Figure 1. Working model of the neural
systems that underlie adaptive navigation (colors =
The ability to accurately
navigate one’s environment is essential for many aspects of individual
and species survival, from the procurement of food and water to the
selection of mates and locating safe habitats. Consequently, it might
be expected that the fundamental neural mechanisms underlying adaptive
navigation have been highly conserved across the evolution of vertebrate
animals (Mizumori, Canfield, & Yeshenko, 2005). This possibility is supported by a
wide range of empirical data showing that particular brain structures
(e.g., the hippocampus, striatum, or thalamus) appear to make comparable
functional contributions to spatial orientation or spatial learning by a
variety of animal species (Cain & Malwal, 2002; Healy, 1998; Jeffery, 2003; Krushinskaya,
1966). Thus, efforts to understand the
functional organization amongst brain systems during rodent navigation
may provide a good model for discovering fundamental principles of
neural systems interactions in other species, including humans.
In order to
study the neural mechanisms of experience-dependent navigation, it is
useful to identify the likely essential elements of this complex
behavior (Mizumori, Cooper, Leutgeb, & Pratt, 2000a). Functional elements that can be
considered essential to adaptive navigation include the evaluation of
external and internal sensory information, the integration of this
sensory evaluation with past knowledge, the modulation of such spatial
mnemonic integration by internal state information (such as motivation,
stress, and hormone status) and the determination of the appropriate
behavioral output. The evolutionary success of this rather complex
navigational system may relate to the fact that each of the above
essential functions can be accomplished in different ways. For example,
multiple types of sensory information can be analyzed in parallel,
thereby allowing animals to use different kinds of environmental
information in an opportunistic and adaptive manner (Cain & Malwal,
2002; Healy, 1998; Jeffery, 2003; Krushinskaya, 1966) when environmental or
task conditions change. This allows animals to readily switch to
alternate sensory modalities to guide accurate navigation with minimal
disruption (see Bingman, this volume). As another example of the flexible operation of a
functional element of the navigational system, response selection neural
circuitry may engage different behaviors or behavioral strategies
depending upon task requirements.
behavioral evidence demonstrated a crucial role for the limbic system
(especially the hippocampus) in adaptive navigation. For example, hippocampal lesions consistently produce spatial learning deficits in
rodents and monkeys, while nonspatial forms of learning remain intact
(O'Keefe & Conway, 1978). More recent neurophysiological evidence
supports the view that the essential functions underlying adaptive
navigation require specific patterns of neural activation across a broad
network of brain structures that extends beyond the hippocampus (see
Figure 1). Therefore, in what follows, our focus on the neural
mechanisms of rodent navigation begins with a discussion of hippocampal
contributions, then a description of the broader neural circuitry
underlying adaptive navigation. We also include a demonstration of
spatial representation by fish telencephalon to demonstrate that the
mechanisms underlying rodent navigation may reflect evolutionarily
Figure 2. Hippocampal circuitry of spatial processing.
first evidence for navigation-related neural representations came in the
early 1970’s with the descriptions of location-specific firing by
hippocampal pyramidal neurons (O'Keefe & Dostrovsky, 1971; Ranck,
1973, Figures 2 and 3). These cells, referred to as place cells, showed
dramatically elevated discharge rates when a rat passed through
circumscribed locations, or place fields, in a fixed environment.
Studies of the properties of hippocampal place fields have provided new
insights relevant to the issue of the integration of internal and
external sensory information, as well as to the issue of how past
knowledge impacts sensory processing that defines the current spatial
context. These studies will be reviewed in
Part II: Spatial Context-Dependent Coding by
Hippocampal Place Cells. Part
III Modulatory Influences on Spatial Mnemonic Processing
describes mechanisms by which hippocampal spatial context analysis may
be modulated by internal state information.
Part IV: Spatial Context and Internal State Information Impact Ongoing Behaviors will discuss
possible ways in which behavioral output may be influenced by the neural
processing of spatial context and internal state information. The
results of this neural systems analysis of rodent navigation have
revealed new fundamental principles of operation relevant to the issue
of how neural systems of the brain mediate complex forms of learning.
These insights are discussed in the final section.
discovery of place cells sparked intense electrophysiological
investigations of spatial navigation. Consequently, this area of
research has become one of the most prolific fields of study in the
cognitive and behavioral neurosciences. It is now commonly reported in
the literature that, depending upon the behavioral task, a majority of
hippocampal pyramidal neurons recorded in a given test session will
exhibit a place field. These place fields are considered to be quite
reliable in that the same field is consistently demonstrated by a cell
when it is recorded across multiple sessions within a constant
environment. A population of place cells, then, could serve to generate
a mental representation of the spatial layout of an environment, known
as a cognitive map (O'Keefe & Conway, 1978; Tolman, 1948). Place
fields have been recorded in different species, including the rat
mouse, and most recently, the fish (Figure 3B;
Canfield & Mizumori, 2004). Extensive work has been published on the sensory
and mnemonic properties of hippocampal place fields, as well as their
molecular and neurocomputational foundations (for reviews, see Best & White, 1999;
Best, White, & Minai, 2001; Blum & Abbott, 1996; Hasselmo,
Hay, Ilyn, & Gorchetchnikov, 2002;
Jeffery, 2003; Lorincz & Buzsaki, 2000; McNaughton et al., 1996; Muller & Stead,
1996; O'Mara, 1995; Poucet, Save, & Lenck-Santini, 2000; Redish, 2001; Sharp, 2002).
Place fields are
controlled by visual information.
