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How do
neurons know? By
Patricia Smith Churchland Dćdalus Winter 2004 42-50 My knowing anything depends
on my neurons–the cells of my brain.1 More precisely, what I know depends on
the specific configuration of connections among my trillion neurons, on the
neurochemical interactions between connected neurons, and on the response
portfolio of different neuron types. All this is what makes me me. The range of things I
know is as diverse as the range of stuff at a yard sale. Some is knowledge
how, some knowledge that, some a bit of both, and some not exactly either.
Some is fleeting, some enduring. Some I can articulate, such as the
instructions for changing a tire, some, such as how I construct a logical
argument, I cannot. Some learning is conscious, some not. To learn some things, such as how to ride a bicycle, I have to try over and over; by contrast, learning to avoid eating oysters if they made me vomit the last time just happens. Knowing how to change a tire depends on cultural artifacts, but knowing how to clap does not. And neurons are at the bottom of it all. How did it come to pass that we know anything? Early in the history of
living things, evolution stumbled upon the advantages accruing to animals
whose nervous systems could make predictions based upon past correlations. Unlike
plants, who have to take what comes, animals are movers, and having a brain
that can learn confers a competitive advantage in finding food, mates, and
shelter and in avoiding dangers. Nervous systems earn their keep in the
service of prediction, and, to that end, map the me-relevant
parts of the world–its spatial relations, social relations, dangers, and so
on. And, of course, brains map their worlds in varying degrees of complexity,
and relative to the needs, equipment, and lifestyle of the organisms they
inhabit.2 Thus humans, dogs, and
frogs will represent the same pond quite differently. The human, for example,
may be interested in the pond’s water source, the potability of the water, or
the potential for irrigation. The dog may be interested in a cool swim and a
good drink, and the frog, in a good place to lay eggs, find flies, bask in
the sun, or hide. Boiled down to essentials, the main problems for the neuroscience of knowledge are these: How do structural arrangements in neural tissue embody knowledge (the problem of representations)? How, as a result of the animal’s experience, do neurons undergo changes in their structural features such that these changes constitute knowing something new (the problem of learning)? How is the genome organized so that the nervous system it builds is able to learn what it needs to learn? The spectacular progress,
during the last three or four decades, in genetics, psychology,
neuroethology, neuroembryology, and neurobiology has given the problems of
how brains represent and learn and get built an entirely new look. In the
process, many revered paradigms have taken a pounding. From the ashes of the
old verities is arising a very different framework for thinking about ourselves and how our brains make sense of the world. Historically,
philosophers have debated how much of what we know is based on instinct, and
how much on experience. At one extreme, the rationalists argued that
essentially all knowledge was innate. At the other, radical empiricists,
impressed by infant modifiability and by the impact of culture, argued that
all knowledge was acquired. Knowledge displayed at
birth is obviously likely to be innate. A normal neonate rat scrambles to the
warmest place, latches its mouth onto a nipple, and begins to suck. A kitten
thrown into the air rights itself and lands on its feet. A human neonate will
imitate a facial expression, such as an outstuck tongue. But other knowledge,
such as how to weave or make fire, is obviously learned postnatally. Such contrasts have seemed
to imply that everything we know is either caused by genes or caused by
experience, where these categories are construed as exclusive and exhaustive.
