write a 2-3-page-long essay overviewing each paper, and one paragraph of personal thoughts 

Annu. Rev. Psychol. 1998. 49:

43

–64
Copyright © 1998 by Annual Reviews Inc. All rights reserved

BRAIN PLASTICITY AND

BEHAVIOR

Bryan Kolb and Ian Q. Whishaw

Department of Psychology, University of Lethbridge, Lethbridge, Alberta, T1K 3M4

Canada; e-mail: kolb@HG.ULETH.CA

KEY WORDS: dendrite arborization, environmental enrichment, cortex, neuropsychology,

experience

ABSTRACT

Brain plasticity refers to the brain’s ability to change structure and function.
Experience is a major stimulant of brain plasticity in animal species as di-
verse as insects and humans. It is now clear that experience produces multi-
ple, dissociable changes in the brain including increases in dendritic length,
increases (or decreases) in spine density, synapse formation, increased glial
activity, and altered metabolic activity. These anatomical changes are corre-
lated with behavioral differences between subjects with and without the
changes. Experience-dependent changes in neurons are affected by various
factors including aging, gonadal hormones, trophic factors, stress, and brain
pathology. We discuss the important role that changes in dendritic arboriza-
tion play in brain plasticity and behavior, and we consider these changes in
the context of changing intrinsic circuitry of the cortex in processes such as
learning.

CONTENTS
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

METHODS OF STUDY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Analysis of Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Analysis of the Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

EXPERIENCE AND THE CHANGING BRAIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Environmental Enrichment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Training in Specific Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Olfactory Experience in Rodents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Plasticity in the Avian Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

0066-4308/98/0201-0043$08.00

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The Invertebrate Nervous System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Dendrites and Behavior in Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

MODULATION OF EXPERIENCE-DEPENDENT CHANGE . . . . . . . . . . . . . . . . . . . . . . 56
Aging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Sex Hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Neurotrophins. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Stress. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

INTRODUCTION

One of the key principles of behavioral neuroscience is that experience can
modify brain structure long after brain development is complete. Indeed, it is
generally assumed that structural changes in the brain accompany memory
storage (Bailey & Kandel 1993). Although the idea that experience can modify
brain structure can probably be traced back to the 1890s (Ramon y Cajal 1928,
Tanzi 1893), it was Hebb who made this a central feature of his neuropsycho-
logical theory (Hebb 1949). Hebb did the first experiments on the conse-
quences of enriched rearing on the behavior of the rat (Hebb 1947). Later, the
group at Berkeley began to demonstrate changes in brain weight, cortical
thickness, acetylcholine levels, and dendritic structure that accompanied the
behavioral changes related to experience (e.g. Diamond et al 1967, 1981;
Globus et al 1973; Rosenzweig & Bennett 1978; Rosenzweig et al 1962). In
the 1970s, and continuing still, William Greenough and his colleagues initi-
ated a multidisciplinary investigation of the cellular effects of rearing animals
in visually or motorically enriched environments (e.g. Greenough & Chang
1989). Perhaps the fundamental point that this group has made over the past
decades is that synapses can form and dendrites can grow well beyond the peri-
od of brain development. Although this point is certainly not unique to Gree-
nough, he and his colleagues have shown most forcefully that the adult mam-
malian brain (and presumably other vertebrate brains as well) can add not only
dendrites and synapses in response to behavioral demands but also supportive
tissue elements such as astrocytes and blood vessels.

There is now an extensive literature correlating neural and glial changes
with behavioral change in species ranging from insects to humans. Because
many other chapters review synaptic changes during development, our focus is
on studies in which some sort of experimental manipulation has been shown to
change behavior and neural structure, especially in mammals.

Assumptions

The study of brain and behavioral correlations necessarily involves assump-

tions about methodology, and this review chapter is no exception. In particu-

44 KOLB & WHISHAW

lar, we assume that changes in the structural properties of the brain will reflect

changes in brain function. Furthermore, we assume that the most likely place

to identify neural changes associated with behavior is at the synapse. In order

to relate synaptic change to behavioral change, it also is assumed in the current

review that synaptic changes can be measured by analysis of the postsynaptic

structure of cells, either by light microscope or electron microscope tech-

niques. We do not consider neurochemical or neurophysiological changes in

the current review.

METHODS OF STUDY

Analysis of Behavior

Three important sets of behavioral distinctions are relevant to plastic changes

in the brain. The first relates to the difference between exercise and skill acqui-

sition. Running in a treadmill is wonderful exercise as it no doubt improves

cardiovascular function, reduces body fat, and improves health in old age, all

of which may contribute to brain plasticity. But running may not require much

in the way of plastic changes to support it. Learning a new skill, such as play-

ing a musical instrument, learning to type, or reading Braille requires exten-

sive practice, and this practice is likely instrumental in changing the neuropil

in relevant brain regions. This distinction is methodologically important be-

cause a group receiving exercise can be used as a control for nonspecific ef-

fects in a group receiving skill training. In most of the experiments reviewed

below some form of exercise regime is administered to a control group that

will subsequently be compared with a group receiving skill training.
A more subtle, and often unrecognized, distinction is that between volun-

tary movements and supporting reactions. Most voluntary movements, such as

advancing a limb to grasp food, require concomitant supporting reactions. For

example, when a quadruped, such as a rat, lifts and advances a limb to reach for

food in an experimental test, it must support its weight with its remaining

limbs. To do so, it usually supports and shifts its weight with the diagonal

couplet of the contralateral forelimb and the ipsilateral hind limb, and it some-

