May 10, 2023

The 2023 Telluride Neuromorphic Cognition Engineering Workshop will bring together an interdisciplinary group of researchers from academia and industry, including engineers, computer scientists, neuroscientists, behavioral and cognitive scientists. The workshop is supported in part by NSF’s Science of Learning and Augmented Intelligence Program.

“This workshop has a long and successful track-record of advancing and integrating our understanding of biological and artificial systems of learning. Many collaborations catalyzed by the workshop have led to significant technology innovations, and the training of future industry and academic leaders,” says NSF Program Director Soo-Siang Lim.

Among topics at last year's Telluride Workshop seminar series was a timely discussion of large language models and artificial intelligence, published in Neural Computation by workshop co-founder Terry Sejnowski, entitled "Large Language Models and the Reverse Turing Test."

The annual three-week hands-on, project-based meeting is organized around specific topic areas to explore organizing principles of neural cognition that can inspire implementation in artificial systems. Each topic area is guided by a group of experts who will provide tutorials, lectures and hands-on project guidance.  

2023 Topic Areas:

• Audio-motor coupling: the case of speech and music: Explores the idea that learning to speak and learning to play an instrument require similar neural mechanisms by which a mapping between motor and auditory areas must be established and reinforced through auditory-motor feedback.
• Neural learning for control: Focuses on applying existing and developing new learning approaches for movement control by using simulations and simple robotic demonstrations.
• Open-source neuromorphic hardware, software and wetware: Aims to use open-source tools to accelerate growth in neuromorphic research. This involves building upon pre-existing tools, showing off their many capabilities, and interfacing them together with the use of open-source tools to accelerate growth in neuromorphic research.
• Quantum-inspired neuromorphic systems: Delves into the physics of optimization governing the dynamics in neuromorphic systems in order to understand how certain computational tasks are energetically more suitable for neuromorphic architectures.

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