no code implementations • 16 Apr 2024 • Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh, Manil-Dev Gomony, Federico Corradi, Paul Detteter, Laura Garrido-Regife, Bernabe Linares-Barranco, Manolis Sifalakis
In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized.
no code implementations • 24 Jan 2021 • Pablo Lopez-Osorio, Alberto Patino-Saucedo, Juan P. Dominguez-Morales, Horacio Rostro-Gonzalez, Fernando Perez-Peña
The Spiking Central Pattern Generator consists of a network of five populations of Leaky Integrate-and-Fire neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus.