times assists in balancing itself and moving by using its tail (Whishaw & Mik-

lyaeva 1996). Whereas it is widely accepted that acquiring the act of reaching

is a skill that is accompanied by morphological changes in the forelimb area of

motor cortex (Greenough et al 1985), it is unclear whether balancing, weight

shifting, and tail use are also skills in the same sense. This could be tested em-

pirically, but the more important point relates to how much of the brain under-

goes plastic change during the acquisition of the skill of reaching. Perhaps only

the neurons controlling the reaching arm undergo change, but it is more likely

BRAIN PLASTICITY AND BEHAVIOR 45

that the entire motor system will change to varying degrees as the animal ac-

quires the reaching skill. Thus, experiments must be quite complex and in-

clude measures from the brain area of interest, adjacent brain regions, and the

contralateral hemisphere as well as from appropriate control groups.
The third distinction is between recovery and compensation following

brain injury. There is a great deal of interest in how surviving brain tissue

changes to contribute to recovery following brain injury (e.g. Forgie et al 1996,

Jones & Schallert 1994, Prusky & Whishaw 1996). Careful analysis of the be-

havior of rats recovering from injury suggests that much of what appears on

cursory examination to be recovery is actually a compensatory substitution of

new movements for lost movements (Whishaw & Miklyaeva 1996). Thus, the

recovery of behavior following injury may not be due to spared neurons as-

suming the functions of lost neurons but may be due to spared neurons chang-

ing their morphology to support compensatory skills. Because both recovery

and compensation are potentially important avenues for therapeutics follow-

ing brain injury, this distinction, though subtle, is not unimportant.

Analysis of the Brain

The analysis of neuronal change and behavior rests upon the assumption that

changes will be found at the synapse when behavior changes. There are, how-

ever, two problems to solve. First, the visualization of morphology must pro-

vide similar results from animal to animal and study to study. Injections of

neuronal tracers to identify axons will not satisfy this concern because visuali-

zation of axon terminals depends critically upon how many cells are actually

affected by the injection. Furthermore, it is not always possible, a priori, to

know where in the brain to look for experience-dependent change, so injec-

tions into focal areas are often impractical. Second, because there are so many

neurons in the brain, it is essential that only a subset of cells is stained, at best

randomly, throughout the brain. Golgi-type stains solve these concerns. Some-

where between 1%–4% of neurons are stained, and there is good evidence that

the staining is random (e.g. Pasternak & Woolsey 1987). Furthermore, modern

Golgi techniques (e.g. Golgi-Cox; see Gibb & Kolb 1997) provide reliable and

extensive staining. Once the cells are stained with a Golgi procedure, the den-

dritic length can be measured with the aid of a light microscope. Cells are

traced using either some sort of semiautomated imaging system or a camera lu-

cida procedure in which cells are drawn with pen and ink. The length of den-

dritic arborization and the density of synaptic spines then can be estimated us-

ing various methods (e.g. Capowski 1989, Kolb et al 1997a). The rationale for

selecting dendrites is based upon their special attributes. In particular, den-

drites represent up to 95% of the receptor surface with which neurons form

46 KOLB & WHISHAW

connections (Schade & Baxter 1960). The dendrites grow and retract in re-

sponse to various events including neuronal activity, various chemicals, and

injury to adjacent neurons. This makes dendrites one of the more sensitive in-

dicators of change within the CNS. Finally, because dendrites form a large

fraction of the neuropil, they are an important indicator of the functional ca-

pacity of neural networks. Furthermore, because the majority of excitatory

synapses are found on synaptic spines, measurements of spine characteristics

including their density, shape, and ultrastructural characteristics also can be

made to supplement the dendritic results. (Of course, dendrites do not allow

quantification of the actual number of synapses; this can only be done using

electron microscopic procedures.)

EXPERIENCE AND THE CHANGING BRAIN

We can now identify a large range of neural changes associated with experi-

ence. These include increases in brain size, cortical thickness, neuron size,

dendritic branching, spine density, synapses per neuron, and glial numbers.

The magnitude of these changes should not be underestimated. For example, in

our own studies of the effects of housing rats in enriched environments, we

consistently see changes in young animals in overall brain weight on the order

of 7%–10% after 60 days (e.g. Kolb 1995). This increase in brain weight repre-

sents increases in glia, blood vessels, neuron soma size, dendritic elements,

and synapses. It would be difficult to estimate the total number of increased

synapses, but it is probably on the order of 20% in the cortex, which is an ex-

traordinary change!

Environmental Enrichment

The logic of enrichment studies is that one group of animals is placed in labo-

ratory cages while a second group is housed in a more stimulating environ-

ment, the extreme case being Hebb’s home (Hebb 1947). Hebb’s enrichment

procedure was extreme because Hebb’s rats roamed fully around his home,

whereas most studies of this sort have placed the experimental animals in large

enclosures that contain visually stimulating objects and an opportunity to in-

teract haptically with the environment, including the objects. In many studies,

the objects are changed routinely and in some studies the social housing condi-

tions may also be manipulated. As mentioned above, it was the group of Ben-

nett, Krech, Rosenzweig, Diamond, and their colleagues at the University of

California, Berkeley that first showed large changes in various measures of

cortical morphology. As important and seminal as the Berkeley experiments

were, they had the weakness that they did not demonstrate changes in brain or-

ganization so much as in brain size. It was not until the early 1970s that several

BRAIN PLASTICITY AND BEHAVIOR 47

groups, including the Berkeley group, began to look at dendritic fields (e.g.