But recent discoveries in molecular biology, neuroembryology, and
neurobiology have demolished this sharp distinction between nature and
nurture. One such discovery is that normal development, right from the
earliest stages, relies on both genes and epigenetic conditions. For example,
a female (xx) fetus developing in a uterine environment that is unusually
high in androgens may be born with male-looking genitalia and may have a
masculinized area in the hypothalamus, a sexually dimorphic brain region. In
mice, the gender of adjacent siblings on the placental fetus line in the
uterus will affect such things as the male/female ratio of a given mouse’s
subsequent offspring, and even the longevity of those offspring. On the other hand,
paradigmatic instances of long-term learning, such as memorizing a route
through a forest, rely on genes to produce changes in cells that embody that
learning. If you experience a new kind of sensory-motor event during the
day–say, for example, you learn to cast a fishing line–and your brain
rehearses that event during your deep sleep cycle, then the gene zif-268
will be up-regulated. Improvement in casting the next day will depend on the
resulting gene products and their role in neuronal function. Indeed, five important
and related discoveries have made it increasingly clear just how interrelated
‘nature’ and ‘nurture’ are, and, consequently, how inadequate the old
distinction is.3 First, what genes do is
code for proteins. Strictly speaking, there is no gene for a sucking reflex,
let alone for female coyness or Scottish thriftiness or cognizance of the
concept of zero. A gene is simply a sequence of base pairs containing the
information that allows rna to string together a sequence of amino acids to
constitute a protein. (This gene is said to be ‘expressed’ when it is
transcribed into rna products, some of which, in turn, are translated into
proteins.) Second, natural selection
cannot directly select particular wiring to support a particular domain of
knowledge. Blind luck aside, what determines whether the animal survives is
its behavior; its equipment, neural and otherwise, underpins that behavior.
Representational prowess in a nervous system can be selected for, albeit
indirectly, only if the representational package informing the behavior was
what gave the animal the competitive edge. Hence representational
sophistication and its wiring infra-structure can be selected for only via
the behavior they upgrade. Third, there is a truly
stunning degree of conservation in structures and developmental organization
across all vertebrate animals, and a very high
degree of conservation in basic cellular functions across phyla, from worms
to spiders to humans. All nervous systems use essentially the same
neurochemicals, and their neurons work in essentially the same way, the
variations being vastly outweighed by the similarities. Humans have only
about thirty thousand genes, and we differ from mice in only about three
hundred of those;4 meanwhile, we share about 99.7
percent of our genes with chimpanzees. Our brains and those of other primates
have the same organization, the same gross structures in roughly the same
proportions, the same neuron types, and, so far as we know, much the same
developmental schedule and patterns of connectivity. Fourth, given the high
degree of conservation, whence the diversity of multicellular organisms?
Molecular biologists have discovered that some genes regulate the expression
of other genes, and are themselves regulated by yet other genes, in an
intricate, interactive, and systematic organization. But genes (via rna) make
proteins, so the expression of one gene by another may be affected via
sensitivity to protein products. Additionally, proteins, both within cells
and in the extracellular space, may interact with each other to yield further
contingencies that can figure in an unfolding regulatory cascade. Small
differences in regulatory genes can have large and far-reaching effects,
owing to the intricate hierarchy of regulatory linkages between them. The
emergence of complex, interactive cause-effect profiles for gene expression
begets very fancy regulatory cascades that can beget very fancy organisms–us,
for example. Fifth, various
aspects of the development of an organism from fertilized egg to
up-and-running critter depend on where and when cells are born. Neurons
originate from the daughter cells of the last division of pre-neuron cells.
Whether such a daughter cell becomes a glial (supporting) cell or a neuron,
and which type of some hundred types of neurons the cell becomes, depends on
its epigenetic circumstances. Moreover, the manner in which neurons from one
area, such as the thalamus, connect to cells in the cortex depends very much
on epigenetic circumstances, e.g., on the spontaneous activity, and later,
the experience-driven activity, of the thalamic and cortical neurons. This is
not to say that there are no causally significant differences between, for
instance, the neonatal sucking reflex and knowing how to make a fire.
Differences, obviously, there are. The essential point is that the
differences do not sort themselves into the archaic ‘nature’ versus ‘nurture’
bins. Genes and extragenetic factors collaborate in a complex
interdependency.5 Recent discoveries in
neuropsychology point in this same direction. Hitherto, it was assumed that
brain centers–modules dedicated to a specific task–were wired up at birth.
