10 Important Differences Between Brains and Computers

[ Artificial Intelligence, Cognitive Neuroscience, Computational
Modeling ]
Posted on: March 27, 2007 12:38 PM, by Chris Chatham

"A good metaphor is something even the police should keep an eye on."
- G.C. Lichtenberg

Although the brain-computer metaphor has served cognitive psychology
well, research in cognitive neuroscience has revealed many important
differences between brains and computers. Appreciating these
differences may be crucial to understanding the mechanisms of neural
information processing, and ultimately for the creation of artificial
intelligence. Below, I review the most important of these differences
(and the consequences to cognitive psychology of failing to recognize
them): similar ground is covered in this excellent (though lengthy)

Difference # 1: Brains are analogue; computers are digital

It's easy to think that neurons are essentially binary, given that
they fire an action potential if they reach a certain threshold, and
otherwise do not fire. This superficial similarity to digital "1's and
0's" belies a wide variety of continuous and non-linear processes that
directly influence neuronal processing.

For example, one of the primary mechanisms of information transmission
appears to be the rate at which neurons fire - an essentially
continuous variable. Similarly, networks of neurons can fire in
relative synchrony or in relative disarray; this coherence affects the
strength of the signals received by downstream neurons. Finally,
inside each and every neuron is a leaky integrator circuit, composed
of a variety of ion channels and continuously fluctuating membrane

Failure to recognize these important subtleties may have contributed
to Minksy & Papert's infamous mischaracterization of perceptrons, a
neural network without an intermediate layer between input and output.
In linear networks, any function computed by a 3-layer network can
also be computed by a suitably rearranged 2-layer network. In other
words, combinations of multiple linear functions can be modeled
precisely by just a single linear function. Since their simple 2-layer
networks could not solve many important problems, Minksy & Papert
reasoned that that larger networks also could not. In contrast, the
computations performed by more realistic (i.e., nonlinear) networks
are highly dependent on the number of layers - thus, "perceptrons"
grossly underestimate the computational power of neural networks.

Difference # 2: The brain uses content-addressable memory

In computers, information in memory is accessed by polling its precise
memory address. This is known as byte-addressable memory. In contrast,
the brain uses content-addressable memory, such that information can
be accessed in memory through "spreading activation" from closely
related concepts. For example, thinking of the word "fox" may
automatically spread activation to memories related to other clever
animals, fox-hunting horseback riders, or attractive members of the
opposite sex.

The end result is that your brain has a kind of "built-in Google," in
which just a few cues (key words) are enough to cause a full memory to
be retrieved. Of course, similar things can be done in computers,
mostly by building massive indices of stored data, which then also
need to be stored and searched through for the relevant information
(incidentally, this is pretty much what Google does, with a few

Although this may seem like a rather minor difference between
computers and brains, it has profound effects on neural computation.
For example, a lasting debate in cognitive psychology concerned
whether information is lost from memory because of simply decay or
because of interference from other information. In retrospect, this
debate is partially based on the false asssumption that these two
possibilities are dissociable, as they can be in computers. Many are
now realizing that this debate represents a false dichotomy.

Difference # 3: The brain is a massively parallel machine; computers
are modular and serial

An unfortunate legacy of the brain-computer metaphor is the tendency
for cognitive psychologists to seek out modularity in the brain. For
example, the idea that computers require memory has lead some to seek
for the "memory area," when in fact these distinctions are far more
messy. One consequence of this over-simplification is that we are only
now learning that "memory" regions (such as the hippocampus) are also
important for imagination, the representation of novel goals, spatial
navigation, and other diverse functions.

Similarly, one could imagine there being a "language module" in the
brain, as there might be in computers with natural language processing
programs. Cognitive psychologists even claimed to have found this
module, based on patients with damage to a region of the brain known
as Broca's area. More recent evidence has shown that language too is
computed by widely distributed and domain-general neural circuits, and
Broca's area may also be involved in other computations (see here for
more on this).

Difference # 4: Processing speed is not fixed in the brain; there is
no system clock

The speed of neural information processing is subject to a variety of
constraints, including the time for electrochemical signals to
traverse axons and dendrites, axonal myelination, the diffusion time
of neurotransmitters across the synaptic cleft, differences in
synaptic efficacy, the coherence of neural firing, the current
availability of neurotransmitters, and the prior history of neuronal
firing. Although there are individual differences in something
psychometricians call "processing speed," this does not reflect a
monolithic or unitary construct, and certainly nothing as concrete as
the speed of a microprocessor. Instead, psychometric "processing
speed" probably indexes a heterogenous combination of all the speed
constraints mentioned above.

Similarly, there does not appear to be any central clock in the brain,
and there is debate as to how clock-like the brain's time-keeping
devices actually are. To use just one example, the cerebellum is often
thought to calculate information involving precise timing, as required
for delicate motor movements; however, recent evidence suggests that
time-keeping in the brain bears more similarity to ripples on a pond
than to a standard digital clock.

