CAREER: Spiking Neural Circuits and Networks with Temporally Dynamic Learning

The human brain is adept at processing vast amounts of real-world information, learning new concepts, and adapting to changing environments. Emulating the plasticity and cognitive abilities of the brain using electronics will enable new paradigms in computing not currently accessible with any digital system. Achieving this goal requires new, efficient approaches that mimic an array of complex mechanisms observed in biology. Chief among these are the rules governing changes in the strength of connections between neurons. Synaptic connection strengths are believed to be ultimately responsible for memory, reasoning, perception, and other higher-order functions. A combination of established and emerging semiconductor device and circuit technologies will be used in this project to investigate networks with biologically-realistic learning. Integrated education and outreach activities will enhance understanding and awareness of biological and electronic neural networks for middle and high school students, regionally and nationally.

This CAREER award supports the development of electronic spiking neural networks (SNNs) that capture the dynamic synaptic learning modalities found in the brain. Circuit blocks with short-term memory will be used in conjunction with devices that change electrical resistance over longer periods to achieve synaptic learning that depends on spiking frequency as well as individual spike times. A primary objective is the design, fabrication, and testing of a neuro-synaptic architecture with learning rates tunable over a range of milliseconds to hours. The physical system will then be used to study cognitive tasks that combine classical conditioning and spatio-temporal pattern recognition. Sensitivity of the network to device variations and control parameters will be examined in terms of pattern recognition accuracy, power consumption, and stability. Benchmarks will also be applied to quantify system performance relative to other techniques. Ideas and concepts generated by this research will ultimately advance the capabilities of intelligent machines employed for a wide variety of tasks such as sensory processing, inference and outcome prediction, anomaly detection, pattern recognition and classification, and big data analytics.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.