Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Zhong, Weishun"'
Autor:
Zhong, Weishun, Can, Tankut, Georgiou, Antonis, Shnayderman, Ilya, Katkov, Mikhail, Tsodyks, Misha
Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across different narratives. We introduce a statistical ensemble of random trees
Externí odkaz:
http://arxiv.org/abs/2412.01806
The extremely limited working memory span, typically around four items, contrasts sharply with our everyday experience of processing much larger streams of sensory information concurrently. This disparity suggests that working memory can organize inf
Externí odkaz:
http://arxiv.org/abs/2408.07637
A promising strategy to protect quantum information from noise-induced errors is to encode it into the low-energy states of a topological quantum memory device. However, readout errors from such memory under realistic settings is less understood. We
Externí odkaz:
http://arxiv.org/abs/2401.06300
Autor:
Zhong, Weishun
Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks such as error correction, learning, and optimization. Despite the empiri
Externí odkaz:
https://hdl.handle.net/1721.1/152568
Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states. Here, we present a new architecture called many-body localized (MBL) hidden Born machine that utilizes both MBL dynamics and hidden units a
Externí odkaz:
http://arxiv.org/abs/2207.02346
A central question in computational neuroscience is how structure determines function in neural networks. The emerging high-quality large-scale connectomic datasets raise the question of what general functional principles can be gleaned from structur
Externí odkaz:
http://arxiv.org/abs/2206.08933
Publikováno v:
Sci. Rep. 11, 9333 (2021)
Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body lear
Externí odkaz:
http://arxiv.org/abs/2004.03604
Far-from-equilibrium many-body systems, from soap bubbles to suspensions to polymers, learn the drives that push them. This learning has been observed via thermodynamic properties, such as work absorption and strain. We move beyond these macroscopic
Externí odkaz:
http://arxiv.org/abs/2001.03623
In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating factors are also statistically independent, disentangled representation
Externí odkaz:
http://arxiv.org/abs/1912.05127
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which
Externí odkaz:
http://arxiv.org/abs/1809.11167