Important Information for Proposers

Any proposal submitted in response to this funding opportunity should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

2022 Telluride workshop delved into the relationship between brains and computers

February 21, 2023

Update, February 2023: Papers emerging from the Telluride workshop series include a timely discussion of artificial intelligence in Neural Computation by workshop co-founder Terry Sejnowski, entitled "Large Language Models and the Reverse Turing Test."

An interdisciplinary group of researchers from academia and industry — including engineers, computer scientists, neuroscientists and behavioral and cognitive scientists  — gathered last summer in Telluride, Colorado, for the 2022 Telluride Neuromorphic Cognition Engineering Workshop. Neuromorphic engineers design and fabricate artificial neural systems that are modeled after biological nervous systems. Support from the National Science Foundation for over 25 years has seen the evolution and expansion of neuromorphic engineering research from a focus on low-level sensory processing to current emphases on higher-level problems in perception, cognition and learning.

This annual three-week workshop included background lectures on systems and cognitive neuroscience, practical tutorials and hands-on projects. This year’s workshop, which was supported in part by NSF’s Science of Learning and Augmented Intelligence program, covered the following topic areas:
  • Neuromorphic tactile exploration — the design of robots that can explore their surroundings and detect the properties of objects via touch.
  • Lifelong learning at scale: from neuroscience theory to robotic applications — the development and application of algorithms that allow computers to learn continually from their environment.
  • Cross-modality brain signals: auditory, visual and motor — studying the brains of people who are engaged in multisensory activities like watching videos and learning to play music.
  • Neuromorphic tools, techniques and hardware — helping workshop participants learn to use hardware for neuromorphic engineering projects.

“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.

Opinions, findings or recommendations of NSF awardees or their institutions do not necessarily reflect the views of the National Science Foundation.