Globus et al 1973, Uylings et al 1978). The most thorough studies of this sort

were done, however, by Greenough. Typical experiments showed that the den-

dritic fields of these neurons increased by about 20% relative to cage-reared

animals (e.g. Greenough & Volkmar 1973; Volkmar & Greenough 1972).

These effects were not restricted to the visual cortex, although other regions

tended to show lesser effects and some cell types were relatively unaffected

(e.g. Greenough et al 1973). A parallel set of studies has examined changes in

the cerebellum of animals trained in complex motor tasks and, as might be an-

ticipated, there are parallel changes in the Purkinje cells, which are the major

output cell of the cerebellum. Furthermore, as in the studies of neocortical re-

gions, there is evidence that neuronal changes are not inevitable consequences

of experience because cerebellar granule cells do not show the same changes

(e.g. Floeter & Greenough 1979).
Although most studies of environment-dependent changes in the cortex

have been done with rodents, several studies have used monkeys or cats. In
general, these studies have found similar results (e.g. Beaulieu & Colonnier
1987, Floeter & Greenough 1979, Stell & Riesen 1987). One curious differ-
ence between the rodent and primate studies appears to be the effects upon the
visual system. Because monkeys are highly visual compared to rats, one might
predict greater effects upon the visual cortex of monkeys; yet the opposite ap-
pears to be true. In fact, it appears that the effects upon the primary visual cor-
tex of monkeys reared in enriched environments are negligible (e.g. Riesen et
al 1977, Struble & Riesen 1978). One explanation is that much of the explora-
tion of the visual world of monkeys is done without movement, and because
monkeys in relatively impoverished housing can still visually explore their en-
vironment, this stimulation may be sufficient to ensure the development of vis-
ual cortical synapses. In contrast, the visual system of the rat has relatively
poor acuity and is not designed for pattern vision so much as for spatial naviga-
tion. The gathering of spatial information likely requires movement in space.
An alternate explanation is that because the visual areas of the primate have
expanded dramatically, and because the primary visual cortex is multifunc-
tional, then it is “higher” visual areas that show greater experience-dependent
changes. In this case, one might predict that visual experiences that empha-
sized object exploration and recognition would lead to growth in the ventral
visual pathway, whereas visual experiences that emphasized visuomotor guid-
ance, such as in climbing or object manipulation, would lead to growth in the
dorsal visual pathway (for a discussion of the two pathways, see Milner &
Goodale 1995).

Most studies of dendritic change have used a Golgi-type technique to meas-

ure dendritic space, and from this there is an assumption that dendritic space is

48 KOLB & WHISHAW

correlated tightly with synaptic numbers. Turner & Greenough (1983, 1985)

examined this hypothesis directly by calculating the number of synapses per

neuron in the cortex of animals housed in enriched environments. They found

an increase of about 20% in the number of synapses per neuron in the brains of

enriched versus cage-reared animals. Thus, although the density of synapses in

a section of cortical tissue is relatively constant in enriched and cage-reared

animals, there is more dendritic space in the enriched animals and, conse-

quently, there are more synapses per neuron. Similarly, Beaulieu & Colonnier

(1988) analyzed the number and type of synapses in cats reared in laboratory

cages or in enriched housing. Like Turner & Greenough, they found that syn-

aptic changes correlated with experience. One additional finding, however,

was that experience increased the number of excitatory synapses per neuron

and decreased the number of inhibitory ones in the visual cortex. Thus, enrich-

ment had modified the excitatory-inhibitory equilibrium of the visual cortex.

One prediction from this observation is that neurons in the cortex of enriched

animals would be more reactive to visual stimulation than those in impover-

ished animals.
It is reasonable to expect that if there are increases in the size of the den-

dritic fields of neurons, and correspondingly in the number of synapses per

neuron, then these neurons will require more support both from glial cells, es-

pecially astrocytes, and from blood vessels. In one heroic series of studies,

Sirevaag & Greenough (e.g. 1987, 1988, 1991) used light and electron micro-

scope techniques to analyze 36 different aspects of cortical synaptic, cellular,

and vascular morphology in rats raised in complex or in caged-housing envi-

ronments. The simple conclusion was that there is a coordinated change not

only in neuronal morphology but also in glial, vascular, and metabolic pro-

cesses in response to differential experiences. Thus, not only are there more

synapses per neuron in animals with enriched experience, there is also more

astrocytic material, more blood capillaries, and a higher mitochondria volume.

(Mitochondrial volume is used as a measure of metabolic activity.) It is there-

fore clear that when the brain changes in response to experience there are the

expected neural changes but there are also adjustments in metabolic require-

ments of the larger neurons. One interesting implication of this conclusion is

that things that influence the maintenance and adjustment of the metabolic

components of the aging brain can be expected to influence the brain’s capac-

ity for neural change as well (e.g. Black et al 1987, 1989). This speaks to the

importance of examining the effects of exercise and nutrition on the brain’s ca-

pacity for change, especially in senescence. It is important to note in this con-

text, however, that merely having exercise is not sufficient to induce neuronal

changes. Black et al (1990) trained animals to negotiate a complex obstacle

course (“acrobat rats”) or placed rats in running wheels where they obtained

BRAIN PLASTICITY AND BEHAVIOR 49

forced exercise. The animals in the wheels showed increased capillary forma-

tion but no change in cerebellar Purkinje cell synapses, whereas the acrobat

rats showed a 30% increase in Purkinje synapses. Thus, merely increasing neu-

ronal support does not change the neurons. The critical feature for neuronal

change is presumably increased neuronal processing, which would be facili-

tated by a complementary increase in metabolic support.