The idea was that we were able to see because dedicated ‘visual modules’ in
the cortex were wired for vision; we could feel because dedicated modules in
the cortex were wired for touch, and so on. The truth turns out to be
much more puzzling. For example, the visual
cortex of a blind subject is recruited during the reading of braille, a
distinctly non-visual, tactile skill–whether the subject has acquired or
congenital blindness. It turns out, more-over, that stimulating the subject’s
visual cortex with a magnet-induced current will temporarily impede his
Braille performance. Even more remarkably, activity in the visual cortex
occurs even in normal seeing subjects who are blindfolded for a few days
while learning to read braille.6 So long as the blindfold remains firmly in
place to prevent any light from falling on the retina, performance of braille
reading steadily improves. The blindfold is essential, for normal visual
stimuli that activate the visual cortex in the normal way impede acquisition of
the tactile skill. For example, if after five days the blindfold is removed,
even briefly while the subject watches a television program before going to
sleep, his braille performance under blindfold the next day falls from its
previous level. If the visual cortex can be recruited in the processing of
nonvisual signals, what sense can we make of the notion of the dedicated
vision module, and of the dedicated-modules hypothesis more generally? What is clear is that the
nature versus nurture dichotomy is more of a liability than an asset in
framing the inquiry into the origin of plasticity in human brains. Its
inadequacy is rather like the inadequacy of ‘good versus evil’ as a framework
for understanding the complexity of political life in human societies. It is
not that there is nothing to it. But it is like using a grub hoe to remove a
splinter. An appealing idea is that
if you learn something, such as how to tie a trucker’s knot, then that
information will be stored in one particular location in the brain, along
with related knowledge – say, between reef knots and half-hitches. That is,
after all, a good method for storing tools and paper files–in a particular
drawer at a particular location. But this is not the brain’s way, as Karl
Lashley first demonstrated in the 1920s. Lashley reasoned that if
a rat learned something, such as a route through a certain maze, and if that
information was stored in a single, punctate location, then you should be able
to extract it by lesioning the rat’s brain in the right place. Lashley
trained twenty rats on his maze. Next he removed a different area of cortex
from each animal, and allowed the rats time to recover. He then retested each
one to see which lesion removed knowledge of the maze. Lashley discovered
that a rat’s knowledge could not be localized to any single region; it
appeared that all of the rats were some-what impaired and yet somewhat
competent–although more extensive tissue removal produced more serious memory
deficit. As improved experimental
protocols later showed, Lashley’s non-localization conclusion was essentially
correct. There is no such thing as
a dedicated memory organ in the brain; information is not stored on the
filing cabinet model at all, but distributed across neurons. A general
understanding of what it means for information to be distributed over neurons
in a network has emerged from computer models. The basic idea is that
artificial neurons in a network, by virtue of their connections to other
artificial neurons and of the variable strengths of those connections, can
produce a pattern that represents something – such as a male face or a female
face, or the face of Churchill. The connection strengths vary as the
artificial network goes through a training phase, during which it gets
feedback about the adequacy of its representations given its input. But many
details of how actual neural nets–as opposed to computer-simulated ones–store
and distribute information have not yet been pinned down, and so computer
models and neural experiments are coevolving. Neuroscientists are trying to understand the structure of learning by using a variety of research strategies. One strategy consists of tracking down experience-dependent changes at the level of the neuron to find out what precisely changes, when, and why. Another strategy involves learning on a larger scale: what happens in behavior and in particular brain sub-systems when there are lesions, or during development, or when the subject performs a memory task while in a scanner, or, in the case of experimental animals, when certain genes are knocked out? At this level of inquiry, psychology, neuroscience, and molecular biology closely interact. Network-level research
aims to straddle the gap between the systems and the neuronal levels. One
challenge is to understand how distinct local changes in many different
neurons yield a coherent global, system-level change and a task suitable
modification of behavior. How do diverse and far-flung changes in the brain
underlie an improved golf swing or a better knowledge of quantum mechanics? What kinds of
experience-dependent modifications occur in the brain? From one day to the
next, the neurons that collectively make me what I am undergo many structural
changes: new branches can sprout, existing branches can extend, and new
receptor sites for neurochemical signals can come into being. On the other
hand, pruning could decrease branches, and therewith decrease the number of
synaptic connections between neurons. Or the synapses on remaining branches
could be shut down altogether. Or the whole cell might die, taking with it
all the synapses it formerly supported. Or, finally, in certain special
regions, a whole new neuron might be born and begin to establish synaptic
connections in its region. And that is not all.