Because hippocampal place cells have been so extensively
studied, a great deal is known about the factors that govern their
firing. For example, the firing of place cells is not controlled by
magnetic north or any other geomagnetic referent. Rather, place cells
fire when subjects occupy a particular location relative to the
arrangement of objects within an environment (i.e., allocentric space).
This is perhaps best illustrated in experiments in which rats are
trained to retrieve rewards from particular locations in an environment
that contains a number of distinct visual cues distributed around the
periphery (e.g., Lenck-Santini, Save, & Poucet, 2001; O'Keefe &
Conway, 1978). The rat is then removed from
the environment and the distal cues are rotated (e.g., 90 degrees
clockwise). Remarkably, when the rat is returned to the rotated
environment, the place fields are shifted 90 degrees in the same
direction and rats approach a location shifted 90 degrees clockwise from
the original position. Experiments such as these illustrate the
powerful control distal visual objects exert over place cell firing.
fields recorded from intact rats are less likely to be controlled by
local intramaze cues. For example, place fields remain fixed with
respect to distal cues when the maze, and any intramaze cues, are
rotated (Miller & Best, 1980; Olton, Branch, & Best, 1978). Consistent with
this, rats need to see the distal cues of a familiar environment to
reproduce the previously seen place fields. Place fields may disappear
or shift to new locations if rats are brought into the room in darkness
(Mizumori, Ragozzino, Cooper, & Leutgeb, 1999b; Quirk, Muller, & Kubie,
Place cells also
encode self-motion information.
Although visual input provides strong control over place fields,
this control does not appear exclusive. That is, it has been
demonstrated that place fields can remain stable without visual input.
Although rats need to see distal cues upon initial entry into a familiar
environment to generate the same place fields observed in prior
recording sessions, place fields remain stable if the lights are
extinguished after the rats have seen the environment (Markus, Barnes,
McNaughton, Gladden, & Skaggs, 1994; Muller, Kubie, & Saypoff, 1991; Quirk et al., 1990). Blind and deaf
rats exhibit place fields (Hill & Best, 1981; Save, Cressant, Thinus-Blanc,
& Poucet, 1998).
These results have led to the suggestion that the hippocampus may
play a role in calculating the present position by keeping track of the
previous movements through the environment, a process called path
integration or dead reckoning (McNaughton et al., 1996; Mittelstaedt & Mittelstaedt, 1982;
Whishaw & Gorny, 1999). The present location
can be calculated by accounting for velocity and direction of movement,
as well as the distance traveled from a known start position. Indeed,
studies have shown that subjects are able to navigate using path
integration, and that the navigational systems of the brain receive
self-motion information such as proprioception (limb position),
vestibular input and motor efference copy (for review, see Etienne &
of the directional and angular velocity information needed for path
integration appears to be encoded in the anterior thalamus, subiculum,
retrosplenial cortex (Mizumori & Williams, 1993; Taube & Muller,
1998; Sharp, Blair, & Cho, 2001). This circuitry is anatomically connected
with the hippocampus and may provide self motion information to hippocampal neurons (Leutgeb et al., 2000). Indeed, the firing of many
place cells depends on the rat’s velocity and direction of movement
through the place field (McNaughton, Barnes, & O'Keefe, 1983; Mizumori,
McNaughton, Barnes, & Fox, 1989b). The firing patterns of inhibitory interneurons of the
hippocampus, commonly referred to as theta cells because many of them
fire synchronously with the theta rhythm, are also sensitive to the
velocity and acceleration of translational movements by the animal
(Buzsaki, 2002; Fox & Ranck, 1981; Ranck, 1973; Vanderwolf, 1969).
The use of external sensory information and path
integration are not mutually exclusive. Without external sensory
information, small errors accumulate over time and positional
information derived by path integration alone becomes increasingly
erroneous as subjects move through their environment. Consistent
with this idea, spatial coding including location and directional
information becomes disrupted over time in the absence of external
sensory information, such as when the room lights are extinguished.
Interestingly, the spatial coding of place cells and cells that code
directional heading ( so called head direction cells) is restored when
subjects are allowed to view cues that can be used to determine the
current location and orientation, suggesting that landmarks are used
periodically to update path integration information. Thus, both
idiothetic and sensory information contribute to efficient navigation
(Etienne & Jeffery, 2004; Knierim, Kudrimoti, & McNaughton, 1998;
McNaughton et al., 1996; also see Brown, this volume). In spite of these
findings, the hippocampal role in path integration remains uncertain.
Whereas some studies have found hippocampal lesions impair path
integration (Maaswinkel, Jarrard, & Whishaw, 1999; Whishaw, McKenna, &
Maaswinkel, 1997; Whishaw, Hines, & Wallace, 2001), other studies have found evidence for only an indirect hippocampal involvement in path integration (Alyan & McNaughton, 1999;
Save, Guazzelli, & Poucet, 2001).
Sensory encoding by
place cells is guided by experience.
Memory processes appear to exert some degree of
control over the sensory coding by place cells. As mentioned
above, place fields persist in darkness after a rat views a familiar
et al., 1999b; Quirk et al., 1990). In contrast, the place fields were disrupted when rats
were introduced into the same environment in darkness. This pattern of
responses suggests that the fields can be maintained by mnemonic
representation of the visual environment rather than local intramaze
cues. Also consistent with the view that memory guides sensory
processing by place cells, place fields can remain stable after some or
all of the cues have been removed (e.g., Markus et al., 1994; Mizumori et
al., 1999b; O'Keefe & Conway, 1978).