Training in Specific Tasks

Although it is tempting to conclude that the synaptic changes observed in ani-
mals housed in complex environments reflect changes in learning about the en-
vironment, there is little direct evidence of this. One way to approach this issue
is to train animals in specific tasks and then to demonstrate specific changes in
dendritic fields of neurons in regions suspected of being involved in the per-
formance of such tasks. Perhaps the most convincing studies of this sort were
done by Chang & Greenough (1982). These studies took advantage of the fact
that the visual pathways of the laboratory rat are about 90% crossed. That is,
about 90% of the cortical projections from the left eye project via the right lat-
eral geniculate nucleus to the right hemisphere. Chang & Greenough placed
occluders on one eye of rats and then trained the animals in a visual maze.
Comparison of the neurons in the two hemispheres revealed that those in the
trained hemisphere had larger dendritic fields. This experiment is compelling
because the rest of the two hemispheres (e.g. auditory, somatosensory, or ol-
factory regions) would still have interacted with the maze, and both hemi-
spheres would be required for the motor demands. It was only the visual cortex
contralateral to the open eye that could process and/or store the task-specific
visual information, however; and this was reflected by the specific dendritic
changes in that hemisphere.

Another experiment is relevant here. Although they did not train animals in

a visual learning task, Tieman & Hirsch (1982) raised cats with lenses that re-

stricted visual exposure to lines oriented vertically or horizontally. Many pre-

vious studies had shown that cells in the visual cortex of cats with such re-

stricted experience show a marked change in their tuning characteristics.

Hence, neurons in cats with selective exposure to lines of vertical orientation

are most excitable when presented with lines of the same orientation. The new

wrinkle in the Tieman & Hirsch study was that they examined the morphology

of visual cortical neurons from cats with selective horizontal or vertical visual

experience. Cats raised in a normal environment showed a random distribution

of orientation of dendritic fields, but cats raised with selective experiences

showed a change in the orientation of the dendritic fields. These changes were

specific, too, because they occurred in pyramidal cells in visual cortex and not

in the adjacent stellate cells.

50 KOLB & WHISHAW

A second set of experiments has taken advantage of the fact that rats are

very talented at using their forepaws to retrieve food from tubes, through bars,

and so on. Because the cortical control of the forelimbs is largely crossed, it is

possible to train one limb to reach for food and to compare the layer V neurons

in the forelimb region of motor cortex, many of which form the cortical spinal

tract, in the trained and untrained hemispheres. Several studies have shown

dendritic changes in the expected neurons (Greenough et al 1985, Kolb et al

1997a, Withers & Greenough 1989).
The changes in dendritic fields seen in the studies of visual and motor learn-

ing are strikingly reminiscent of the changes seen in studies of enriched rear-

ing, which have been taken as evidence that the observed changes in synaptic

connectivity in animals in enriched environments are somehow involved in

memory and learning (Greenough & Chang 1989). While this is a reasonable

conclusion, there may be important differences in details of dendritic change

in the enrichment and learning studies. It is generally found that enrichment

not only increases dendritic length but also increases the density of synaptic

spines on the dendrites. In contrast, animals trained in specific tasks show

changes in dendritic length but not in spine density (Kolb et al 1996). Thus, it

appears that although there are marked similarities between the effects of en-

riched rearing and specific training on dendritic fields, there may be differ-

ences in other measures of dendritic morphology, especially spine density.

Olfactory Experience in Rodents

Rodents have a keen sense of smell so it is reasonable to suppose that experi-

ence would have significant effects upon the structure of the olfactory system.

In fact, the general finding is that olfactory deprivation leads to restricted mor-

phological development of the olfactory system, whereas olfactory training or

olfactory “enrichment” leads to enhanced development (e.g. Doving & Pinch-

ing 1973, Pinching & Doving 1974, Rehn & Breipohl 1986, Rehn et al 1986)

(see also the extensive studies by Leon and colleagues, e.g. Leon 1992a,b).

One surprising finding is that olfactory experience not only changes the mor-

phology of existing neurons, but it also alters the number of neurons. For ex-

ample, odor deprivation results in reductions in neuronal number (e.g. Brunjes

& Frazier 1986, Meisami & Safari 1981, Skeen et al 1986), whereas enriched

odor exposure leads to increased neuron numbers (Rosselli-Austin & Williams

1990). This neuronal increase is not trivial, being in the order of 35-40%! Evi-

dence of increased neuron numbers in the olfactory system is especially in-

triguing because it has not been seen in analyses of neocortical or cerebellar

cortex. One important difference between the olfactory system and neocortical

and cerebellar regions is that olfactory neurons are generated throughout the

lifetime of rodents (e.g. Lois & Alvarez-Buylla 1994). Thus, it is likely that en-

BRAIN PLASTICITY AND BEHAVIOR 51

hanced olfactory experience influences neuronal growth in the olfactory bulb

throughout life. One possible reason for this could be that the addition and de-

letion of olfactory neurons throughout life allow a mechanism for the nervous

system to form new olfactory memories and to modify existing ones. It is note-

worthy that the other forebrain structure that generates neurons throughout

adulthood is the dentate gyrus of the hippocampus, and this area has been im-

plicated in certain types of learning and memory.

