Neuromorphics: Compiling code by configuring connections

With a power budget of 12 watts, the brain perceives, decides and acts. KAIST's battery-powered HUBO 2 robot uses over a quarter of its energy just to control walking even in predictable environments. BrainGate's implantable brain machine interface (BMI) uses half of its energy to wirelessly transmit recorded neural signals because it cannot decode the intended movement. How can engineers build processors as energy-efficient as the brain? Our approach is to mimic its graded dentritic potentials using analog circuits and its all-or-none axonal spikes using digital circuits. To compute with our energy-efficient silicon neurons, we use use the Neural Engineering Framework (NEF).

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Neuromorphic Robotic Controller
Neurogrid's spiking neurons compute motor torques applied to the robot arm's joints, based on their current angles, which the computer relays to Neurogrid—and uses to animate a virtual copy of the robot arm (computer monitor, right side; left side shows spike activity). The applied torques move the robot arm's end-effector to a user-commanded location, which the computer also relays to Neurogrid. Controlling torques instead of position makes the robot compliant to external perturbations—demonstrated by bringing a board into contact with the pen attached to the robot's end-effector.

NEF enables us to compile perceptual, cognitive and motor algorithms onto networks of spiking neurons.

Developed by Eliasmith and Anderson, NEF defines three principles. First, a population of neurons collectively represents a time-varying vector through nonlinear encoding and linear decoding. Second, alternative linear decodings that transform the vector (linearly or nonlinearly) are used to compute weighted connections from one neural population to the next. Third, recurrent connections—from a neural population back to itself—realize a transformation that governs the vector's dynamics.

Our ultimate goal is to build an autonomous robot that perceives, decides and acts using large-scale networks of silicon neurons.

Our robot will perceive the world through a silicon retina, decide what to do using a silicon cortex, and act on the world with a mechanical arm. Currently, we are using NEF to configure silicon neurons to decode intended movements energy-efficiently for a next-generation implantable BMI and to control a robotic arm energy effiently for a next-generation humanoid robot. We are also architecting an NEF chip capable of implementing networks large enough to realize a fully cognitive system.

Alexander Neckar is architecting an NEF chip.
Sam Fok is prototyping robot controllers and BMIs on Neurogrid.
Samir Menon is building robots and applying operational space control.
Paul Nuyujukian (Shenoy lab) is testing spiking-neural-network BMIs.

Chris Eliasmith
Oussama Khatib
Rajit Manohar
Krishna Shenoy

National Institutes of Health
Office of Naval Research


ID Article Full Text
C42 S Menon, S Fok, A Neckar, O Khatib, and K Boahen, Controlling Articulated Robots in Task-Space with Spiking Silicon Neurons, IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE Press, pp nn-mm, 2014.
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S Choudhary, S Sloan, S Fok, A Necker, E Trautmann, P Gao, T Stewart, C Eliasmith, and K Boahen, Silicon Neurons that Compute, International Conference on Artificial Neural Networks, LNCS vol VV, pp XX-YY, Springer, Heidelberg, 2012. In Press

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C39 J Dethier, P Nuyujukian, C Eliasmith, T Stewart, S A Elassaad, K V Shenoy, and K Boahen, A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm, Advances in Neural Information Processing Systems 24, Curran Associates, Inc., pp 2213-21, 2011.
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C38 J Dethier, V Gilja, P Nuyujukian, S A Elassaad, K V Shenoy, and K Boahen, Spiking Neural Network Decoder for Brain-Machine Interfaces, IEEE EMBS Conference on Neural Engineering, IEEE Press, pp 369-399, 2011.
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J38 J Dethier, P Nuyujukian, S I Ryu, K V Shenoy, and K A Boahen, Design and validation of a real-time spiking-neural-network decoder for brain machine interfaces, Journal of Neural Engineering. Vol 10, p. 036008, Apr 2012
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