When it fires, a neuron consumes significantly more energy than an equivalent computer operation. And yet, anetwork of coupled neurons can continuously learn, sense and perform complex tasks at energy levels that are currently unattainable for even state-of-the-art processors.
What does a neuron do to save energy that a contemporary computer processing unit doesn’t?
Computer modelling by researchers at Washington University in St. Louis’ McKelvey School of Engineering may provide an answer. Using simulated silicon “neurons,” they found that energy constraints on a system, coupled with the intrinsic property neurons have to move to the lowest-energy configuration, leads to a dynamic, at-a-distance communication protocol that is both more robust and more energy-efficient than traditional computer processors.
The research, from the lab of Shantanu Chakrabartty, the Clifford W. Murphy Professor in the Preston M. Green Department of Systems & Electrical Engineering, was published last month in the journal Frontiers in Neuroscience.
It’s a case of doing more with less.