Denis Kleyko wins SSF Future Research Leaders award
Award to provide his lab funding of SEK 15 million ($1.5M) over five years.
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Award to provide his lab funding of SEK 15 million ($1.5M) over five years.
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To study mathematical principles of learning in artificial and natural Intelligence
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Thesis develops a theoretical framework for the hippocampal formation in terms of a compositional associative memory system.
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Chris Kymn, Zeyu Yun, Galen Chuang and Sonia Mazelet presented at Neurips 2024!
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Jamie's PhD thesis work is devoted to the development of fundamental theory for deep learning.
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Chris Kymn, Ed Sandoval and Hadi Vafaii present posters at SfN 2024.
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To teach and lead research on algorithms and learning and reasoning under computational constraints, such as memory, precision and network communication.
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PhD thesis on understanding brain dynamics using control theory and building a generative model of the fruit fly connectome.
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Redwood members present at Vision Science Society (VSS) conference.
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Redwood Center members Christopher Kymn, Sonia Mazelet and Annabel Ng presented a full (20+5) minute talk at the 11th Annual Neuro-Inspired Computational Elements (NICE) 2024.
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Chris Kymn presents a poster at COSYNE 2024, entitled "A residue-number attractor neural network model of error-correcting updates among grid cell modules".
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To study complex brain computation and stochastic neural dynamics.
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Joint grant with Nina Miolane (UC Santa Barbara) and Stella Yu (University of Michigan) to develop Lie theory models of vision.
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To lead a research group at UC Davis studying the computational principles that govern representation learning.
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Sophia Sanborn, Christian Shewmake, Zeyu Yun and Yubei Chen present at the 11th International Conference on Learning Representations (ICLR) held in Kigali, Rwanda.
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Ankit Kumar gives a talk at the Computational and Systems Neuroscience (COSYNE) 2023 conference, held in Montreal, Canada.
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Redwood 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.
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PhD thesis explores mathematically robust invariants in the context of machine learning, signal processing, and associative memories
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Ph.D. thesis explores spatiotemporal properties of natural retinal images.
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Jamie 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.
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To 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.
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Analog 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
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Orthogonal CNNs enforce a type of orthogonality on convolutional filters that makes them easier to train and perform better at classification and inpainting.
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Will continue work on unsupervised learning at the Redwood Center and (in the Fall) Facebook AI Research.
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Denis 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.
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Five new preprints detail work on diverse topics like phasor associative memory, grid cell replay, and unsupervised learning
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PhD 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.
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Yubei 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.
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PhD 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.
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Fellowship 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.
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Redwood Center launches new website and social media accounts
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Bootcamp workshop welcomes new research fellows and visitors to this semester's program on 'The Brain and Computation'
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Louis Kang joins the Redwood Center to study biological neural networks with Mike DeWeese.
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