Sophia Sanborn graduates
PhD thesis explores mathematically robust invariants in the context of machine learning, signal processing, and associative memories
Read MorePhD thesis explores mathematically robust invariants in the context of machine learning, signal processing, and associative memories
Read MoreRedwood Center members Connor Bybee, Denis Kleyko, and Christopher Kymn presented full (20+5) minute talks at the 9th Annual Neuro-Inspired Computational Elements (NICE) workshop, held virtually from March 28-April 1, 2022.
Read MoreRedwood members present at the 2022 Computational and systems neuroscience conference (COSYNE) in Lisbon, Portugal.
Read MorePh.D. thesis explores spatiotemporal properties of natural retinal images.
Read MoreJamie Simon awarded NSF Fellowship; Adrianne Zhong and Chris Kymn selected for NDSEG Fellowship
Read MoreResonator networks are recurrent neural networks designed to solve high-dimensional vector factorization problems. We explain their theory and applications in two new papers appearing in the journal Neural Computation.
Read MoreDylan Paiton and collaborators explain how sparse inference with recurrence and inhibition leads to more selective and robust representations in neural networks.
Read MoreTo lead Neural Circuits and Computations Unit in studying how hippocampal circuits produce memory.
Read MoreAlex Anderson and collaborators develop a mathematical model showing how the eyes' self-generated drift motion can improve high-acuity vision by averaging over retinal inhomogeneities.
Read MoreAnalog devices, noise, and neural network loss surface geometry among topics studied by three new Redwood PhD graduates.
Read MoreJoint source & channel coding allows one to better store and retrieve data from Phase Change Memory
Read MoreOrthogonal CNNs enforce a type of orthogonality on convolutional filters that makes them easier to train and perform better at classification and inpainting.
Read MoreWill continue work on unsupervised learning at the Redwood Center and (in the Fall) Facebook AI Research.
Read MoreDenis is working on Vector Symbolic Architectures for modeling cognitive computation.
Read MoreBrian Cheung, Chris Warner, Dylan Paiton, Mayur Mudigonda, and Shariq Mobin graduate from the Redwood Center and UC Berkeley. Learn about their work here.
Read MoreFive new preprints detail work on diverse topics like phasor associative memory, grid cell replay, and unsupervised learning
Read MorePhD thesis discusses aspects of early vision processing for local sparse feature learning as well as time-asymmetry in linear models of cochlear processing. Eric will be joining Verizon Media Group this spring to work on computer vision.
Read MoreYubei Chen, Dylan Paiton, and Bruno Olshausen describe how sparse coding and manifold learning are connected, leading to a new unsupervised learning algorithm for simultaneously capturing sparse features and low-dimensional transformations of data.
Read MorePhD thesis shows how cortical neurons can simultaneously estimate form and motion from drifting retinal images, providing a first account for why, and how, visual acuity improves with eye movement.
Read MoreFellowship to support work on theoretical tools for understanding network computations in nervous systems
Read MoreWork by Ryan Zarcone and collaborators on joint source-channel coding for PCM devices to be presented at IEEE Data Compression Conference March 27-30.
Read MoreRedwood Center launches new website and social media accounts
Read MoreBootcamp workshop welcomes new research fellows and visitors to this semester's program on 'The Brain and Computation'
Read MoreLouis Kang joins the Redwood Center to study biological neural networks with Mike DeWeese.
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