Plasticity in the Avian Brain

Three general types of studies look at brain plasticity and behavior in birds.

These include studies of bird song, imprinting, and one-trial learning. Studies

of bird song have been reviewed extensively elsewhere (e.g. Bottjer & Arnold

1997) and largely have focused on the development of neurons and their con-

nectivity. Our emphasis here therefore is on imprinting and one-trial learning.
Imprinting is a process whereby an organism learns, during a sensitive peri-

od in development, to restrict its social preferences to a specific class of ob-
jects (e.g. Bateson 1966). Imprinting is especially common in precocial birds
such as chickens or geese. Within hours of hatching, a young bird will ap-
proach and follow a moving object, which may or may not resemble an adult
female of the same species. Horn and his colleagues (for reviews, see Dudai
1989, Horn 1985) have identified a specific neural region (region IMHV) in
the chick brain that changes morphologically during imprinting. For example,
there is increased metabolic activity and genetic (RNA) activity in IMHV dur-
ing imprinting. Morphological studies have emphasized ultrastructure where it
has been shown that imprinting is correlated with an increase in the length of
the postsynaptic density (PSD) of spine synapses in the IMHV, but only in the
left hemisphere (Horn et al 1985). The PSD is the active receptor-dense region
of the postsynaptic cell. Thus, as the PSD lengthens, the number of receptors
for neurotransmitters increases (Matus et al 1981). Horn (1985) noted that
small changes in the length of the postsynaptic density provide an effective
way for presynaptic cells to control the firing of postsynaptic cells, or to con-
trol local synaptic interactions. Significantly, there does not seem to be an in-
crease in synapse number in the IMHV during imprinting.

Several studies have taken advantage of the observation that one-day-old

chicks peck spontaneously at a small bright chrome bead. For instance, Patel &

Stewart (1988) coated the bead with either a substance with an aversive taste or

nonaversive water. Chicks presented with the aversive taste learn in one trial to

avoid the bead, whereas those presented with the nonaversive bead continue to

peck (for a review, see Rose 1985). Various regions of the chick brain, such as

the hyperstriatum, show enhanced activity following training as revealed both

by electrophysiological investigations and studies of glucose accumulation.

52 KOLB & WHISHAW

Patel & Stewart used a Golgi technique to impregnate chick brains 25 h after

training and found a twofold increase in spine density in the neurons in a re-

gion of the hyperstriatum (intermediate medial hyperstriatum ventrale) in the

“trained” chicks. They concluded that this represented an increase in synapses

that reflected the learning. This interpretation was supported by a second study

in which Patel et al (1988) trained chicks on the passive avoidance task de-

scribed above but, in their experiment, half of the trained chicks were given a

subconvulsive transcranial electroshock 5 min after training. This procedure

rendered about half of the trained animals amnesic for the experience. The

spine density was found to be higher in the chicks that remembered the aver-

sive nature of the training stimulus than in the chicks rendered amnesic. This

finding argues strongly in favor of a specific role for dendritic spines in

experience-dependent memory formation in the chick.
There is one additional study that suggests that their conclusion may not be

quite correct, however. Wallhausser & Scheich (1987) presented newly

hatched chicks with either a hen or an acoustic stimulus, with the goal of im-

printing the chicks to the visual or auditory stimulus. The neurons in different

regions of the hyperstriatum of the imprinted chicks were compared with those

of isolated chicks. There was a decrease in spine density. Thus, in the first

study, there was an increase 25 h after training, whereas in the latter study there

was a decrease 7 days after training. The simplest conclusion from the chick

studies is that the novel stimulation may cause an initial rapid increase in spine

density, followed by a pruning. The critical experiment here would be to exam-

ine the neurons in the brains of animals killed at different times in the training.

The Invertebrate Nervous System

There is a burgeoning literature on the effects of experience on the morphol-

ogy of neurons of invertebrates, both during metamorphosis (e.g. Jacobs &

Weeks 1990, Kent & Levine 1993) and in response to experience (e.g. Hoy et

al 1985). Two studies on Drosophila are especially intriguing. In one, Technau

(1984) showed that the complexity of neurons in Drosophila melanogaster de-

pends upon the flies’ living conditions. Flies were housed for 3 weeks either

singly in small plastic vials or in groups of 200 in larger enclosures with col-

ored visual patterns on the walls, various odor sources, and plants. Analysis of

the Kenyon cell fibers in the mushroom bodies (cells in the “brain” of the fly)

showed about 15% more fibers in the enriched versus impoverished flies. A

subsequent study by Heisenberg et al (1995) showed that most regions of the

Drosophila brain were continuously reorganized throughout life in response to

specific living conditions. In particular, social and sexual activity was associ-

ated with increased brain size, as was the volume of space available. These

experience-dependent changes in Drosophila are remarkable and leave little

BRAIN PLASTICITY AND BEHAVIOR 53

doubt that experience is a major force in shaping the nervous system of all ani-

mals. Furthermore, changes in insect brains are not only seen in artificial lab

experiments but can also be seen in ecologically valid settings. For example,

Withers et al (1993) examined the changes in the brain of the honey bee in rela-

tion to the division of labor in adult worker bees. Adult worker bees spend

about the first 3 weeks of their 4–7-week life performing a variety of tasks

within the hive, including caring for the queen and brood (“nursing”). They

then make a dramatic transition in behavior and begin to forage outside for

food. Food foraging is a complex behavior that requires that the animal learn

the location of both the hive and the food and the nature of different foods, as

well as learn to recognize and use species-typical signals about food sources

from other bees. Withers et al (1993) found not only that the behavioral change

is associated with striking changes in brain structure, but that these changes are

dependent not on the age of the animal but on its foraging experience. This

honey bee model offers a new entry into the cellular mechanisms of neural and

behavioral plasticity.