Repeated high rates of synaptic firing (spiking) will deplete the
neurotransmitter vesicles available for release, thus constituting a kind of
memory on the order of two to three seconds. The constituents of particular
neurons, the number of vesicles released per spike, and the number of
transmitter molecules contained in each vesicle, can change. And yet,
somehow, my skills remain much the same, and my autobiographical memories
remain intact, even though my brain is never exactly the same from day to
day, or even from minute to minute. No ‘bandleader’ neurons
exist to ensure that diverse changes within neurons and across neuronal
populations are properly orchestrated and collectively reflect the lessons of
experience. Nevertheless, several general assumptions guide research. For
convenience, the broad range of neuronal modifiability can be condensed by
referring simply to the modification of synapses. The decision to modify
synapses can be made either globally (broadcast widely) or locally (targeting
specific synapses). If made globally, then the signal for change will be
permissive, in effect saying, “You may change yourself now”–but not dictating
exactly where or by how much or in what direction. If local, the decision
will likely conform to a rule such as this: If distinct but
simultaneous input signals cause the receiving neuron to respond with a
spike, then strengthen the connection between the input neurons and the
output neurons. On its own, a signal from one presynaptic (sending) neuron is
unlikely to cause the postsynaptic (receiving) neuron to spike. But if two
distinct presynaptic neurons–perhaps one from the auditory system and one
from the somatosensory system–connect to the same postsynaptic neuron at the
same time, then the receiving neuron is more likely to spike. This joint
input activity creates a larger postsynaptic effect, triggering a cascade of
events inside the neuron that strengthens the synapse. This general arrangement
allows for distinct but associated world events (e.g., blue flower and plenty
of nectar) to be modeled by associated neuronal events. The nervous system
enables animals to make predictions.7 Unlike plants, animals can use past
correlations between classes of events (e.g., between red cherries and a
satisfying taste) to judge the probability of future correlations. A central
part of learning thus involves computing which specific properties predict
the presence of which desirable effects. We correlate variable rewards with a
feature to some degree of probability, so good predictions will reflect both
the expected value of the reward and the probability of the reward’s
occurring; this is the expected utility. Humans and bees alike, in the normal
course of the business of life, compute expected utility, and some neuronal
details are beginning to emerge to explain how our brains do this. To the casual observer, bees seem to visit flowers for nectar on a willy-nilly basis. Closer observation, however, reveals that they forage methodically. Not only do bees tend to remember which individual flowers they have already visited, but in a field of mixed flowers with varying amounts of nectar they also learn to optimize their foraging strategy, so that they get the most nectar for the least effort. Suppose you stock a small
field with two sets of plastic flowers–yellow and blue–each with wells in the
center into which precise amounts of sucrose have been deposited.8 These
flowers are randomly distributed around the enclosed field and then baited
with measured volumes of ‘nectar’: all blue flowers have two milliliters;
one-third of the yellow flowers have six milliliters, two-thirds have none.
