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Yuwei Cui
Researcher at University of Maryland, College Park
Publications - 21
Citations - 970
Yuwei Cui is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Hierarchical temporal memory & Competitive learning. The author has an hindex of 11, co-authored 21 publications receiving 763 citations. Previous affiliations of Yuwei Cui include University of Science and Technology of China.
Papers
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Journal ArticleDOI
Diverse suppressive influences in area MT and selectivity to complex motion features.
TL;DR: It is demonstrated that direction-selective suppression can impart selectivity of MT neurons to more complex velocity fields and that it can be used for improved estimation of the three-dimensional velocity of moving objects.
Journal ArticleDOI
Inferring Cortical Variability from Local Field Potentials.
TL;DR: A direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials is demonstrated, which provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.
Posted ContentDOI
The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding
TL;DR: This paper describes a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death, and shows how the properties are met using these metrics and targeted artificial simulations.
Proceedings ArticleDOI
A comparative study of HTM and other neural network models for online sequence learning with streaming data
TL;DR: A comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms shows HTM and long-short term memory (LSTM) give the best prediction accuracy.
Journal ArticleDOI
Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells.
TL;DR: A circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast, is presented, which accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train.