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January 30, 2009

IBM Team for DARPA SyNAPSE

IBM

Dr. Stuart Parkin, Physicist and Materials Scientist

Ph.D. Physics, Cambridge (1982); World renowned leader in spintronics materials and devices; inventor of spin-valve sensor and magnetic tunnel junction magnetic random access memory; ~70 issued patents and >350 published papers; Member, National Academy of Sciences; IBM Fellow; Fellow Royal Society (London), Fellow American Physical Society, AAAS, IEEE and MRS awardee, many major international prizes; Director IBM-Stanford SpinAps Center.

Dr. Paul P. Maglio, Cognitive Scientist:

PhD, Cognitive Science, UCSD (1995); Senior Manager of Service Systems Research at IBM, responsible for service science world-wide; 13 issued patents and >90 published papers in computer science, cognitive science, and business; serves on many university and society advisory boards, and has chaired numerous international conferences; Associate Adjunct Professor of Cognitive Science, UC Merced.

Dr. Chung Lam, Technologist

Ph. D., Electrical Engineering, Rensselaer Polytechnic Institute in 1988 on the IBM Resident Study Program.  Dr. Lam has published more than 50 papers and holds more than 70 US patents.  He is a Distinguished Engineer at IBM Research and manages the Phase-Change Memory Project since 2003.

Dr. Bülent Kurdi, Technologist

Ph.D. Optics, The Institute of Optics, University of Rochester (1989); a technical professional who combines broad expertise from variety of complimentary technical disciplines with a proven track record of turning research projects into cost-effective manufacturing technologies while minimizing risk.

Dr. J. Campbell Scott, Physicist

Ph.D. Physics, Univ. of Pennsylvania (1975); World renowned leader in organic electronic materials and devices; inventor in the areas of organic photoconductors, electrophotography, organic photorefractive materials, biochemical sensors, organic light-emitting diodes, and nonvolatile memory; > 17 patents and > 160 scientific and technical publications; Fellow American Physical Society, member Materials Research Society.

Stanford University

Prof. Kwabena Boahen, Neuromorphic Engineer

Ph.D. Computation and Neural Systems, Caltech (1997). Nationally recognized pioneer in neuromorphic engineering; innovations include chips that emulate the retina, thalamus, hippocampus, visual cortex, and retinotectal map formation; >60 publications, including a Scientific American cover story; several distinguished honors, including Packard Fellowship, NSF CAREER Award, ONR Young Investigator Award, and NIH Director’s Pioneer Award; Director, Stanford Brains in Silicon Lab.

Prof. Brian Wandell, Stein Family Professor

Chair of Psychology, and member of Electrical Engineering and Radiology (by courtesy); co-director of Initiative on Human Health; ~10 patents, ~130 published papers; and textbook Foundations of Vision. Troland Award (NAS), Electronic Imaging Scientist of the Year (SPIE), Tillyer Award (OSA), Edridge Green Medal in Ophthalmology, NAS member since 2003.

Prof. H.-S. Phillip Wong,
Nano-technologist

At Stanford since September, 2004 after 16 years at IBM Research. IEEE Fellow, IEEE EDS AdCom member (2001 - 2006). IEDM committee member (1998 - 2007), Technical Program Chair (2006) and General Chair (2007). ISSCC committee member (1998 - 2004), Editor-in-Chief of the IEEE Transactions on Nanotechnology (2005 - 2006). Member of the Emerging Research Devices Working Group of ITRS.

University of Wisconsin-Madison

Prof. Gulio Tononi, Neuroscientist

MD 1985, PhD Neurobiology 1989, Psychiatry 1989 (Pisa); Professor of Psychiatry, Distinguished Chair in Consciousness Science, U. Wisconsin, Madison. Developed the Integrated Information Theory of Consciousness (2004); the Synaptic Homeostasis Hypothesis of sleep function (2003). Published several times in Nature, Science, etc. Authored 3 books on consciousness. NIH Director Pioneer Award, Honorary Doctor, U. Zurich, International Prizes; Many Patents.

Columbia University Medical Center

Prof. Stefano Fusi, Physicist and Theoretical Neuroscientist

 

Ph.D. Physics, Hebrew University of Jerusalem (1999); Professor at ETH, Zurich, from 2005. Since 2007, is also Assistant Professor at Columbia University, NY. He discovered and solved the fundamental problem of memory forgetting in electronic synapses. He is the author of 38 journal papers, some of them published on Nature Neuroscience and Neuron.

Cornell University

Prof. Rajit Manohar, Computer Scientist

Ph.D. Computer Science, Caltech (1998); Leader in asynchronous VLSI design; inventor of GHz-speed FPGA technology and ultra low power processors; ~10 issued patents and >50 published papers; MIT Technology Review TR35 awardee; Founder and Chief Technology Officer, Achronix Semiconductor Corp.