Other studies have also shown that experience can
alter place cell firing (Lever, Wills, Caccucci, Burgess, & O’Keefe,
2002; Shapiro & Eichenbaum, 1999; Wilson & McNaughton,
1993). For example, hippocampal neurons showed an
experience-dependent expansion of their place fields with repeated
passes through an environment (Mehta, Barnes, & McNaughton, 1997). Also, the location
of place fields can change with learning. For example, shifting the
location where rewards could be obtained to a new location in an
environment was associated with a migration of the place fields toward
the new goal location (Breese et al., 1989). Other studies have found
similar experience-dependent migration of place fields with changes in
goal locations (Hollup, Molden, Donnett, Moser, & Moser, 2001;
Kobayashi, Tran, Nishijo, Ono, & Matsumoto, 2003; Markus et al., 1995). The influence of prior experience on place fields
illustrates the convergence of navigation studies with the well
documented role of the hippocampus in learning and memory processes
(Cohen & Eichenbaum, 1994; Eichenbaum & Cohen, 2001; Scoville & Milner,
1957; Squire & Zola-Morgan, 1991).
cells encode more than visual spatial information.
Despite the remarkable correlation between neuronal firing and
processing relevant to spatial navigation, it has become increasingly
apparent that hippocampal function is not limited to spatial
navigation. For example, hippocampal neurons exhibit robust responses
to non-spatial stimuli such as tones, odors, and other task relevant
stimuli (Eichenbaum, Kuperstein, Fagan, & Nagode, 1987; Freeman,
Cuppernell, Flannery, & Gabriel, 1996; Kang &
Gabriel, 1998). Within the hippocampus, the phenomenon of representational
reorganization, often referred to as remapping, is often observed
following spatial and nonspatial experimental manipulations.
When subjects are placed in a novel environment, stable place fields
exhibited in a previous environment suddenly shift to new and
unpredictable (but stable) locations (Muller & Kubie, 1987). That
is, the pattern of spatial representation across populations of cells
undergoes reorganization. This phenomenon was originally labeled
remapping because it appeared as though subjects were generating a new
map for the new environment (Bostock, Muller, & Kubie, 1991; Kubie & Muller, 1991; Muller & Kubie, 1987; Wilson & McNaughton, 1993). However,
this representational reorganization can occur when the sensory
environment has not changed and there is no obvious need for a new
environment-based map. For example, Skaggs and McNaughton (1998) found
that rats generated two different representations for separate, but
visually identical environments. Representational reorganization can
also be induced within a single test session by a change in behavioral
requirements (Markus et al., 1995), strategy (Yeshenko, Guazzelli, &
reward location (Smith & Mizumori, 2006), and even past or future
behavioral trajectories (Ferbinteanu & Shapiro, 2003; Wood,
Dudchenko, Robitsek, & Eichenbaum, 2000).
fields represent spatial context.
notion of a hippocampal role in context processing is supported by an
extensive literature indicating that hippocampal damage renders subjects
insensitive to background contextual stimuli present in a conditioning
environment (for reviews, see Anagnostaras, Gale, & Fanselow, 2001; Maren, 2001; Myers & Gluck, 1994). For example, subjects with hippocampal damage
do not exhibit conditioned fear responses to contextual stimuli (e.g.,
Kim & Fanselow, 1992; Phillips & LeDoux, 1994). Moreover, when the
contextual stimuli (e.g., odor, illumination, or visual background)
are altered, intact subjects show a decline in conditioned responding,
while subjects with hippocampal lesions exhibit no such decline.
Instead, they continue responding as if the context had not changed
(Freeman, Weible, Rossi, & Gabriel, 1997; Holt & Maren, 1999; Penick & Solomon, 1991).
Historically, ‘context’ has been thought of as the continuously present
background stimuli present in any learning situation. However, several
recent studies have shown that factors other than these background cues
can influence place fields. For example, many hippocampal neurons
exhibited dramatically different place fields when rats shifted from a
random foraging task to a guided foraging task in the same environment
(Markus et al., 1995). Hippocampal neurons have also been shown to
respond differentially depending on past or future behavioral responses
(Ferbinteanu & Shapiro, 2003; Wood et al., 2000). In these studies,
rats were trained on a T-maze task. Remarkably, the same neurons
exhibited different place fields, depending on which goal arm the rat
was about to approach or which arm the rat previously had visited.
Thus, the firing patterns underwent representational reorganization even
though the physical environment had not changed. Findings such as these
suggest that, as far as hippocampal coding is concerned, a ‘context’ can
incorporate not only the physical environment, but also the cognitive
and behavioral features of a given situation.
The finding that some of the neuronal firing
patterns cannot be attributed to the spatial properties of the
environment per se suggests that hippocampal function is not limited to
spatial processing. Consistent with the suggestions of others
(e.g., Nadel, Willner, & Kurz, 1985), it is hypothesized here that the hippocampus
importantly contributes to a broader conceptualization of context
processing. However, spatial knowledge may be a critical element
of context processing since the spatial layout of an environment is an
essential defining feature of any context; it may provide the framework
within which detailed spatial and nonspatial information is processed.
the spatial referent is provided to the hippocampus via entorhinal cortical
‘grid cells.’ These cells show multiple place fields that appear as
vertices of a tessellating triangular patterned array (Hafting, Fyhn,
Molden, Moser, & Moser, 2005). The hippocampus, then, can be thought of as processing
spatial context information during navigation (Mizumori et al., 1999b; Nadel et al.,
Different places are frequently associated with
different behaviors (e.g., foraging or predator avoidance). Therefore, it is particularly important to be able to determine that a
given sensory environment is the appropriate context for a particular
set of behaviors, whereas a different environment may be the appropriate
context for different behaviors. Consistent with this idea is the
fact that hippocampal neurons generate a new firing pattern (i.e.,
a new representational organization) when subjects encounter a new
environment or when the behavioral demands (e.g., strategy, motivational
state or reward location) change within the same environment.