This sucrose distribution ensures that the mean value of visiting a
population of blue flowers is the same as that of visiting the yellow
flowers, though the yellow flowers are more uncertain than the blues. After an initial random
sampling of the flowers, the bees quickly fall into a pattern of going to the
blue flowers 85 percent of the time. You can change their foraging pattern by
raising the mean value of the yellow flowers–for example, by baiting
one-third of them with ten milliliters. The behavior of the bees displays a
kind of trade-off between the reliability of the source type and the nectar
volume of the source type, with the bees showing a mild preference for
reliability. What is interesting is this: depending on the reward profile
taken in a sample of visits, the bees revise their strategy. The bees appear
to be calculating expected utility. How do bees–mere bees–do this? In the bee brain there is
a neuron, though itself neither sensory nor motor, that responds positively
to reward. This neuron, called VUMmx1 (‘vum’ for short), projects very
diffusely in the bee brain, reaching both sensory and motor regions, as it
mediates reinforcement learning. Using an artificial neural network, Read
Montague and Peter Dayan discovered that the activity of vum represents
prediction error–that is, the difference between ‘the goodies expected’ and
‘the goodies received this time.’9 Vum’s output is the
release of a neuromodulator that targets a variety of cells, including those
responsible for action selection. If that neuromodulator also acts on the
synapses connecting the sensory neurons to vum, then the synapses will get
stronger, depending on whether the vum calculates
‘worse than expected’ (less neuromodulator) or ‘better than expected’ (more
neuromodulator). Assuming that the Montague-Dayan model is correct, then a surprisingly simple circuit, operating according to
a fairly simple weight-modification algorithm, underlies the bee’s
adaptability to foraging conditions. Dependency relations
between phenomena can be very complex. In much of life, dependencies are
conditional and probabilistic: If I put a fresh worm on the hook, and if
it is early afternoon, then very probably I will catch a trout here.
As we learn more about the complexities of the world, we ‘upgrade’ our
representations of dependency relations;10 we learn,
for example, that trout are more likely to be caught when the water is cool,
that shadowy pools are more promising fish havens than sunny pools, and that
talking to the worm, entreating the trout, or wearing a ‘lucky’ hat makes no
difference. Part of what we call intelligence in humans and other animals is
the capacity to acquire an increasingly complex understanding of dependency
relations. This allows us to distinguish fortuitous correlations that are not
genuinely predictive in the long run (e.g., breaking a tooth on Friday the
thirteenth) from causal correlations that are (e.g., breaking a tooth and
chewing hard candy). This means that we can replace superstitious hypotheses
with those that pass empirical muster. Like the bee, humans and
other animals have a reward system that mediates learning about how the world
works. There are neurons in the mammalian brain that, like vum, respond to
reward.11 They shift their responsiveness to a stimulus that predicts reward,
or indicates error if the reward is not forthcoming. These neurons project from
a brainstem structure (the ventral tegmental area, or ‘vta’) to the frontal
cortex, and release dopamine onto the postsynaptic neurons. The dopamine,
only one of the neuron-chemicals involved in the reward system, modulates the
excitability of the target neurons to the neurotransmitters, thus setting up
the conditions for local learning of specific associations. Reinforcing a behavior by
increasing pleasure and decreasing anxiety and pain works very efficiently.
Nevertheless, such a system can be hijacked by plant derived molecules whose
behavior mimics the brain’s own reward system neurochemicals. Changes in
reward system pathways occur after administration of cocaine, nicotine, or
opiates, all of which bind to receptor sites on neurons and are similar to
the brain’s own peptides. The precise role in brain function of the large
number of brain peptides is one of neuroscience’s continuing conundrums.12 These discoveries open
the door to understanding the neural organization underlying prediction. They
begin to forge the explanatory bridge between experience-dependent changes in
single neurons and experience-dependent guidance of behavior. And they have
begun to expose the neurobiology of addiction. A complementary line of
research, meanwhile, is untangling the mechanisms for predicting what is
nasty. Although aversive learning depends upon a different set of structures
and networks than does reinforcement learning, here too the critical
modifications happen at the level of individual neurons, and these local
modifications are coordinated across neuronal populations and integrated
across time. Within other areas of
learning research, comparable explanatory threads are beginning to tie
