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Samuel Muscinelli
Samuel Muscinelli
Postdoctoral researcher, Columbia University
Email verificata su columbia.edu - Home page
Titolo
Citata da
Citata da
Anno
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
F Colombo, SP Muscinelli, A Seeholzer, J Brea, W Gerstner
arXiv preprint arXiv:1606.07251, 2016
582016
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
SP Muscinelli, W Gerstner, T Schwalger
PLOS Computational Biology 15 (6), e1007122, 2019
30*2019
Ultraviolet asymptotics of glueball propagators
M Bochicchio, SP Muscinelli
Journal of High Energy Physics 2013 (8), 1-51, 2013
262013
Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response
V Esmaeili, K Tamura, SP Muscinelli, A Modirshanechi, M Boscaglia, ...
Neuron 109 (13), 2183-2201. e9, 2021
172021
Exponentially long orbits in Hopfield neural networks
SP Muscinelli, W Gerstner, J Brea
Neural Computation 29 (2), 458-484, 2017
42017
Optimal stimulation protocol in a bistable synaptic consolidation model
C Gastaldi, S Muscinelli, W Gerstner
Frontiers in computational neuroscience, 78, 2019
32019
Divergent sensory processing converges in frontal cortex for a planned motor response
V Esmaeili, K Tamura, SP Muscinelli, A Modirshanechi, M Boscaglia, ...
bioRxiv, 2020.10. 06.326678, 2020
22020
Task-dependent optimal representations for cerebellar learning
M Xie, S Muscinelli, KD Harris, A Litwin-Kumar
bioRxiv, 2022
2022
Optimal routing to cerebellum-like structures
S Muscinelli, M Wagner, A Litwin-Kumar
bioRxiv, 2022
2022
Slow dynamics in structured neural network models
SP Muscinelli
EPFL, 2018
2018
A hierarchy of time scales supports unsupervised learning of behavioral sequences
SP Muscinelli, W Gerstner
BMC neuroscience 16 (S1), P78, 2015
2015
Long Timescale Sequence Recognition Using Adaptive Neural Networks
SP Muscinelli, W Gerstner
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–12