Difference # 5 - Short-term memory is not like RAM

Although the apparent similarities between RAM and short-term or
"working" memory emboldened many early cognitive psychologists, a
closer examination reveals strikingly important differences. Although
RAM and short-term memory both seem to require power (sustained
neuronal firing in the case of short-term memory, and electricity in
the case of RAM), short-term memory seems to hold only "pointers" to
long term memory whereas RAM holds data that is isomorphic to that
being held on the hard disk. (See here for more about "attentional
pointers" in short term memory).

Unlike RAM, the capacity limit of short-term memory is not fixed; the
capacity of short-term memory seems to fluctuate with differences in
"processing speed" (see Difference #4) as well as with expertise and

Difference # 6: No hardware/software distinction can be made with
respect to the brain or mind

For years it was tempting to imagine that the brain was the hardware
on which a "mind program" or "mind software" is executing. This gave
rise to a variety of abstract program-like models of cognition, in
which the details of how the brain actually executed those programs
was considered irrelevant, in the same way that a Java program can
accomplish the same function as a C++ program.

Unfortunately, this appealing hardware/software distinction obscures
an important fact: the mind emerges directly from the brain, and
changes in the mind are always accompanied by changes in the brain.
Any abstract information processing account of cognition will always
need to specify how neuronal architecture can implement those
processes - otherwise, cognitive modeling is grossly underconstrained.
Some blame this misunderstanding for the infamous failure of "symbolic

Difference # 7: Synapses are far more complex than electrical logic

Another pernicious feature of the brain-computer metaphor is that it
seems to suggest that brains might also operate on the basis of
electrical signals (action potentials) traveling along individual
logical gates. Unfortunately, this is only half true. The signals
which are propagated along axons are actually electrochemical in
nature, meaning that they travel much more slowly than electrical
signals in a computer, and that they can be modulated in myriad ways.
For example, signal transmission is dependent not only on the putative
"logical gates" of synaptic architecture but also by the presence of a
variety of chemicals in the synaptic cleft, the relative distance
between synapse and dendrites, and many other factors. This adds to
the complexity of the processing taking place at each synapse - and it
is therefore profoundly wrong to think that neurons function merely as

Difference #8: Unlike computers, processing and memory are performed
by the same components in the brain

Computers process information from memory using CPUs, and then write
the results of that processing back to memory. No such distinction
exists in the brain. As neurons process information they are also
modifying their synapses - which are themselves the substrate of
memory. As a result, retrieval from memory always slightly alters
those memories (usually making them stronger, but sometimes making
them less accurate - see here for more on this).

Difference # 9: The brain is a self-organizing system

This point follows naturally from the previous point - experience
profoundly and directly shapes the nature of neural information
processing in a way that simply does not happen in traditional
microprocessors. For example, the brain is a self-repairing circuit -
something known as "trauma-induced plasticity" kicks in after injury.
This can lead to a variety of interesting changes, including some that
seem to unlock unused potential in the brain (known as acquired
savantism), and others that can result in profound cognitive
dysfunction (as is unfortunately far more typical in traumatic brain
injury and developmental disorders).

One consequence of failing to recognize this difference has been in
the field of neuropsychology, where the cognitive performance of brain-
damaged patients is examined to determine the computational function
of the damaged region. Unfortunately, because of the poorly-understood
nature of trauma-induced plasticity, the logic cannot be so
straightforward. Similar problems underlie work on developmental
disorders and the emerging field of "cognitive genetics", in which the
consequences of neural self-organization are frequently neglected .

Difference # 10: Brains have bodies

This is not as trivial as it might seem: it turns out that the brain
takes surprising advantage of the fact that it has a body at its
disposal. For example, despite your intuitive feeling that you could
close your eyes and know the locations of objects around you, a series
of experiments in the field of change blindness has shown that our
visual memories are actually quite sparse. In this case, the brain is
"offloading" its memory requirements to the environment in which it
exists: why bother remembering the location of objects when a quick
glance will suffice? A surprising set of experiments by Jeremy Wolfe
has shown that even after being asked hundreds of times which simple
geometrical shapes are displayed on a computer screen, human subjects
continue to answer those questions by gaze rather than rote memory. A
wide variety of evidence from other domains suggests that we are only
beginning to understand the importance of embodiment in information

Bonus Difference: The brain is much, much bigger than any [current]

Accurate biological models of the brain would have to include some
225,000,000,000,000,000 (225 million billion) interactions between
cell types, neurotransmitters, neuromodulators, axonal branches and
dendritic spines, and that doesn't include the influences of dendritic
geometry, or the approximately 1 trillion glial cells which may or may
not be important for neural information processing. Because the brain
is nonlinear, and because it is so much larger than all current
computers, it seems likely that it functions in a completely different
fashion. (See here for more on this.) The brain-computer metaphor
obscures this important, though perhaps obvious, difference in raw
computational power.

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