Recent studies in our laboratory have provided
considerable support for the idea that cognitive and behavioral features
of a situation shape the representation organization of hippocampal
neurons. In one study, rats were trained on a T-maze to use a
place strategy (i.e., go to a particular location) during the first part
of a training session and they were trained to use a response strategy
(e.g., turn right) during the second part of the session (Yeshenko et
al., 2001). The place fields of many hippocampal neurons shifted
to new locations when the rat switched strategies. This
reorganization occurred even when the rat was engaged in the identical
sequences of motor behaviors in the ‘place’ and
‘response’ trials (see Figure 4). Thus, hippocampal processing was not
modulated by the subjects’ behavior per se. Rather, it was modulated by
the subjects’ application of a particular behavioral strategy.
Another recent study found that the same kind of representational
reorganization could occur across spatial contexts differentiated only
by the locations where rewards could be found (Smith
5). In this study rats were trained to go to one location on a plus
maze for a reward during the first half of a training session and to a
different location during the second half of the session. As was the
case in the previous study, the physical environment and the behavioral
responses (turn right, go straight, turn left) were similar in the two
session halves (i.e., contexts). Additionally, the appropriate
behavioral strategy (i.e., to approach a given location for reward) was
the same. Again, hippocampal neurons exhibited markedly different place
fields depending on the spatial context. In this case, the spatial
contexts were distinguished by the rats’ knowledge of where rewards
could be obtained.
context-dependent movement and other nonspatial firing.
movement-related firing of hippocampal neurons has been interpreted as
suggesting a hippocampal role in path integration (see above). However,
the fact that hippocampal lesions have not consistently yielded
impairments in path integration (Alyan & McNaughton, 1999) indicates
that the role of the hippocampus in path integration remains uncertain.
One possibility is that, as was the case with spatial firing, the
movement-related firing of hippocampal neurons may reflect context
processing. For example, neuronal firing may be positively correlated
with the subjects’ velocity in one situation (i.e., during the use of a
response strategy) but uncorrelated or negatively correlated with
velocity in another situation (i.e., during the use of a place strategy
in the same physical environment; Figure 6). Importantly, in studies such
as this one, the critical comparison takes place when the same rat is
exhibiting the same pattern of locomotion. In this way, differential
behavioral activity cannot account for the changed fields. An
intriguing interpretation of this finding is that the hippocampal
movement-sensitive coding does not reflect ongoing behavior per se, but
it may include the learned behaviors (e.g., movements) relevant to a
given spatial context.
The hippocampal role
in evaluation of the spatial context.
Figure 6. Egocentric neural codes may reflect learned
behavioral responses. A. Reward-related firing
before (left) and after (right) context change.
B. Velocity and acceleration
correlates vary with spatial context (Hippocampus and
findings discussed thus far indicate that neural coding within
the hippocampus involves more that just the spatial layout of the
environment: the spatial firing properties of hippocampal neurons
undergo reorganization when the behavioral or strategic requirements
change. We have also found that neuronal responses to the reward and
other task relevant stimuli depend on the context (Smith
Such spatial context-dependent coding may provide a means of binding
together a location with the events, behaviors, and strategies that are
relevant to that location. This kind of binding together of items,
events, and places as part of an episodic memory has been proposed as a
key feature of hippocampal function (Aggleton & Brown, 1999; Cohen & Eichenbaum, 1994;
Eichenbaum & Cohen, 2001).
involvement of the hippocampus in navigation, memory and context
processing suggests that a critical function of the hippocampus may be
the ongoing evaluation of the spatial context. It is suggested here
that a new hippocampal context code is generated whenever subjects are
exposed to a novel situation. With experience, the context code
could become associated with the relevant memories, behaviors, and
strategies that are appropriate for that context. When subjects are reintroduced
to the environment, the context code is reactivated and the subject is
able to retrieve the appropriate information and behaviors.
Reactivation of a learned spatial context code each time the subject
enters a familiar environment would serve to further strengthen the
connections, presumably in the neocortex, that represent long term
memory of that context. Relatively small changes in the environment
lead to partial reorganization of the hippocampal neural representations
in that only a portion of the cells recorded show responses to the
experimental manipulation (Knierim et al., 1998). However, when key
aspects of the context change, such as the appropriate strategy or the
reward locations, massive reorganization of the hippocampal code occurs
and an entirely new code is generated. In effect, the hippocampus
treats the altered situation as a new context. Perhaps hippocampus
compares the learned code with the reorganized pattern to determine the
extent to which a spatial context has changed (Mizumori et al., 1999b).
A detected change could lead to the modification or updating of the
cortical long term memory representations. This form of context coding
would provide a flexible means of distinguishing the myriad of
situations one could experience and then efficiently retrieving the
information relevant to each one.
An animal's interpretation of its current
internal and external sensory environments depends not only on how it
interacts with or behaves in the environment, but also on the current
motivational state. When one is hungry, preferential attention
will be directed towards cues and behaviors that might otherwise be
ignored if one is in the same environment but is searching for an escape
route. Traditionally, the effects of motivational states on
behavior have been studied either by considering the consequences of
varying hunger or thirst, or by studying the effects of appetitive or
(e.g., most recently, Kennedy & Shapiro, 2004). The hypothalamus has long been
considered central to the regulation of homeostatic systems such as
hunger and thirst. The amygdala-prefrontal cortical circuitry has been
strongly implicated in learning the association between specific
environmental cues and their reinforcement consequences (Alheid, de
Olmos, & Beltramino, 1995), as has been the striatal-prefrontal circuit (Cardinal,
Winstanley, Robbins, & Everitt, 2004).
of hippocampal processing.
