Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Capone, Cristiano"'
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardw
Externí odkaz:
http://arxiv.org/abs/2405.15616
Behavioral changes in animals and humans, as a consequence of an error or a verbal instruction, can be extremely rapid. Improvement in behavioral performances are usually associated in machine learning and reinforcement learning to synaptic plasticit
Externí odkaz:
http://arxiv.org/abs/2404.07150
Autor:
Capone, Cristiano, Muratore, Paolo
Reinforcement learning (RL) faces substantial challenges when applied to real-life problems, primarily stemming from the scarcity of available data due to limited interactions with the environment. This limitation is exacerbated by the fact that RL o
Externí odkaz:
http://arxiv.org/abs/2402.10069
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