Dendrites and Behavior in Humans

On the basis of studies in laboratory animals it is reasonable to expect correla-

tions between neuronal structure and behavior in humans. One way to ap-

proach this would be to look for a relationship between cell structure and edu-

cation. Jacobs et al (1993) did, in fact, consider this question and found a rela-

tionship between extent of dendritic arborization in a cortical language area

(Wernicke’s area) and amount of education. Hence, the cortical neurons from

the brains of deceased people with university education had more dendritic ar-

bor than those from people with high school education who, in turn, had more

dendritic material than those with less than high school education. Of course, it

may have been that people with larger dendritic fields were more likely to go to

university, but that is not easy to test.
Another way to look at the relationship between human brain structure and

behavior is to consider the functional abilities of people and to correlate them

with neuronal structure. For example, one might expect to find differences in

language-related areas between people with high and low verbal abilities. This

experiment is difficult to do, however, because it presupposes behavioral

measures before death, and this is not normally done. However, Jacobs et al

(1993) considered this possibility by taking advantage of the now well-

documented observation that females have verbal abilities that are superior to

those of males (for a review, see Kolb & Whishaw 1996). Thus, when they ex-

amined the structure of neurons in Wernicke’s area, they found that females

have more extensive dendritic arbors than males. Furthermore, in a subsequent

study, Jacobs et al (1993) found that this sex difference was present as early as

54 KOLB & WHISHAW

age 9, suggesting that such sex differences emerge within the first decade.

These sex differences in cortical architecture in humans are parallel to those

reported in other studies showing sex differences in cerebral blood flow and

glucose metabolism, with females having a level about 15% higher than that of

males (e.g. Baxter et al 1987).
Scheibel et al (1990) approached the matter in a slightly different way.

They began with two hypotheses. First, they suggested that there is a relation-

ship between the complexity of dendritic arbor and the nature of the computa-

tional tasks performed by a brain area. To test this hypothesis, they examined

the dendritic structure of neurons in different cortical regions that they pro-

posed to have functions that varied in computational complexity. For example,

when they compared the structure of neurons corresponding to the somatosen-

sory representation of the trunk versus those for the fingers, they found the lat-

ter to have more complex cells. They reasoned that the somesthetic inputs from

receptive fields on the chest wall would constitute less of an integrative and in-

terpretive challenge to cortical neurons than those from the fingers and thus

that neurons representing the chest were less complex. Similarly, when they

compared the cells in the finger area to those in the supramarginal gyrus

(SMG), a region that is associated with higher cognitive processes, they found

the SMG neurons to be more complex. The second hypothesis was that den-

dritic trees in all regions are subject to experience-dependent change. As a re-

sult, they hypothesized that predominant life experiences (e.g. occupation)

should be reflected in the structure of dendritic trees. Although they did not

test this hypothesis directly, they did have an interesting observation. In their

study comparing cells in the trunk area, finger area, and the SMG, they found

curious individual differences. For example, especially large differences in

trunk and finger neurons were found in the brains of people who were typists,

machine operators, and appliance repairmen. In each of these, a high level of

finger dexterity maintained over long periods of time may be assumed. In con-

trast, one case with no trunk-finger difference was a salesman in whom one

would not expect a good deal of specialized finger use. These results are sug-

gestive although we would agree with Scheibel et al’s caution that “a larger

sample size and far more detailed life, occupation, leisure, and retirement his-

tories are necessary” (p. 101). The preliminary findings in this study do sug-

gest that such an investigation would be fruitful.
Finally, one can look at pathological development and see if there is a neu-

ral correlate of abnormal behavior. In one such study, Purpura (1974) exam-

ined the dendritic structure of neurons from the brains of retarded versus aver-

age intelligence children. He did not quantify the dendritic length, but he did

find marked differences in dendritic structure. The retarded children had spin-

dly dendrites that had a very much reduced spine density. This abnormal spine

BRAIN PLASTICITY AND BEHAVIOR 55

density is intriguing because it is reminiscent of the low spine density that we

have consistently observed in rats with cortical injury in what would be

equivalent to the third trimester of human development. Like retarded chil-

dren, these rats have severe behavioral deficits that render them unable to learn

cognitive tasks that are solved easily by animals with similar brain injuries

later in life (e.g. Kolb & Gibb 1991a). More recently, there have been several

studies of children with trisomic chromosomal states, such as Down’s syn-

drome and trisomy 13 (e.g. Becker et al 1986, Jay et al 1990, Marin-Padilla

1974), the general observation being that there is anomalous spine morphol-

ogy, decreased spine density, and small dendritic fields in many types of retar-

dation.