Figure 7. Septal regulation of hippocampal neural representation
substantial evidence that several subcortical structures exert powerful
control over the excitability of limbic system neurons. The traditional
interpretation of these subcortical influences has been that they
somehow gate, or filter, cortical information arriving in the hippocampus
(e.g., Winson, 1984). The gating hypothesis is supported by findings
that electrical stimulation of numerous subcortical structures
facilitates synaptic transmission through the hippocampus (Alvarez-Leefmans
& Gardiner-Medwin, 1975; Assaf & Miller, 1978; Bilkey &
Goddard, 1985; Dahl & Winson, 1985; Mizumori, McNaughton, & Barnes,
1979). Of these, the medial septum appears to be strategically
located to provide the navigation circuit with information concerning
the animal’s motivational state since the septum receives direct input
from hypothalamic nuclei (Jakab & Leranth, 1995; Swanson & Cowan,
and projects directly to the hippocampus (Figure 7). The hippocampal effects
(see below) are mediated by powerful GABAergic and cholinergic septal
afferents onto both pyramidal and nonpyramidal neurons within multiple
subregions of the hippocampus (Freund & Antal, 1988; Freund & Buzsaki,
1996; Risold & Swanson, 1995).
Disruption of septal function, either by permanent lesions or reversible
inactivation, impairs hippocampal-dependent learning (Harzi & Jarrard, 1992; Mizumori et al., 1989a; Winson, 1978) and the patterned
activity of hippocampal neurons. In an intact, behaving animal,
recordings of the hippocampal EEG show a rhythmic oscillation around the
theta frequency (about 7-9 Hz). Compromising the integrity of the
medial septum significantly attenuates the hippocampal theta rhythm
(Mizumori et al., 1989a; Winson, 1978). Studies that record hippocampal
single neuron activity during active navigation show that septal lesions
or reversible inactivation prevent hippocampal place fields from
responding appropriately to changing environments (Ikonen, McMahan,
Gallagher, Eichenbaum, & Tanila, 2002; Leutgeb & Mizumori, 1999; Mizumori et al.,
1989a). Other evidence
shows that cholinergic input (presumably from the septum) significantly
modulates hippocampal long-term potentiation (LTP; a synaptic model of
plasticity). Also, it appears that there is an increase in
acetylcholine release during new learning (Gold,
2003; Ragozzino, Pal, Unick, Stefani, & Gold, 1998). Together, these data indicate that the septum is in a key
position to regulate hippocampal processing of cortical (i.e., sensory
and/or mnemonic) information as well as the efficiency of
intrahippocampal network functions.
theoretical interpretation of the septal influence is that it identifies
for hippocampus the appropriate internal state (or motivational state;
Mizumori et al., 2000a). Relevant spatial context information could
arrive in septum via the hippocampal CA3 efferent projection system
(Fig. 7). Indeed, it has been shown that the lateral septum (the main
cortical input area of the septal region) contains neurons whose firing
is correlated with the location of animals within their environment
(Leutgeb & Mizumori, 1999; Zhou, Tamura, Kuriwaki, & Ono, 1999). That is, lateral septal neurons show place fields and, similar to hippocampal place
fields, they respond to changes in the spatial context. Our current
working hypothesis (Mizumori et al., 2000a) is that the lateral septum,
via its extensive projection to various hypothalamic and mammillary
nuclei, informs the motivational system of the brain about the current
spatial context. In doing so, it may bias the firing properties of
hypothalamic neurons to reflect the appropriate motivational state.
Such a bias, in turn, could dramatically alter hypothalamic influences
over efferent structures, such as the medial septum. Changes in medial
septal activity, in turn, could have consequences for hippocampal neural
plasticity. As a result, the hypothalamo-septal informational system
could be thought of as reflecting the motivational perspective within
which hippocampus should interpret sensory information. Effectively,
such an operation may disambiguate, or selectively filter, current
sensory input according to the current motivational state. Consistent
with this view, it has been shown recently that the motivational state
of an animal can importantly impact hippocampal-dependent learning
(Kennedy & Shapiro, 2004).
modulation of hippocampal processing.
The effects of motivational states on limbic
function have also been studied in terms of the role of reinforcement or
incentive values of sensory cues in learning. Basolateral amygdala
lesions impair fear conditioning (Kapp, Frysinger, Gallagher, & Haselton,
1979; LeDoux, Cicchetti, Xagoraris, & Romanski, 1990), second-order conditioning (Hatfield,
Han, Conley, Gallagher, & Holland, 1996), and conditioned place preferences (McDonald & White,
1993). The amygdala, then, may contribute to adaptive
navigation by contributing knowledge about the incentive value of reward
(Pratt & Mizumori, 1998). To test this hypothesis, rats were trained to
discriminate locations on a maze that predicted the presence of large or
small amounts of reward (chocolate milk). Some basolateral amygdala
neurons showed elevated or reduced firing rates in anticipation of
encounters with a large reward, while others showed elevated or reduced
firing in anticipation of small amounts of reward. Control procedures
demonstrated that movement or gustatory aspects of reward consumption
could not account for the anticipatory firing. Given that the
basolateral amygdala receives hippocampal information via the subiculum
and entorhinal cortex, structures that show rather diffuse spatial codes
(Mizumori, Ward, & Lavoie, 1992; Quirk, Muller, Kubie, & Ranck, 1992; Sharp & Green, 1994), it
is likely that the amygdala does not receive a precise spatial context
code. Also, since amygdala lesions do not result in consistent spatial
learning deficits (Kesner & Williams, 1995; McDonald & White, 1993),
the contribution of the amygdala may be conditional depending on the
extent to which distinguishing incentive values is a salient feature of
prefrontal cortex may make a more direct contribution than the amygdala
toward the evaluation of incentive values within a given spatial
context. Permanent prefrontal cortical lesions in rats result in
reliable deficits on tasks that require the flexible use of location
information (Gemmell & O'Mara, 1999; Grannon, Save, Buhot, & Poucet,
1996; Poucet &
Herrmann, 1990). Also, reversible inactivation of the prefrontal cortex
impairs spatial working memory (Ragozzino et al.,
1998; Seamans, Floresco, & Phillips, 1995). Attempts to identify the neural codes of
the prefrontal cortex have
led to the surprising finding of a paucity of spatial representation (Jung,
Qin, McNaughton, Barnes, 1998; Poucet,
1997; Pratt & Mizumori, 2001). This
result was unexpected because of the reported effects of prefrontal
cortical lesions and because of the known direct connection from the
CA1 region of the hippocampus to the prefrontal cortex (Jay & Witter, 1991).
