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Feature Article
Distributed Modular Architectures
Linking Basal Ganglia, Cerebellum, and
Cerebral Cortex: Their Role in Planning
and Controlling Action
The motor system includes structures distributed widely through the
CNS. and in this feature article we present a scheme for how they
might cooperate in the control of action. Distributed modules, which
constitute the basic building blocks of our model, include recurrent
loops connecting distant brain structures, as well as local circuitry
that modulates loop activity. We consider interconnections among the
basal ganglia, cerebellum, and cerebral cortex and the specialized
properties of certain cell types within each of those structures. namely. striatal spiny neurons, cerebellar Purkinje cells, and neocortical
pyramidal cells. In our model, striatal spiny neurons of the basal ganglia function in contextual pattern recognition under the training influence of reinforcement signals transmitted in dopamine fibers. Cerebellar Purkinje cells also function in pattern recognition, in their
case to select and execute actions through training supervised by
climbing fibers, which signal discoordination. Neocortical pyramidal
cells perform collective computations learned through a localtraining
mechanism and also function as information stores for other modular
operations. We discuss how distributed modules might function in a
parallel. cooperative manner to plan, modulate, and execute action.
Modularity has emerged as an important architectural principle of vertebrate nervous systems, one probably reflecting
both its evolutionary history and development (Rakic, 1988).
The CNS has enlarged dramatically since vertebrate brains
first evolved, and replication of existing modules, subject to
subsequent phylogenetic and ontogenetic modification, represents a common and convenient mechanism for expansion
and elaboration in biological systems. Columns in the neocortex (Mountcastle, 1978), parasagittal strips in the cerebellar cortex (Oscarsson, 1980; Voogd and Bigare, 1980), and
striosomes of the striatum (Graybiel, 1991) epitomize some
of the many forms modularity assumes in the mammalian
CNS. To that list one might add the barrels, blobs, bands, islands, pools, clusters, and aggregates of many kinds scattered
throughout the vertebrate brain.
Neurobiologists typically consider modular organization in
the context of local circuits and circumscribed brain regions.
However, principles of modularity may also apply to the organization of the neural networks that link physically separated regions of the CNS. This conjecture receives support
from neuroanatomical studies that demonstrate a high degree
of topographic specificity in the projection pathways linking
different regions of the cerebral cortex, thalamus, basal ganglia, and cerebellum (e.g., Asanuma et al., 1983; Goldman-Rakic, 1984, 1988a,b; Ito, 1984; Alexander et al., 1986; Gibson et
al., 1987; Shinoda and Kakei, 1989; Hoover and Strick, 1993).
Motivated by these and other examples of anatomical specificity in projection pathways, we and others have been exploring an extension of the concept of modularity-from
modules restricted to a single neuronal structure to those that
may be distributed over a number of distant, but functionally
cooperative, structures (Goldman-Rakic, 1988a; Selemon and
Goldrnan-Rakic, 1988; Houk, 1989; Houk et al., 1990; Eisenman
et al., 1991; Houk and Barto, 1992; Berthier et al., 1993; Houk
and Wise, 1993; Wise and Houk, 1994). Specificity in these
James
c. Houk! and Steven P. Wise 2
1 Department of Physiology, Northwestern University
Medical School, Chicago, Illinois 60611 and 2 Laboratory of
Neurophysiology, National Institute of Mental Health,
Poolesville, Maryland 20837
long-distance connections may create modules with the interesting property of being anatomically distributed while being united in some emergent function. We term such
cooperatively interacting subunits distributed modules to reinforce the idea that they include disparately located neuronal
structures connected through their projection neurons.
The concept of distributed modules may assist understanding the cooperative function of the diverse "motor control"
structures and the computational architectures that subserve
adaptive action. In venturing to build on the work of many
others who have considered the cooperative operations of
motor control structures (e.g., Kornhiiber, 1971; Allen and
Tsukahara, 1974; Arbib, 1987; Kawato et al., 1987; Selemon
and Goldman-Rakic, 1988; Grossberg and Kuperstein, 1989),
we have proposed certain general information processing
functions for three kinds of distributed modules: (1) corticalbasal ganglionic modules linking cerebral cortex, thalamus,
and basal ganglia; (2) cortical-cerebellar modules linking
parts of the cortex, pons, thalamus, and cerebellum; and (3)
cortical-cortical and their associated cortical-thalamic modules linking columns of cerebral cortex and thalamus through
reciprocal connections (Houk and Wise, 1993; Wise and Houk,
1994). We begin our discussion by considering certain cellular
specializations in each of these three types of modules.