together the many levels of nervous system organization. This research has
deepened our understanding of working memory (holding information at
the ready during the absence of relevant stimuli) spatial learning,
autobiographical memory, motor skills, and logical inference. Granting the
extraordinary research accomplishments in the neuroscience of knowledge,
nevertheless it is vital to realize that these are still very early days for
neuroscience. Many surprises–and even a revolution or two–are undoubtedly in
store. Together, neuroscience,
psychology, embryology, and molecular biology are teaching us about ourselves
as knowers– about what it is to know, learn, remember, and forget. But
not all philosophers embrace these developments as progress. 13 Some believe
that what we call external reality is naught but an idea created in a nonphysical
mind, a mind that can be understood only through introspection and
reflection. To these philosophers, developments in cognitive neuroscience
seem, at best, irrelevant. The element of truth in these
philosophers’ approach is their hunch that the mind is not just a passive
canvas on which reality paints. Indeed, we know that brains are continually
organizing, structuring, extracting, and creating. As a central part of their
predictive functions, nervous systems are rigged to make a coherent story of
whatever input they get. ‘Coherencing,’ as I call it, sometimes entails
seeing a fragment as a whole, or a contour where
none exists; sometimes it involves predicting the imminent perception of an
object as yet unperceived. As a result of learning, brains come to recognize
a stimulus as indicating the onset of meningitis in a child, or an eclipse of
the Sun by the Earth’s shadow. Such knowledge depends upon stacks upon stacks
of neural networks. There is no apprehending the nature of reality except via
brains, and via the theories and artifacts that brains devise and interpret. From this it does
not follow, however, that reality is only a mind-created idea. It
means, rather, that our brains have to keep plugging along, trying to devise
hypotheses that more accurately map But does all of this mean
that there is a kind of fatal circularity in neuroscience – that the brain
necessarily uses itself to study itself? Not if you think about it. The brain
I study is seldom my own, but that of other animals or humans, and I can
reliably generalize to my own case. Neuroepistemology involves many brains –
correcting each other, testing each other, and building models that can be
rated as better or worse in characterizing the neural world. Is there anything left
for the philosopher to do? For the neurophilosopher, at least, questions
abound: about the integration of distinct memory systems, the nature of
representation, the nature of reasoning and rationality, how information is
used to make decisions, what nervous systems interpret as information, and so
on. These are questions with deep roots reaching back to the ancient Greeks,
with ramifying branches extending throughout the history and philosophy of
Western thought. They are questions where experiment and theoretical insight
must jointly conspire, where creativity in experimental design and creativity
in theoretical speculation must egg each other on to unforeseen
discoveries.14 Notes 1 Portions of this paper
are drawn from my book Brain-Wise: Studies in Neurophilosophy ( 2 See Patricia Smith
Churchland and Paul M. 3 In this discussion, I am
greatly indebted to Barbara Finlay, Richard Darlington, and Nicholas
Nicastro, “Developmental Structure in Brain Evolution,” Behavioral and
Brain Sciences 24 (2) (April 2001): 263–278. 4 See John Gerhart and
Marc Kirschner, Cells, Embryos, and Evolution (Oxford: Blackwell,
1997). 5 See also Steven Quartz
and Terrence J. Sejnowski, Liars, Lovers, and Heroes ( 6 See Alvaro Pascual-Leone
et al., “Study and Modulation of Human Cortical Excitability with
Transcranial Magnetic Stimulation,” Journal of Clinical Neurophysiology 15
(1998): 333– 343. 7 John Morgan Allman, Evolving
Brains (New York: Scientific American Library, 1999). 8 This experiment was done
by Leslie Real, “Animal Choice Behavior and the Evolution of Cognitive Architecture,”
Science (1991): 980–986. 9 See Read Montague and
Peter Dayan, “Neurobiological Modeling,” in William Bechtel, George Graham,
and D. A. Balota, eds., A Companion to Cognitive Science (Malden,
Mass.: Blackwell, 1998). 10 Clark N. Glymour, The
Mind’s Arrows ( 11 See Paul W. Glimcher, Decisions,
Uncertainty, and the Brain ( 12 I am grateful to Roger
Guillemain for discussing this point with me. 13 I take it as a sign of
the backwardness of academic philosophy that one of its most esteemed living
practitioners, Jerry Fodor, is widely supported for the following conviction:
“If you want to know about the mind, study the mind –not the brain, and
certainly not the genes” (Times Literary Supplement, 16 May 2003,
1–2). If philosophy is to have a future, it will have to do better than that. 14 Many thanks to Ed
McAmis and Paul Churchland for their ideas and revisions. |