Instead, the most consistent behavioral correlate identified for
prefrontal cortex neurons was reward-related discharge. Similar to amygdala
neurons, prefrontal neurons changed firing rates in anticipation of
encounters with rewards of different magnitudes (Pratt & Mizumori, 2001). The combined results of the lesion and neural
recording studies are consistent with the view that prefrontal cortex
may provide a prospective representation of the incentive value
associated with different locations in a context-specific manner. This
function contrasts slightly with that of the amygdala, which is
considered to associate the incentive values with specific cues in a
context-independent way. The prospective coding by prefrontal cortex
neurons may impact neural response patterns in efferent structures, such
as the striatum and motor cortex, structures traditionally thought to
control ongoing voluntary movements.
memory-guided evaluation of internal and external sensory information,
and its subsequent modulation by internal state information, must
ultimately come to impact processes involved in the evaluation of the
consequences of behavior, and the selection of future responses. The
latter two processes may exist as a functional unit since it is highly
adaptive to be able to quickly modify one’s action depending on the
consequences of a previous act. The striatum and frontal cortex may
work in concert for this purpose. It has been suggested that the
striatum generates signals that allow animals to assess the extent to
which behavioral errors are made. Such signals could be used by
to modify ongoing behaviors, or select new behaviors, as needed (Houk,
1995; Schultz, 2002; Schultz, Tremblay, & Hollerman, 2003).
Striatal evaluation of
specific reference to the case of adaptive navigation, Mizumori, Pratt,
& Ragozzino (1999a), Mizumori et al. (2001), and Mizumori, Ragozzino, &
Cooper (2000b) proposed that the striatum compares the extent to
which the outcome of a recent behavioral response is consistent with
that expected based on past experience within the same spatial context.
The striatum may send different signals to the cortex depending upon whether
a match or mismatch to the expected reinforcement outcome is detected.
Such a ‘response-reference system’ should be useful for many forms of
learning. The response-reference system interpretation of striatal
function appears consistent with the findings of lesion studies and the
pattern of striatal afferent and efferent connections.
studies show an important role for the striatum in adaptive navigation.
Striatal lesions have been shown to produce selective spatial deficits
especially during new learning (Annett, McGregor, & Robbins, 1989;
Floresco, Seamans, & Phillips, 1996; Gal, Joel, Gusak, Feldon, & Weiner,
1997; Ploeger, Spruijt, & Cools, 1994). There may be
specialization for spatial processing within specific regions of the
striatum, such as the ventral and the dorsomedial striatum. In
contrast, the lateral striatum may selectively contribute to a different
sort of learning, one that occurs more slowly (e.g., stimulus-response or
response-response learning, e.g., Devan & White, 1999). The
anatomical separation of different learning functions is likely related
to the different patterns of afferent and efferent connections found in
these areas. For example, the ventral and dorsomedial striatum receive
extensive convergent input from multiple sensory and association areas
of the neocortex and the limbic system (McGeorge & Faull, 1989). The
lateral striatum, in contrast, has a distinct pattern of connections
with sensory and motor areas of the neocortex (Flaherty & Graybiel,
1993). Computationally speaking, this topographically constrained
pattern of input into lateral striatum places a restriction on the
number of combinatorial patterns that can be produced, resulting in
well-defined stimulus-response relationships. In contrast, the highly
convergent pattern of input to ventral and dorsomedial striatum endows
these regions with tremendous combinatorial power to produce the high
degree of flexible (or contextual) processing needed to evaluate the
reinforcement outcomes of ever changing spatial contexts (Mizumori et
there is a clear and distinct topographical organization to the striatal
afferent patterns, the intrastriatal computations appear relatively
consistent across the structure. That is, the distribution of striatal
GABAergic medium spiny projection neurons and cholinergic interneurons
is rather homogeneous across the striatum. Further, these medium spiny
neurons similarly possess a bistable membrane property that permits
selective filtering of incoming information (Stern, Jaeger, & Wilson,
1998; Wilson, 1995). The synaptic efficiency of the spiny neurons is modulated by
dopamine signals that are thought to reflect current reinforcement
conditions (Houk, 1995; Schultz, Apicella, Romo, & Scarnati, 1995;
Schultz, Dayan, & Montague, 1997).
Thus, the different regions within the striatum may perform similar
response-reference system computations on distinct types of information.