MODULATION OF EXPERIENCE-DEPENDENT CHANGE

The demonstration that dendritic and/or synaptic change is related to experi-
ence is intriguing, but it is not proof of a relationship. The critical experiments
are those in which an experimental manipulation alters the behavior and the
morphology changes in a meaningful manner. Various manipulations fit this
requirement, including especially age, sex hormones, neurotrophins, stress,
and injury.

Aging

Despite nearly a century of effort by scores of investigators, many of the basic
questions concerning changes in the aging brain are swirling in controversy
(Coleman & Flood 1987). Buell & Coleman (1979) first noted that the aged
brain showed an increase in dendritic growth that was hypothesized to com-
pensate for the loss of neurons with age. That is, their general idea is that the
number of synapses in the cortex is maintained by adding synapses to the den-
drites of the adjacent neurons in the cortex. This growth would seem to be in
accord with the general observation that most middle-aged people can be pre-
sumed to have suffered neuronal loss but do not appear demented. Further-
more, Buell & Coleman (1985) have shown that there is a failure of dendritic
growth in the demented brain. As intriguing as the relationship between aging,
dementia, and dendritic growth appears, we must caution that there nonethe-
less remains some controversy because not all brain regions appear to suffer
cell death with age (e.g. Coleman & Flood 1987).

Sex Hormones

There is accumulating evidence that the male brain and the female brain differ

in their structure and respond differently to experience. Specifically, Juraska

and her colleagues (e.g. Juraska 1984, 1986, 1990; Juraska et al 1985, 1989)

56 KOLB & WHISHAW

were the first to report that the visual cortex is more sensitive to experience in

males than it is in females. This is not a general increased sensitivity of males,

however, because they have also reported that the hippocampus is more sensi-

tive to experience in females than in males. These differences are related to the

circulating gonadal hormone and therefore can be manipulated with hormone

injections. Evidence of sex differences in cortical structure has now been

shown to occur in the prefrontal cortical regions of lab-reared rats (e.g. Kolb &

Stewart 1991, 1995), and recently it has been shown that injury to these mor-

phologically dimorphic areas produces sexually dimorphic differences in

functional recovery (Kolb & Cioe 1996).
The importance of sex hormones is not restricted to development. Stewart

& Kolb (1994) have shown that ovariectomized or gonadectomized adult rats
show significant change in cortical structure, especially in the females. Thus,
the brains of ovariectomized rats not only grew heavier, but the cortical neu-
rons showed a 25% increase in dendritic arbor and a 10% increase in spine
density. This result implies that cortical morphology is hormone-dependent
throughout the life of the animal. Because experience has sexually dimorphic
effects, it seems reasonable to suppose that changes in hormonal state, espe-
cially in postmenopausal women, will alter the brain’s response to experience.

Neurotrophins

Neurotrophins, which are chemicals known to have growth-enhancing proper-

ties in the nervous system, influence dendritic structure and also interact with

experience. It is known, for instance, that administration of nerve growth fac-

tor during adulthood increases both dendritic branching and spine density

throughout the cortex (e.g. Kolb et al 1997b). Furthermore, experience differ-

entially modulates the levels of different neurotrophins such as nerve growth

factor which, in turn, stimulates growth (e.g. Schoups et al 1995). Thus, it is

possible that one route of action of experience on the brain is to stimulate (or

inhibit?) the production of neurotrophins, and these, in turn, alter neuronal

structure. This promises to be an area of intense study in the near future.

Stress

Stress has effects on the neuroendocrine system and this, in turn, has been

shown to affect cell morphology (e.g. Sapolsky 1987, Sirevaag et al 1991,

Stewart & Kolb 1988). Most studies to date have focused on the hippocampal

formation, but there is reason to suspect that cortical neurons are also vulner-

able to the effects of stress (Stewart & Kolb 1988). There is no specific evi-

dence about whether stress interacts with experience-dependent changes in the

brain, but it seems likely.

BRAIN PLASTICITY AND BEHAVIOR 57

Injury

When the cortex is damaged there are changes in the remaining cortex that are
correlated with functional outcome. For example, when Kolb & Gibb (1991b)
removed the frontal cortex in adult rats there was an initial drop in dendritic ar-
borization near the injury. This atrophy slowly resolved and four months later
there was a significant increase in dendritic morphology, which was correlated
with partial restitution of function. In contrast, large sensorimotor cortex le-
sions lead only to neuronal atrophy and no evidence of functional recovery
(Kolb et al 1997b).

These results are reminiscent of those seen in the aging and demented brain:
When there is evidence of dendritic growth it leads to functional recovery,
whereas when there is no dendritic growth there is no recovery. This principle
can be seen even more clearly in the developing brain. One of our consistent
findings over the past decade has been that when the cortex of the developing
rat is damaged in the first few days of life, which corresponds to a time just af-
ter neural proliferation is complete but neural migration and differentiation is
still ongoing, there is a marked generalized atrophy of dendritic arborization
and a decrease in spine density in neurons throughout the cortical mantle (for a
review, see Kolb 1995). This result is correlated with a miserable functional
outcome and is reminiscent of the marked abnormalities in the brains of re-
tarded children (Purpura 1974). In contrast, when the cortical mantle of rats is
damaged in the second week of life, which corresponds to the period of rapid
dendritic growth and synaptic formation, there is a generalized enhancement
of dendritic arborization and/or spine density throughout the remaining cortex
(Kolb & Gibb 1991a). This enhanced dendritic response is correlated with dra-
matic functional recovery. Thus, we see that if the injury in the cortex leads to
increased dendritic space, there is a good functional outcome, whereas if the
injury leads to a retarded development of dendritic material, there is a poor
functional outcome. Furthermore, we have shown that with treatments that re-
verse the dendritic atrophy, such as administration of neurotrophins or housing
in enriched environments, there is functional improvement.

