46 citations · 226 across the 18 of their papers we have counts for
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q-bio.NC2021★ 6 cited
Error-based or target-based? A unifying framework for learning in recurrent spiking networks
Cristiano Capone, Paolo Muratore, Pier Stanislao Paolucci
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 l…
q-bio.NC2021★ 16 cited
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…