Neurophysiological data are also consistent with the response-reference
theory of striatal function. In behaving animals, dorsal and ventral
striatal neurons exhibit changes in firing relative to specific
egocentric movements (e.g., right turns or forward movement), reward
acquisition, as well as an animal’s location and directional heading
within an environment (Lavoie & Mizumori, 1994; Mizumori
et al., 1999a, 2000b; Wiener, 1993). The striatal place fields appear similar to
hippocampal place fields (when tested in a familiar environment) in
terms of field reliability and firing rates. However, striatal place fields tend
to be larger than hippocampal fields. In addition to place
cells, another type of spatial correlate of striatal cells is reflected
by increased firing (by as much as ten times the baseline rate) when a
rat’s head is aligned with certain orientations in space. The preferred
orientation of these ‘head direction cells’ remains constant regardless
of the location of the animal in a fixed environment (for an example,
see Figure 8). Striatal head direction cells show many of the same
features as place cells in that the preferred orientations (e.g., north)
can be shown to shift by an amount that corresponds with a shift in the
visual environment, or the preference can shift randomly if the visual
cues are changed sufficiently (Mizumori et al., 2000b). Thus, these
cells are thought to signal context-dependent directional orientation,
and not orientation relative to geomagnetic conditions or to a specific
visual cue. One mechanism by which dopamine may contribute to
reinforcement coding by striatum is to stabilize striatal neural
representations. Consistent with this hypothesis, the stability of the
directional selectivity of the head direction signal has been shown to
be disrupted following injection of a dopamine receptor antagonist (Figure
8). All of the behaviorally correlated striatal neuron types exhibit
partial reorganization after a change in spatial context. That is, only
a portion of the movement-correlated cells, for example, show a change in the
movement correlate after the context change. In sum, then, the
particular combination of neural representations found in striatum
(e.g., movement, reward, place, or orientation), and the finding that all of
these representation types appear context-dependent (Mizumori et al.,
1999a, 2000b; Yeshenko, Guazzelli, & Mizumori, 2004) and exhibit partial reorganization
were consistent with the response reference theory.
The PrCM of frontal
cortex may direct hippocampal effects on ongoing behavior.
evaluation of reinforcement outcome by the striatum is critically important
for modifying future behaviors. The circuitry involved in the latter
function likely involves neocortical operations that ultimately impact
the output of the primary motor cortex. Recently, efforts have focused on
arguably one of the more direct routes whereby limbic output may become
integrated with the basal ganglia (i.e., striatal) and movement control areas
of the cortex (Mizumori, Pratt, Cooper, & Guazzelli, 2002; Mizumori,
Puryear, Gill, & Guazzelli, 2004a). This route
extends from the hippocampus, to a posterior region of the cortex (the retrosplenial cortex), and then to the medial precentral cortex (Figure 1;
variously referred to as PrCM, FR 2 or AGm, van Groen & Wyss, 1990;
Reep, Goodwin, & Corwin, 1990; Swanson & Kohler, 1986; Vogt & Miller, 1983; Zilles & Wree, 1995). From the PrCM, information can move directly to
the primary motor cortex (or FR1, Donoghue & Parham, 1983; Reep, Corwin,
& Hashimoto, 1997; Zilles & Wree,
1995). The PrCM also projects directly to the dorsal
striatum (Reep, Corwin, Hashimoto, & Watson, 1984; Reep & Corwin, 1999;
Sesack, Deutch, Roth, & Bunney, 1989; Zheng
& Wilson, 2002). Thus, the PrCM appears to be strategically
located to contribute to the integration of the basal ganglia and frontal
cortical movement control systems in a hippocampal (or
investigation of the relevance of PrCM function to experience-dependent
navigation included characterizing the behavioral correlates of PrCM
neurons in rats performing a spatial maze task, testing the spatial
context-dependency of these neural representations, and then testing
whether PrCM neural representations are affected by the removal of
limbic input from the retrosplenial cortex. It was found that the PrCM contains neural codes for
directional heading, a variety of egocentric movements, and different
reward conditions. These codes were demonstrated to be
context-sensitive in that the behavioral correlates were significantly
altered by changes in the visual spatial environment. For example, head
direction cells appeared to shift directional preferences, or lose their
directional firing property altogether, when the lights were turned off
during the performance of a familiar spatial working memory task.