Cellular Specializations for Information Processing
Neurons have impressive morphological and physiological
specializations (Llinas, 1988; Shepherd, 1990), which probably
reflect their distinct information processing capabilities. Although we cannot review this topic in detail, it will be important to highlight a few prominent cellular specializations
as they relate to the information processing modules discussed in this article.
Purkinje and Striatal Spiny CeUs
The classic theoretical work on pattern classification networks (Nilsson, 1965) and perceptrons (Rosenblatt, 1962;
Minsky and Papert, 1969) employed threshold logic units.
These artificial neurons had (1) high convergence ratios of
diverse inputs onto each output neuron, (2) specialized training signals for the efficient adjustment of the large number
of weights associated with these inputs, and (3) sharp thresholds between on- and off-states of the output neurons. The
latter property ensured clean "decision surfaces" for detecting
the presence or absence of the input patterns to which the
units were tuned. While pattern classifiers constructed from
such simple threshold logic units have definite limitations
(Minsky and Papert, 1969), they nevertheless demonstrate remarkable recognition capabilities. Purkinje cells in the cerebellar cortex and striatal spiny neurons in the basal ganglia
have several unique structural and functional properties that
appear to be analogous to threshold logic units.
First, like threshold logic units, Purkinje and striatal spiny
neurons have high convergence ratios. This characteristic results from their extensive dendritic trees, numerous spinous
Cerebral Cortex Mar/Apr 1995;2:95-110; 1047-3211/95/$4.00
synapses, and a pattern of innervation favoring the convergence of diverse afferents onto individual neurons. Each Purkinje cell is contacted by approximately 200,000 different
parallel fibers (Ito, 1984), and each spiny neuron is contacted
by about 10,000 different corticostriatal afferents (Wilson,
1995). Those are the highest convergence ratios known in
the nervous system.
Second, Purkinje and spiny neurons receive specialized inputs that appear to transmit training information for guiding
the adjustment of synaptic weights. While many types of neuron exhibit synaptic plasticity, in most cases it appears to be
controlled by a Hebbian-like mechanism that depends mainly
on local presynaptic-postsynaptic correlation. As discussed
later, a Hebbian mechanism functions well for some types of
learning, such as the unsupervised learning problems confronted by sensory systems. By itself, however, it is inadequate
for training motor systems to interact with the environment
in useful ways. In these cases, training information that reports on the success or failure of interactions with the environment is also required, and learning is most efficient when
this information is specific. In the cerebellar cortex, climbing
fibers are highly specific and appear to transmit training information resembling a punishment signal reporting on failures in movement coordination (Ito, 1984; Houk and Barto,
1992). In the basal ganglia, dopamine fibers appear to provide
training information that signals predictions of reinforcement
(Wickens, 1990; Ljungberg et aI., 1991, 1992; Schultz et aI.,
1993; Houk et aI., 1995). This input is topographically organized (Graybiel, 1991; Gerfen, 1992); however, the specificity
is probably not as pronounced as that for climbing fibers.
Third, an unusually high density of voltage-dependent ion
channels render the electrical responses of Purkinje and
spiny neurons exceptional. In particular, persistent calcium
current in Purkinje cell dendrites supports plateau potentials
that have sharp thresholds for state transitions (Llinas and
Sugamori, 1980; Ekerot, 1984). This nonlinear dynamical feature is thought to convert dendrites into bistable elements
and the neurons as a whole into multistable input-output
units (Houk et al., 1990; Yuen et aI., 1992). The discrete
thresholds for plateau potentials are analogous to the sharp
decision surfaces of threshold logic units. Similarly, the sharp
division between "up" and "down" states exhibited by spiny
neurons (Wilson, 1990) would provide sharp decision surfaces for these neurons in pattern recognition tasks.
Cortical Pyramidal Cells
Pyramidal neurons of the cerebral cortex also have several
important specializations in morphology and physiology, ones
that differ from those of Purkinje and spiny neurons. First,
pyramidal neurons have combinations of ion channels that
promote graded frequencies of firing in response to graded
strengths of input (McCormick et aI., 1985), in contrast to the
dual-state behavior characteristic of striatal spiny neurons and
Purkinje cell dendrites. Processing units with graded outputs
are clearly advantageous when the results of a neural computation need to be expressed over a range of magnitudes, as
contrasted with the "yes" or "no" values required of threshold
logic units. Second, intra-axonal staining (Gilbert and Wiesel,
1983; De Felipe et aI., 1986; Shinoda and Kakei, 1989) and other methods (e.g., Goldman and Nauta, 1977;Jones et aI., 1978;
Goldman-Rakic, 1988a,b; Huntley and Jones, 1991) have
shown that individual fibers from the thalamus or cortex form
clusters of synapses, which suggests that individual afferents
could make a variable number of synapses on a given pyramidal neuron. This would extend the range of input weights.
