Dharmendra S. Modha

My Work and Thoughts.

Visionary Developer of Fortan Dies…

By

A New York Times article reported that:

"John W. Backus, who assembled and led the IBM team that created Fortran, the first widely used programming language, which helped open the door to modern computing, died on Saturday at his home in Ashland, Ore. He was 82."

You can see his biography here.  He was one of my personal heroes, a truly inspiring figure.

"Innovation," Mr. Backus said, "was a constant process of trial and error."

“You need the willingness to fail all the time,” he said. “You have to generate many ideas and then you have to work very hard only to discover that they don’t work. And you keep doing that over and over until you find one that does work.” 

Cognitive Computing 2007

By

Building on the success of 2006 Almaden Institute on Cognitive Computing, Robert Hecht-Nielsen, Masoud Nikravesh and I are organizing Cognitive Computing 2007. This promises to be an amazing event, and I invite you to register online.

Dates:

May 2-3, 2007

Venue:

Berkeley Art Museum (Auditorium)

Speakers:

Nobelist Donald Glazer, James Anderson, Michael Arbib, Ed Callaway, Robert Hecht-Nielsen, Edgar Koerner, Dharmendra S. Modha, Lotfi A. Zadeh

Panelists:

Jose M. Carmena, Steve Jurvetson, Paul Rhodes, and Lloyd Watts

Registration is free.

Towards real-time, mouse-scale cortical simulations

By

Last week, we presented a poster on a fascinating new result at the CoSyNe 2007 conference:

Neurobiologically realistic, large-scale cortical and sub-cortical simulations are bound to play a key role in computational neuroscience and its applications to cognitive computing. One hemisphere of the mouse cortex has roughly 8,000,000 neurons and 8,000 synapses per neuron. Modeling at this scale imposes tremendous constraints on computation, communication, and memory capacity of any computing platform.

We have designed and implemented a massively parallel cortical simulator with (a) phenomenological spiking neuron models; (b) spike-timing dependent plasticity; and (c) axonal delays.  

We deployed the simulator on a 4096-processor BlueGene/L supercomputer with 256 MB per CPU. We were able to represent 8,000,000 neurons (80% excitatory) and 6,300 synapses per neuron in the 1 TB main memory of the system. Using a synthetic pattern of neuronal interconnections, at a 1 ms resolution and an average firing rate of 1 Hz, we were able to run 1s of model time in 10s of real time!

I believe that such cortical simulators are the linear accelerators of neuroscience. We are already able to study extremely large-scale cortical dynamics. This is a developing story…please stay tuned in!

Reference:

James Frye, Rajagopal Ananthanarayanan, and Dharmendra S. Modha, "Towards real-time, mouse-scale cortical simulations," CoSyNe: Computational and Systems Neuroscience, Salt Lake City, Utah, Feb 22-25, 2007 PDF

Google and AI

By

The programming language of humans, if you will, would include the workings of your brain, said Google co-founder Larry Page, who offered his hypothesis Friday night during a plenary lecture here at the annual American Association for the Advancement of Science conference. His guess, he said, was that the brain’s algorithms weren’t all that complicated and could be approximated, eventually, with a lot of computational power. Specifically, Page said "When AI happens, it’s going to be a lot of computation, not so much … clever algorithms." Given the size of DNA (~600 MB compressed), the algorithms of the brain are "probably not that complicated."

"…artificial intelligence…I don’t think it’s that far off as people think."

Links:

  1. Press article
  2. Video