Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Goldental, Amir"'
Autor:
Meir, Yuval, Sardi, Shira, Hodassman, Shiri, Kisos, Karin, Ben-Noam, Itamar, Goldental, Amir, Kanter, Ido
Publikováno v:
Sci Rep 10, 19628 (2020)
Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-law to zero with database size. For rapid decision making with one train
Externí odkaz:
http://arxiv.org/abs/2211.08430
Autor:
Hodassman, Shiri, Meir, Yuval, Kisos, Karin, Ben-Noam, Itamar, Tugendhaft, Yael, Goldental, Amir, Vardi, Roni, Kanter, Ido
Publikováno v:
Sci Rep 12, 16003 (2022)
Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing methods apply recurrent neural networks, which suffer from training diffic
Externí odkaz:
http://arxiv.org/abs/2203.13028
Publikováno v:
Phys. Rev. E 105, 014401 (2022)
Refractoriness is a fundamental property of excitable elements, such as neurons, indicating the probability for re-excitation in a given time-lag, and is typically linked to the neuronal hyperpolarization following an evoked spike. Here we measured t
Externí odkaz:
http://arxiv.org/abs/2111.02689
Autor:
Sardi, Shira, Vardi, Roni, Meir, Yuval, Tugendhaft, Yael, Hodassman, Shiri, Goldental, Amir, Kanter, Ido
Publikováno v:
Scientific Reports 10, Article number: 6923 (2020) https://www.nature.com/articles/s41598-020-63755-5
Attempting to imitate the brain functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning. Here, using neuronal c
Externí odkaz:
http://arxiv.org/abs/2005.04106
Publikováno v:
Scientific Reports 6, Article number: 31674 (2016)
Catastrophic failures are complete and sudden collapses in the activity of large networks such as economics, electrical power grids and computer networks, which typically require a manual recovery process. Here we experimentally show that excitatory
Externí odkaz:
http://arxiv.org/abs/1707.06539
Publikováno v:
Scientific Reports 6, Article number: 36228 (2016)
The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that
Externí odkaz:
http://arxiv.org/abs/1707.06549
Publikováno v:
EPL 118 (2017) 46002
Neural networks are composed of neurons and synapses, which are responsible for learning in a slow adaptive dynamical process. Here we experimentally show that neurons act like independent anisotropic multiplex hubs, which relay and mute incoming sig
Externí odkaz:
http://arxiv.org/abs/1707.06501
Publikováno v:
Scientific Reports 7, Article number: 2700 (2017)
We present an analytical framework that allows the quantitative study of statistical dynamic properties of networks with adaptive nodes that have memory and is used to examine the emergence of oscillations in networks with response failures. The freq
Externí odkaz:
http://arxiv.org/abs/1707.05157
Publikováno v:
Front. Neurosci. 9:508 (2015)
The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and delays. Current technological barriers make such a mission unachievab
Externí odkaz:
http://arxiv.org/abs/1601.02189
Publikováno v:
Front. Neural Circuits 9:65 (2015)
Broadband spontaneous macroscopic neural oscillations are rhythmic cortical firing which were extensively examined during the last century, however, their possible origination is still controversial. In this work we show how macroscopic oscillations
Externí odkaz:
http://arxiv.org/abs/1511.00235