Speaker
Description
While machine learning has made tremendous progress in recent years, there is still a large gap between artificial and natural intelligence.
Closing this gap requires combining fundamental research in neuroscience with mathematics, physics, and engineering to understand the principles of neural computation and cognition.
Mixed-signal subthreshold analog and asynchronous digital electronic integrated circuits offer an additional means of exploring neural computation, by providing a computational substrate that shares many similarities with the one of biological brains.
In this subthreshold region of operation, transistor channels employ the same physics of carrier transport (diffusion) as the proteic channels of real neurons.
Thus, complex neuromorphic circuits and networks built following this approach share many similarities with real synapses, neurons, and cortical neural circuits.
In this presentation I will demonstrate how to build neuromorphic processors that use the physics of their computational substrate to directly emulate the physics of biological neural processes in real-time. I will demonstrate how to build complex recurrent electronic neural circuits with dynamics and response properties strikingly similar to those measured in real neural networks. I will argue that these systems can be used to complement numerical simulations in basic research and real-world applications.
