John V. Arthur
Learning: Hippocampus & Olfactory System

Personal Background

As an undergraduate in electrical engineering, I encountered neuromorphic engineering and was instantly sold. It allowed me to fuse my fervor for chip design and my fascination with neuroscience. My Ph.D. dissertation in the Brains in Silicon Lab within the Bioengineering Department at the University of Pennsylvania was on a Silicon Hippocampus. My postdoc here at Stanford with my advisor, Kwabena Boahen, is on Silicon Olfaction.

John Arthur
 

Research Goals

My research goals reflect those of the lab as a whole—both to decipher the brain's computational principles and to apply such principles to engineer brainlike computers. Specifically, I am interested in the role of spike timing in encoding information and controlling synaptic plasticity. I have chosen to study these issues in archicortex (three layered), which is simpler than neocortex (six layered). I am modeling the hippocampus, which forms new memories, as well as the olfactory cortex, which recognizes familiar odors.

Project Status

My Silicon Hippocampus, with 1024 pyramidal neurons, each with 21 plastic synapses, learns and recalls patterns. Synapses from excitable neurons to their lethargic peers are strengthened, resulting in synchronous activation of a stored pattern despite significant variation among its constituent neurons. This synchrony is enhanced by the chip's inhibitory interneurons, which generate rhythmic activity, as shown by this movie.

Silicon Hippocampus USB2.0 Board

Silicon Hippocampus USB2.0 Board communicate with a computer, enabling it to stimulate on-chip synapses, record silicon neuron spikes, control analog bias voltages (using analog-to-digital converters), read and write binary synaptic weights, and control a scanner to record the continuous activity of individual neurons and synapses. Further, the board includes RAM allowing arbitrary connectivity among its neurons.

I embedded the Silicon Hippocampus Chip in a custom PC Board (right). I designed the board to communicate with a computer. Using USB, the computer can stimulate on-chip synapses, record silicon neuron spikes, control analog bias voltages (using analog-to-digital converters), read and write binary synaptic weights, and control a scanner to record the continuous activity of individual neurons and synapses.

Currently, I am using two Hippocampus Chips to construct a novel model of episodic memory, consistent with anatomical and physiological data. This neuromorphic model will learn and recall pattern sequences, akin to how animals learn and recall sequences of locations in navigational tasks.

My Silicon Olfactory System will consist of an antennal lobe (or olfactory bulb) chip and a mushroom body (or piriform cortex) chip. Work at Gilles Laurent's lab at Caltech has revealed how the antennal lobe recodes similar odor-evoked spatial patterns, reducing their overlap by augmenting the spatial code with a temporal one. And how the mushroom body recognizes these spatiotempral patterns as familiar odors. This system builds on my Silicon Hippocampus, as the underlying mechanisms are similar to those found in the hippocampus.

In another project, I am designing neurons for Neurogrid. These two-compartment neurons will express tunable ionic channels that will endow them with various properties, such as spike-frequency adaptation, bursting, and post-inhibitory rebound.

Publications

ID Article Full Text
C35
D Sridharan, B Percival, J Arthur and K Boahen, An in-silico Neural Model of Dynamic Routing through Neuronal Coherence, Advances in Neural Information Processing Systems 20, D Koller, Y Singer and J Platt Eds., MIT Press, 2008.

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J29 J V Arthur and K Boahen,  Synchrony in Silicon: The Gamma Rhythm, IEEE Transactions on Neural Networks, Volume PP, Issue 99, 2007

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J26
P Merolla, J Arthur, B E Shi and K Boahen,  Expandable Networks for Neuromorphic Chips, IEEE Transactions on Circuits and Systems I, vol 54, No 2. pp. 301-311, February 2007

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M12 Arthur J. Learning In Silicon: A Neuromorphic Model of the Hippocampus. Doctoral Dissertation, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 2006.
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C33
J Lin, P Merolla, J Arthur and K Boahen, Programmable Connections in Neuromorphic Grids, 49th IEEE Midwest Symposium on Circuits and Symtems, pp 80-84, IEEE Press, 2006.
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C29
J V Arthur and K Boahen, Silicon Neurons that Inhibit to Synchronize, IEEE International Symposium on Circuits and Systems, pp 4807-4810, IEEE Press, 2006.
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C25
J V Arthur and K Boahen, Learning in Silicon: Timing is Everything, Advances in Neural Information Processing Systems 18, B Sholkopf and Y Weiss, Eds, MIT Press, 2006.
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J24
T Y W Choi, P Merolla, J Arthur, K Boahen and B E Shi, Neuromorphic Implementation of Orientation Hypercolumns, IEEE Transactions on Circuits and Systems I, vol 52, no 6, pp 1049-1060, June 2005.
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M9
Merolla P, Arthur J, and Wittig J Jr. The USB Revolution. The Neuromorphic Engineer, 2(2):10-11, 2005.
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C22
J V Arthur and K Boahen, Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning, International Joint Conference on Neural Networks, IJCNN'04, IEEE Press, pp 1699-1704, 2004.
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Links

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