CONCLUSIONS

One of the most intriguing questions in behavioral neuroscience concerns the

manner in which the brain, and especially the neocortex, can modify its func-

tion throughout one’s lifetime. Taken all together, the evidence discussed

above makes a strong case for a relationship between brain plasticity and be-

havioral change. Indeed, it is now clear that experience alters the synaptic or-

ganization of the brain in species as diverse as fruit flies and humans. Evidence

that these changes are functionally meaningful is more difficult to collect, but

58 KOLB & WHISHAW

there is little doubt that changes in synaptic organization are correlated with

changes in behavior. Thus, animals with extensive dendritic growth, relative to

untreated animals, show facilitated performance on many types of behavioral

measures. In contrast, animals with atrophy in dendritic arborization show a

decline in behavioral capacity. Similarly, factors that enhance dendritic

growth (e.g. nerve growth factor) facilitate behavioral outcome, whereas fac-

tors that block dendritic growth (e.g. brain injury at birth in rats) retard func-

tional outcomes. We should emphasize that although we have stressed changes

in dendritic morphology, there are multiple, and likely dissociable, changes in

the neuron morphology that correlate with behavioral change. These include

increases in dendritic length, dendritic branching pattern, spine density, syn-

apse number, synapse size, glial size and number, and metabolic activity.
The critical question that remains is how dendritic and synaptic change is

related to behavioral change. The current evidence clearly shows that den-
drites in the cortex may show a net proliferation, regression, or stability de-
pending upon various factors that affect behavior. It seems likely that a net pro-
liferation of dendrites is a response to an increased availability of afferent sup-
ply. In contrast, the net reduction in dendrites, which is seen in response to in-
jury, for example, is likely to reflect a decline in the afferent supply to a cell. In
this view, dendrites are hypothesized to be in a state in which they are con-
stantly ready to expand or retract their territory, limited largely by the avail-
ability of afferent nourishment and by the metabolic capacities of the cell. Be-
cause changes in the dendritic length are presumed to reflect changes in synap-
tic connectivity, it follows that increased dendritic arbor reflects increased
synapse formation, whereas decreased dendritic arbor reflects decreased syn-
apse formation.

The putative increase (or decrease) in afferent supply to a neuron leads to
the question of where these afferents arise. There is scant evidence that the
adult or even the infant brain is capable of growing new projections over long
distances. Thus, it seems most likely that changes in afferent supply reflect
changes in axonal arborizations of relatively nearby neighbors. A recent analy-
sis by Nicoll & Blakemore (1993) is instructive. They examined the patterns of
connections of pyramidal cells, which are the almost exclusive outputs of the
the neocortex. The axons of pyramidal cells make long-range connections to
other cortical regions or subcortical structures, but they also have axon collat-
erals that form extensive arborizations with nearby cells. In fact, the most com-
mon target of pyramidal cells is other cortical pyramidal cells. Nicoll & Blake-
more estimated that roughly 70% of the excitatory synapses on any layer II/III
pyramidal cell are derived from pyramidal cells in the near vicinity. One way
for the functioning of an intrinsic circuit to change is for the field of influence
of a neuron to change. For example, the diameter of a cell’s dendritic field

BRAIN PLASTICITY AND BEHAVIOR 59

could expand, allowing the cell to interact with a larger number of neurons. Al-
ternatively, the axon terminal could redistribute to enlarge the field of influ-
ence, too. The fact that neurons can expand their field of influence means that
if neurons die, remaining ones could enlarge their field to make up for some of
the lost processing power.

It is assumed in our model that increasing the connectivity of pyramidal
cells will increase their functional capacity. But why should this be? Hebb
(1949) proposed the idea of cell assemblies in which networks of cortical neu-
rons were seen as being responsible for mental activity. A key component of
this model is that individual neurons have little role in behavioral control, but
rather behavior is dependent upon networks of neurons. Furthermore, Hebb
noted that a given neuron could participate in multiple networks, each with a
different function. Calvin (1996) likened this to the person who is on multiple
committees, each with a different function. Thus, if neurons have more con-
nections, they are hypothesized to have more influence on the observed behav-
ior.

A critical feature of this view is that afferent supply influences neuronal
function. We are now left with the question of what controls afferent supply.
Various factors, such as neurotrophins, can influence this supply, but they
must do it through some clear mechanism. One likely candidate is gene expres-
sion. There is ample evidence that the expression of genes in the mature brain
is influenced by environmental and behavioral events (e.g. Dudai 1989). Gene
expression thus provides a mechanism whereby cells can synthesize new pro-
teins needed to form more synapses. Studies in various species, especially
Aplysia, have shown that blockade of protein synthesis blocks long-lasting
changes in both synapses and behavior (Bailey & Kandel 1993). The most
likely mechanism for increased gene activity is neuronal activity, which is
stimulated by behavior and experience. Activity initiated by experience or be-
havior could therefore increase the activity of genetic mechanisms responsible
for dendritic and synaptic growth and, ultimately, behavioral change.

Visit the Annual Reviews home page at

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60 KOLB & WHISHAW

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