Can sleep protect memories from catastrophic forgetting? Oscar C González, Yury Sokolov, Giri P Krishnan, Jean Erik Delanois, Maxim Bazhenov. eLife 2020;9:e51005

Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training. Building upon data suggesting the importance of sleep in learning and memory, we tested a hypothesis that sleep protects old memories from being forgotten after new learning. In the thalamocortical model, training a new memory interfered with previously learned old memories leading to degradation and forgetting of the old memory traces. Simulating sleep after new ...

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Intel Award on Reinforcement Learning

We are working with Intel to develop brain inspired reinforcement learning algorithms for Intel neuromorphic ‘Loihi’ chip. Read more at https://newsroom.intel.com/news/intel-announces-neuromorphic-computing-research-collaborators/

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DARPA L2M Award

Our lab, in collaboration with UCI team, was awarded by DARPA’s Lifelong Learning Machines (L2M) program to develop new brain inspired AL algorithms capable of memory transfer and continual learning without catastrophic forgetting. Please read full press release at https://www.darpa.mil/news-events/2018-05-03

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Postdoctoral position in computational neuroscience and machine learning

Applications are invited for a post-doctoral research position in the laboratory of Dr. Maxim Bazhenov at the University of California, San Diego to develop neuroscience inspired machine learning algorithms capable of continual learning and adapting to the novel situations and contexts. This project involves close collaboration with the experimental laboratory of Dr. Bruce McNaughton (UC Irvine). The ultimate goal of the work is to advance the knowledge of how human and animal brains learn from experience and ...

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Postdoctoral position in computational neuroscience: sleep and memory consolidation

Applications are invited for post-doctoral research position in the laboratory of Dr. Maxim Bazhenov at the University of California, San Diego to study role of sleep in memory and learning. This computational neuroscience project involves close collaboration with several experimental laboratories. The ultimate goal of the work is to understand how sleep rhythms contribute to consolidation of memory for recent learning and how sensory or electrical stimulation during sleep can enchance memory consolidation.

The successful candidate will be responsible ...

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