27 citations · 132 across the 12 of their papers we have counts for
6 papers · 1 filter
Unsupervised sleep-like intra- and inter-layer plasticity categorizes and improves energy efficiency in a multilayer spiking network
Leonardo Tonielli, Cosimo Lupo, Elena Pastorelli +4
Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain ci…
Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Gianluca Gaglioti, Alessandra Cardinale, Cosimo Lupo +10
The simulation of whole-brain dynamics should reproduce realistic spontaneous and evoked neural activity across different scales, including emergent rhythms, spatio-temporal activa…
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
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…
NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model
Chiara De Luca, Leonardo Tonielli, Elena Pastorelli +8
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 po…
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
Cristiano Capone, Cosimo Lupo, Paolo Muratore +1
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 an…
Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
Cristiano Capone, Chiara De Luca, Giulia De Bonis +11
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 imp…