

IBM unveils microchip based on the human brain - sshah2
http://www.newscientist.com/article/dn20810-ibm-unveils-microchip-based-on-the-human-brain.html

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mixmax
_"Eventually, by connecting many such chips, Dharmendra Modha of IBM Research
Almaden, in San Jose, California, hopes to build a shoebox-sized supercomputer
with 10 billion neurons and 100 trillion synapses"

_ and for comparison:

 _"the ultra-efficient human brain is estimated to have 100 billion neurons
and at least 100 trillion synapses"_

This is amazing. IBM is actually reaching towards the old dream of creating a
computer that is as powerful as the human brain. I'm aware that hardware alone
doesn't do it, but still damn impressive.

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technogeek00
Very interesting, but my question is, what are the speed of these chips
compared to conventional ones. The main problem with the human brain is that
while it is fantastic at parallel processing, the speed at what it processes
information is only a few MHz i think. The reverse is true for computers,
great speed, terrible parallel. If these chips solve the problem of parallel
processing but keep speeds the same it would be a great step for computers.

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rbanffy
> the speed at what it processes information is only a few MHz i think

More like Hz, actually. Neurons are slow - I am not sure how frequently can
they fire, but I am sure it is not in the multi-thousand per second range.

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bl
Maximum firing rate differs between neuron types (i.e., cortical pyramidal
neurons versus cerebellar Purkinje neurons) dependent upon cellular membrane
properties (dendrite diameter and ion channel distributions, etc.), but a good
rule-of-thumb to keep in mind is 1 kHz. That maximum "firing rate" is about as
fast as a patch of membrane can generate an action potential (AP, or "spike"),
reset, and fire another.

All that said, a relevant question to ask is, "What is the information content
of a single action potential?" There is not an agreed-upon answer among
neuroscientists. The metaphor linking brain and a personal computer is
extremely strained: to start, it is not quite true that neurons can carry out
logical functions. Neurons are firmly rooted in the analog domain and it is
much more straightforward to infer polynomial-type computations from their
physiology, the order and coefficients being dictated by the particulars of
the scenario. Also, the current thinking is that information is encoded in
bursts or repetitive APs, called "spike trains". But if we must, I'd throw out
a range of 0.5 to 5 bits per spike [Rieke, et al.]. (If you want to go down a
rabbit hole and explore one of _the_ pivotal topics among people studying
neural computation, look up "rate coding" versus "temporal coding". Perhaps
even phrasing the question that way is misleading.)

Here's a side note that may be of interest: during an AP, the trans-membrane
voltage swings from approximately -100 mV to +100 mV (very rough figures). If
you consider that this potential difference is applied across the 3 nano-meter
thick cell membrane, the resulting electric field is very close to the
dielectric breakdown voltage for phospholipid membranes. Our neurons are
constantly operating within a factor or two of literally destroying
themselves! (The preceding paragraph contains very back-of-the-envelope
reasoning. To be more rigorous, I'd have to dig out my notes and reference
materials, but the general point nonetheless holds.)

~~~
rbanffy
That was an impressive explanation. Thanks.

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mattmanser
A couple of other discussions on similar articles in the last few days:

<http://news.ycombinator.com/item?id=2898229>

<http://news.ycombinator.com/item?id=2899299>

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stopsucking
Can we please stop sucking IBM's cock?

-Their chip only has a superficial connection to biological neurons. Any characterization of this chip in terms of "brains" is frankly bullshit. This is like measuring Google's computing clusters in terms of human brainpower.

-Everything their new chip can do has been done in software / FPGAs. While moving ANN's to ASICs can improve training speed (and is important), it does not help with the unsolved algorithmic challenges they present. Furthermore, the hardware structure severely constrains the ways in which these ANNs can be applied.

-Last time this IBM lab made a misleading announcement to the press, it was appropriately ripped to shreds by a very respected individual in the neuroscience community ([http://spectrum.ieee.org/tech-talk/semiconductors/devices/bl...](http://spectrum.ieee.org/tech-talk/semiconductors/devices/blue-brain-project-leader-angry-about-cat-brain))

-Lastly, please refrain from drawing conclusions or extrapolating on science-related articles in the popular press. They are characterized by hyperbole and misinformation, and in many cases are flat-out wrong.

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pland
I'm sure you're quite right and the IEEE link makes oodles of sense, but for
people examining notions of creativity in computational intelligence (for
example), the idea of these chips is quite attractive.

In fact, I remember programming ANNs in horribly non-distributed C paradigms,
and even PureData objects trying to come up with non-garbage computer music in
the 90's and thinking we needed precisely the kind of chip they're trying to
engineer.

This, and the advent of HTMs and other non-ANN ways of going about it, mean
that chips that handle distributed processing for applications that model
human creativity (which is necessarily about concurrent time-based
activities), are a -good- thing no matter what the degree of success, IMHO.

