi, Cristian, Simula, Francesco, Turisini, Matteo, Vicini, Piero
APEIRON is a framework encompassing the general architecture of a distributed heterogeneous processing platform and the corresponding software stack, from the low level device drivers up to the high level programming model. The framework is designed
Externí odkaz:
http://arxiv.org/abs/2307.01009
Autor:
Golosio, Bruno, Villamar, Jose, Tiddia, Gianmarco, Pastorelli, Elena, Stapmanns, Jonas, Fanti, Viviana, Paolucci, Pier Stanislao, Morrison, Abigail, Senk, Johanna
Publikováno v:
Appl. Sci. 2023, 13(17), 9598
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory. O
Externí odkaz:
http://arxiv.org/abs/2306.09855
Autor:
Gutzen, Robin, De Bonis, Giulia, De Luca, Chiara, Pastorelli, Elena, Capone, Cristiano, Mascaro, Anna Letizia Allegra, Resta, Francesco, Manasanch, Arnau, Pavone, Francesco Saverio, Sanchez-Vives, Maria V., Mattia, Maurizio, Grün, Sonja, Paolucci, Pier Stanislao, Denker, Michael
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integ
Externí odkaz:
http://arxiv.org/abs/2211.08527
Autor:
De Luca, Chiara, Tonielli, Leonardo, Pastorelli, Elena, Capone, Cristiano, Simula, Francesco, Lupo, Cosimo, Bernava, Irene, De Bonis, Giulia, Tiddia, Gianmarco, Golosio, Bruno, Paolucci, Pier Stanislao
Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in post-sleep task have not been yet modelled. During sleep, the brain cycl
Externí odkaz:
http://arxiv.org/abs/2211.06889
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons, and cannot achieve t
Externí odkaz:
http://arxiv.org/abs/2211.02553
Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing th
Externí odkaz:
http://arxiv.org/abs/2205.10044
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons and cannot achieve the state-of-art performances of artificial intellige
Externí odkaz:
http://arxiv.org/abs/2201.11717
Learning in biological or artificial networks means changing the laws governing the network dynamics in order to better behave in a specific situation. In the field of supervised learning, two complementary approaches stand out: error-based and targe
Externí odkaz:
http://arxiv.org/abs/2109.01039
Autor:
Capone, Cristiano, De Luca, Chiara, De Bonis, Giulia, Gutzen, Robin, Bernava, Irene, Pastorelli, Elena, Simula, Francesco, Lupo, Cosimo, Tonielli, Leonardo, Mascaro, Anna Letizia Allegra, Resta, Francesco, Pavone, Francesco, Denker, Micheal, Paolucci, Pier Stanislao
Thanks to novel, powerful brain activity recording techniques, we can create data-driven models from thousands of recording channels and large portions of the cortex, which can improve our understanding of brain-states neuromodulation and the related
Externí odkaz:
http://arxiv.org/abs/2104.07445