In article <37ca6624 at redeye.it.ki.se>,
Claes Frisk <Claes.Frisk at psyk.ks.se> wrote:
>Are there any reasonable estimations of the capacity(speed, memory,...) of
>the brain in computer terms?
>I realize that the two can't be directly compared, but I guess that it's
>been done a number of times anyway.
This is all "as far as I know", and I'm a computer professional, not a
neurologist (though I've read several books on the subject), so please take
what I say with a grain of salt.
Additionally, it is possible that my information is out of date, and
it is my hope that if I use outdated information in this post, I will be
corrected by a "real" professional neurologist. As such, it should be
understood that I do not so much present this information as authorative,
rather as the best information that I personally possess. It is not my
intention to spread misinformation, and would welcome corrections from
those who know more about it than I do.
That being said, there are things complicating treatment of the question:
* Some low-level details of the neuron and synapse necessary for knowing
the answer to the question are not currently known to medical science.
* The capabilities of an information system is not necessarily equivalent
to the sum of its parts, or even necessarily readily ascertainable by the
examination of its parts.
* The terminology used to describe the capabilities of a computer system
implies both, abiguous relationships between the informational complexity
of that system and its theoretical capability, and between its theoretical
capability and expected capability.
* What we think we do know about how neurons and synapses work is not
readily comparable to how modern computer systems work, though it might be
able to compare them strictly on the basis of informational complexity.
* Not least, there are very few individuals authorative on both computer
science and neuroscience. Thus we have to rely on information scientists
who do not have professional understanding of neuroscience and on
neuroscientists who do not present their findings in terms readily treated
by the techniques of formal information science.
It is *believed* that synapses convey data in binary format, ie they can
fire or not fire, and the only information the neuron possesses about its
neurons are that they are firing or not firing. It is also believed that
neurons function as threshold functions, firing sets of synapses when the
sum of firing synapses from another set exceeds a particular level. This
is the simplest (in terms of information complexity) model of the neuron
held to be true today, being all that has been demonstrated to be true.
Possible complicating factors include: the pattern of neurotransmitter
concentrations between synapses (which changes over time) and their impact
on the firing of synapses, the use of functions within the neuron more
complex than the threshold function, and non-binary use of the signal
generated by firing synapses (ie, as wave forms; the level of energy
released by the synapses peaks at the moment of discharge, and then slowly
falls back to the nonfiring state. This rate of change is different
between synapses). None of these factors have been demonstrated or ruled
out. Any of them would tremendously impact the informational complexity
of the neuron.
On the extreme end, Penrose (who is, in my opinion, completely off the
wall) has hypothesized that individual neurons are quantum computers of
enormous power. In my own opinion, it is reasonable to construct a
hypothetical "low end" model of the neuron as the simple threshold device
which is influenced by gradual changes in neurotransmitter levels not
directly affected by neural computation, and a hypothetical "high end"
model of the neuron as a simple arithmetic and/or logical function, with
each firing synapse contributing up to 4 bits of information apiece via
Under the "low-end" model, we might represent the neuron as a function
of I binary inputs and J outputs collected into sets associated with a
threshold, fired if their thresholds are exceeded by the sum of the I
binary inputs, where I + J = approx 1000 synapses. Converting this to
terminology applicable to computer systems is difficult, but we can call it
an adder and a 500-entry 10-bit lookup table.
Under the "high-end" model, we might represent the neuron as any of a
number of relatively simple functions of any number of inputs comprising
2600 bits of information and generating about 1300 bits of output, or
equivalent to around 40 canonical (32x32->32-bit) RISC instructions, or a
6,760,000-entry 1300-bit lookup table.
The average rate at which any given neuron fires some of its synapses is
60Hz, but the actual rate depends on surrounding activity. Rates exceeding
120Hz (?) (I think this is correct, but I'm working from memory -- might be
higher) have been measured.
Unfortunately finding the complexity of the system is not simply a matter
of multiplying our guesstimates of individual neural complexity by the
number of neurons in the brain. On one hand, the geometry in which the
neurons are connected and the position of the neuron in the brain probably
adds semantic information value to the data it generates. That might not
make much sense -- the neuron doesn't "know" where it is in the brain -- but
consider that in software, two linked lists of N integers each can be much
more useful than one linked list of 2N integers, because the software which
retrieves data from each of them "knows" to treat the data differently.
Jacobson compression (used in CSLIP and PPP data protocols) uses similar
"knowledge of context" to enable (in extreme cases, where checksums are
discarded) one bit of transmitted information to have equivalent "meaning"
to 320 bits (a complete TCP/IP header) of generated information. On the
other hand, the brain is a massively parallel computing system, and
Amdahl's Law states that the computational power of a parallel system is
limited by the rate at which mutually dependent operations occur -- in the
brain's case, about 120Hz.
In my opinion, this last bit is the clincher. Just how much does Amdahl's
Law limit the power of the human brain? Computer Architects have learned a
great deal about the importance of latency in determining the performance of
a computer system. High latency is VERY VERY BAD for performance, and
sharply limits the practical application of computers with extremely high
aggregate computing capabilities, because every software solution to any
interesting real-life problem has at least some dependence on successive
mututally dependent operations during some part of their execution.
On one hand, the human brain seems to mostly deal with problems which we
have learned to deal with extremely well with massively parallel systems,
but on the other hand we do not know whether there are critical aspects of
our thought process which are terribly bottlenecked on dependent operations,
which must be performed at the comparably slow rate of communication between
neurons (120Hz, 1KHz, or whatnot -- it is certainly much much slower than
the 800MHz current computer technology (Alpha 21264a) is capable of).
Bottlenecks must be taken into account before the practical performance of
a computer system can be accurately evaluated, and we have no way of
detecting or accounting for any such serial dependency bottlenecks in the
So we're left with a huge "it depends". It depends on what you're trying
to actually do with the information you have asked for -- are you trying to
determine how well a hypothetical "reprogrammed" human brain would perform
tasks currently performed by computer systems? Are you trying to determine
how many computer systems it would take to simulate the workings of a human
brain? Are you trying to determine how many computer systems it would take
to simulate the workings of a human mind? The latter two are very, very
different. Presumably the former would include the latter, but precludes
using the computer systems for anything other than simulating threshold
devices working in parallel, which would not take advantage of the computer
system's lower latency characteristics, while the latter might use
completely different low-level implementations (which make the best use of
the hardware's lower latency) to generate the same high-level effect. It
might take ten million Alphas to match the power of the 20% of the brain
that recognizes images, but only one Alpha to do with the recognized image
what the 80% remaining brain does. (These numbers are completely bogus, of
course, chosen to emphasize the point.) It depends on too many things that
we know nothing about, and depends on the implementation details of devices
which we do not even have the basic theory to begin constructing.
Sorry for such a downbeat conclusion. :-/