Comparable alterations in firing correlates were observed for egocentric
and reward PrCM neurons after changes in the visual environment. Thus,
PrCM neural representations appear to be sensitive to the same kind of
context information as hippocampal neural representations. Finally, a
significant number of PrCM head direction and egocentric movement cells
showed dramatic changes in their behavioral correlates after the
retrosplenial cortex was temporarily inactivated by microinjection of a
local anesthetic (tetracaine). It appears, then, that even though the
PrCM can be defined anatomically as a motor system structure, it is
functionally connected with the hippocampal system. As such, it may
play a pivotal role in mediating hippocampal effects on ongoing
interpretation of PrCM function is that it contributes to the
determination of future behaviors, perhaps by computing response
intentions, in a context-dependent way. The fact that the PrCM is rather
uniquely situated to pass on movement intention information to both
the basal ganglia and motor cortex suggests that it is in a position to gate
relevant information depending on whether or not the spatial context has
changed. If a context change has been detected by the hippocampus, there
may be preferential output by the PrCM to the striatum that facilitates the
evaluation of new reinforcement contingencies before behaviors are
changed. If the context is unchanged, then direct messages from
the PrCM to
the motor cortex could result in rapid response outcomes; such a gating
mechanism may contribute to the development of automatic responses in
study of adaptive navigation from a neural systems perspective has
provided much evidence to support the view that this complex behavior
involves the integration of numerous fundamental processes, the first of
which allows for the detection of changes in the expected sensory
environment. This information may interact with, or be regulated by,
other processes that reflect the internal (or motivational) state. Such
evaluation of sensory and motivational states are likely guided by one’s
past experience, and they involve hippocampal-cortical and
septal-hippocampal interactions. Animals must also continuously
evaluate the consequences of their behaviors in order to select
appropriate future behavioral responses. The striatal-frontal cortex
circuit is proposed to serve this function. When functioning optimally,
then, the coordinated actions of these different neural circuits
continuously determine the appropriate navigational behaviors in ever
Several patterns of neural responsiveness become clear upon
consideration of the vast amount of neurophysiological data accumulated
during the many investigations of adaptive navigation. Each pattern
provides new insight into the fundamental nature of the processing
within and between neural systems. For example, representation of
egocentric movement was the most common correlate type found across the
different neural systems studied. Also, within each neural system,
movement-correlated cells represent one of the largest categories of
functionally correlated cells. Different interpretations could be
offered to account for the parallel coding of egocentric movement. One
possibility is that information about the current behavioral state needs
to be incorporated by the local neurocomputational architecture. In
this way, the behaviors relevant to a particular association
(stimulus-stimulus, or stimulus-reward) or a specific stimulus can be
encoded. Another possibility relates to the finding that hippocampal
and striatal egocentric movement correlates often change if the expected
spatial context is changed. This result suggests that the
egocentric code may also reflect a learned association between expected
contextual information and the relevant behavior. The term
‘behavior’ here refers not only to the broad category of behavior
exhibited (e.g., a turn correlate), but also to the details of the
behavior such as the velocity and acceleration of the actual movement. Such an
integrated representation could be useful to provide information to the
local computational network about the expected behavioral context of a
task, a variable known to impact movement-related responses of parietal
cortex neurons in primates (Colby & Goldberg, 1999). The fact that many
brain structures contain such movement codes that are sensitive to
changes in the sensory environment suggests that the behavioral context
(i.e., the behaviors appropriate to a given situation) in which learning
occurs is a fundamental unit of information that is useful for multiple
forms of learning. Also, the broad presence of behavioral context
information may provide one (of many?) functional architectures through
which different neural processing systems can be orchestrated. If the
behavioral context changes (resulting in altered firing patterns of the
context-sensitive movement cells), information is fed back to a neural
network that represents a functional architecture, or global domain, of
behavioral expression (Figure 9). The behavioral expression domain, then,
refers to a processing network that is responsible for behavioral
selection, planning of actions, and the memory of behavioral acts (Mizumori,
Yeshenko, Gill, & Davis, 2004b). Frontal and parietal cortices are likely to be
centrally involved in the operations of this functional domain.
Feedback indicating a change in behavioral context may cause the neural
activity landscape within the behavioral expression domain to
reconfigure, which in turn provides adaptive feedback that updates
movement-sensitive representations in multiple neural systems.
Theoretical model of neural systems interactions.
Similar to the operation of the behavioral expression functional domain,
we postulate the existence of a distributed network that corresponds to
the functional domain of spatial context memory (perhaps involving
parietal and temporal cortices; Figure 9). This network may serve to
coordinate spatial context codes within different neural systems, such
as the hippocampus and the striatum. That is, the spatial context memory
network could define, for different neural systems, an expectation of
sensory, behavioral, and reward elements of a learning situation. As
noted above, this information could be used in different ways to support
local network functions. Feedback to the spatial context memory network
from individual neural systems may be required to update memory as the
learning situation changes. The consequence of such updates may in turn
update memory representations within other functional domains such as
the behavioral expression system. There may be other functional domains
that interact with the spatial context and behavioral control domains.
Another common observation across different categories of correlated
neurons that were recorded within different neural systems is that
changes in context produced only partial reorganization of firing
patterns. That is, only a portion of place, movement, and
reward-related cells responded to context change. If we assume that
context-independent firing reflects expected information based on past
experience (e.g., expected spatial contexts, learned responses, or
reinforcement outcomes), and if we assume that context-dependent neural
codes reflect ongoing features of a current situation, then a
fundamental operating principle that applies to diverse neural systems
could be the engagement in error-driven (match-mismatch) computations.
Such a conclusion is consistent with the prediction of computational
models of striatal and hippocampal function (e.g., Houk, 1995). Such
computations would be highly adaptive for they provide a mechanism by
which past experience can impact the processing of different forms of
Finally, it is noted that not only are similar representation types
found in different neural systems during the performance of a single
task (and presumably during learning), but similar representations are
found across different learning situations. This suggests that
different neural systems continuously engage in their distinct
learning-related computations, regardless of task demands. Their
relative influences on behavioral expression systems may vary depending
upon a number of factors such as experience and motivation. A challenge
for this field is to determine more specifically how neural systems
appear to compete for control over behavioral expression systems, and
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Over the years, many students have made significant
contributions to the generation of data and to the
development of key concepts described in this chapter. In
particular, we would like to thank many of the past graduate
students and postdocs in the lab: Brent Cooper, Katy Gill,
Alex Guazzelli, Stefan Leutgeb, Wayne Pratt, Corey Puryear,
Kay Ragozzino, and Oxana Yeshenko. Also, a large number of
talented undergraduate students provided invaluable
assistance with this work. An NIMH grant (#58755 to S. J.
Y. Mizumori) supported our work over many years.
©2006 All copyrights for the individual chapters are retained by the
authors. All other material in this book is copyrighted by the
editor, unless noted otherwise. If there has been an error with
regards to unacknowledged copyrighted material, please contact the
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