(Or “I, for one, welcome our new cyber-mouse overlords!”)
It’s hard to see this as anything but a distant early warning of some pretty remarkable changes on the near horizon. IBM researchers James Frye, Rajagopal Ananthanarayanan, and Dharmendra S. Modha assembled a simulated mouse cortical hemisphere (that is, a functional half of a mouse brain) on one of the smaller BlueGene/L supercomputers. They then ran the simulation—at ten seconds of computer processing equal to one second of brain function.
In other words: they ran a simulated mouse brain at 1/10 time.
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!
The team published the write-up in the February 5, 2007, edition of Computer Science; a PDF is available of the one-page research report, providing a few technical details.
The human brain has some 100 billion neurons, so this mouse brain simulation is still about 1/12,500 of a simulated human brain. That may sound like a daunting challenge, until a glance at computer history makes clear that such computational capabilities will likely be possible on within 20 years, easily, if not even sooner.
But well before that point, we’ll be able to run simulations of animal brains at accelerated speeds, raising a provocative test of just how important raw cognitive speed is to the emergence of artificial intelligence. Would an accelerated mouse brain simulation simply be a fast-calculating mouse, or will it have other properties and capabilities deriving from the sheer speed? Which would be smarter—a 6,000X faster mouse brain sim, or a 1/2-speed human brain sim?
Some of that is going to depend upon how much of the simulation models actual brain structure, rather than simply the number of connections. That’s likely to be crucial. The brain isn’t simply a haphazard mass of neural junctions, and a functional structure simulation may well prove to be a far greater challenge than simply getting the neural connection sim working. Still, this is not an unsolvable problem, by any extent.
But this raises the question of whatt kinds of programming will be possible with these simulated brains. The IBM simulation simply showed that a functional simulation was possible; evidently, they didn’t try to do anything with the cyber-mouse. It’s not entirely clear what could be done with it. We’re now on the brink of facing a question that had, in the past, been essentially the province of science fiction:
How does one program a simulated mind?
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