The NMI Lab strives to create computing systems that work more like the brain, by combining the fundamental and the applied. Specifically, our long-term research goal is to emulate high-level, cognitive function in dedicated neuromorphic hardware systems.
The lab's approach is to study neural circuits as computational substrates implementing a class of machine learning algorithms, and to design brain-inspired (neuromorphic) architectures that efficiently implement these algorithms.
Outcomes from this research range from brain-computer interfaces to goal-directed and adapting robotic systems.
A Postdoctoral Scholar position in the areas of sensorimotor integration and sensorimotor learning for speech production is available in the ...
Frontiers in Neuroscience. Synaptic sampling machines are the best performing spike-based unsupervised learners to date!