University of California at Merced

Prof. Christopher Kello, Cognitive Scientist

Ph.D. Psychology, University of California, Santa Cruz (1996); Associate Professor of Cognitive Science, University of California, Merced; Internationally recognized leader in neural network modeling of high-level cognition (i.e. human language); 2 patents and >35 published papers; NSF Director’s Award recipient; Member, Psychonomics, Sigma Xi, Cognitive Science Societies; Member, NSF committees on Complexity, Neuroscience, and Cyberinfrastructure.

A Proposal for Mouse Connectivity Project

My colleague, Anthony Ndirango, pointed out a very interesting document that proposes to synthesize complete brainwide neuroanatomical connectivity in mouse at a mesoscopic scale within 5 years and at a cost of less than 20 million dollars.

Title: A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

Authors: Jason W. Bohland, Caizhi Wu, Helen Barbas, Hemant Bokil, Mihail Bota, Hans C. Breiter, Hollis T. Cline, John C. Doyle, Peter J. Freed, Ralph J. Greenspan, Suzanne N. Haber, Michael Hawrylycz, Daniel G. Herrera, Claus C. Hilgetag, Z. Josh Huang, Allan Jones, Edward G. Jones, Harvey J. Karten, David Kleinfeld, Rolf Kotter, Henry A. Lester, John M. Lin, Brett D. Mensh, Shawn Mikula, Jaak Panksepp, Joseph L. Price, Joseph Safdieh, Clifford B. Saper, Nicholas D. Schiff, Jeremy D. Schmahmann, Bruce W. Stillman, Karel Svoboda, Larry W. Swanson, Arthur W. Toga, David C. Van Essen, James D. Watson and Partha P. Mitra

Abstract: In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

January 26, 2009

Martin Rehn and Dileep George

Last Friday, we enjoyed a visit by Dr. Martin Rehn.

Title: Cell assemblies and computation in cortical networks

Abstract: Recurrent neural networks are powerful computational structures. Intractable in the general case, their power is yet to be harnessed, both for practical applications and as a model for the brain. One class of recurrent networks that is theoretically well understood is attractor memory networks. Starting from this idea, we explore extensions that have non-trivial temporal dynamics, and how they apply to sensory coding. It will also be shown how an attractor memory can operate on top of a fairly realistic cortical circuitry, with some conclusions for cortical modelling.

Bio: Martin Rehn is a postdoctoral fellow at the Redwood Center for Theoretical Neuroscience, UC Berkeley, and a Research Scientist at Google. He received a PhD in Computer Science from the Royal Institute of Technology in Stockholm in 2006 and an MSc in Engineering Physics from the same institution in 1999. He is interested in representation and computation in early sensory cortices, associative memory models, and cortical simulations.

On Dec 4, 2008, we had a spirited and wonderful talk by Dr. Dileep George who is Co-Founder and CTO of Numenta. You can find his thesis here.

Title: Towards a Mathematical Model of Cortical Circuits Based on Hierarchical Temporal Learning in the Brain

Abstract: It is well known that the neocortex is organized as a hierarchy. Hierarchical Temporal Memory is a theory of the neocortex that models the necortex using a spatio-temporal hierarchy. The HTM hierarchy is organized in such a way that the higher levels of the hierarchy incorporate larger amounts of space and longer durations of time. The states at the higher levels of the hierarchy vary at a slower rate compared to the lower levels. It is speculated that this kind of organization leads to efficient learning and generalization because it mirrors the organization of the world.

I will start this talk by demonstrating the recent advances at Numenta in using HTM for object recognition. We are able to recognize objects in clutter with a high degree of accuracy. Top-down attention based feedback is used to recognize multiple objects in a scene. Feedback is used to segment out objects from clutter.

I will then describe how the assumptions of hierarchical temporal learning can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of variable memory Markov chains. Bayesian belief propagation equations on  this HTM node gives a set of operation related constraints for the cortical circuits. Anatomical and physiological data provide a second set of constraints related to organization of the circuits. The combination of these two constraints can be used to derive a set of cortical circuits that explain many anatomical and physiological features and predict several other. I will then demonstrate the application of these circuits in the modeling of the subjective contour effect.

Bio: Dileep George is the Chief Technology Officer of Numenta -- a company he co-founded with Jeff Hawkins and Donna Dubinsky. His primary research interests are in understanding the organizational properties of the world and in linking that to the cortical architecture and micro-circuitry.

Dileep joined the Redwood Neuroscience Institute as a Graduate Research Fellow and began working closely with Jeff Hawkins in extending and expressing Jeff's neuroscience theories in mathematical terms. He created the first proof-of-concept program to illustrate these concepts, which triggered the launch of Numenta in 2005. Within five months of Numenta¹s founding, Dileep and his team created the first prototype of HTM technology. Prior to his graduate studies, Dileep worked on developing algorithms for 3G wireless modems.

Dileep holds a Bachelor's degree in Electrical Engineering from the Indian Institute of Technology in Bombay and Master's and Ph.D degrees in Electrical Engineering from Stanford University. Dileep's PhD thesis provides a detailed study of the hierarchical temporal learning in the neocortex.