Third, instead of specific training signals, pyramidal neurons
appear to receive several diffuse inputs that may provide
global mechanisms for nonspecifically modulating synaptic
96 Distributed Modules in Motor Control' Houk and Wise
plasticity and learning (Bassant et aI., 1990; Waterhouse et al.,
1990; Murphy et aI., 1993). At the cortical level learning appears to be guided mainly by local correlations between preand postsynaptic activity. Local learning rules are useful in
unsupervised learning tasks, which can be effective in organizing sensory representations into topographic and cognitive
(e.g., spatial) maps (Barlow, 1989; Reiter and Striker, 1989;
Bear et al., 1990; Schlagger et aI., 1993) or in establishing automatic responses such as conditioned reflexes (Houk and
Barto, 1992). For example, if a neuron already receives a
strong, previously established connection (analogous to an
unconditioned stimulus) capable of forcing the cell to respond in an appropriate manner, this strong input can then
train other inputs by "example." In this manner, a neuron can
select from a set of weak inputs those pertinent to the task
at hand. Later, we suggest that this type of learning may occur
in the frontal cortex where it could be guided by examplesetting, forcing inputs transmitted from the basal ganglia and
cerebellum.
Cortical Basal-Ganglionic Modules
Context Recognition by Spiny Neurons
In our model, the emergent functions of cortical-basal ganglionic modules stem from the capacity of their striatal spiny
neurons for pattern recognition. In the cortical-basal ganglionic module illustrated in Figure lA, note the convergence
of input from several cortical columns (C for cortical columns
generally, F for a frontal column) onto a cluster of striatal
spiny neurons (SP). This feature is well suited for contextual
pattern recognition (Houk and Wise, 1993; Wise and Houk,
1994). We postulate that, through the mediation of reinforcement training signals provided by the dopaminergic cells of
the midbrain, this neuronal architecture learns to recognize
and register complex contextual patterns that are relevant to
behavior. This contextual information includes the state of the
organism, the desirability of an action, the actions planned in
the near future, the location of targets of action, and sensory
inputs suitable for either selecting or triggering motor programs.
Convergence onto Striatal Spiny Cells
The convergent input upon striatal spiny cells from diverse
regions of the cerebral cortex is processed and fed back, via
the pallidum (P) and thalamus (T), to the frontal cortex (Fig.
1A; Alexander et aI., 1986, 1990). The precise extent of cortic os triatal convergence may vary substantially. Parthasarathy
et al, (1992) have reported evidence that the terminal fields
of projections from the frontal eye field (FEF) and the supplementary eye field (SEF) to the striatum overlap almost exactly. Similarly, Flaharty and Graybiel (1991) have shown that
parts of the somatosensory cortex and the primary motor
cortex send converging inputs to striatal targets, potentially
onto individual spiny cells. In contrast, Selemon and GoldmanRakic (1985) found that whereas prefrontal and posterior parietal inputs may project to the same general sector of the
striatum (Yeterian and Van Hoesen, 1978), they interdigitate
at the local level rather than overlap. It is possible that both
interdigitation and frank overlap can be found among corticostriatal projections, and that both provide for a diversity of
inputs to the striatal spiny cells that they contact.
Cortical-Basal Ganglionic Modules as
Context Detectors
Some of the signals that cortical-basal ganglionic modules
might support are illustrated in Figure lB. The present
scheme is based on Chevalier and Deniau (1990). We further
suggest that the spiny neuron burst illustrated in trace SP
Figure 1. A, A cortical-basal ganglionic
module, with an embedded cortical-thalamic module. C, cortical column composed ofcorticocortical, corticothalamic,
and other projection neurons; F, a cortical column inthe frontal cortex; Sp' spiny
striatal neuron; ST, subthalamic nucleus
neuron; P, pallidothalamic neuron; T, thalamocortical neuron. B, Activation patterns of some of the elements in a cortical-basal ganglionic module. Traces SP,
P, and T-F represent the discharge rate
as a function of time for striatal spiny
neurons, pallidothalamic cells, and frontal cortical-thalamic modules, respectively. For traces P and T-F, solid lines
indicate signals transmitted through the
direct pathway in A and stippled lines
indicate signals transmitted through the
ST sideloop.
A
context
registration
B
SP
context detected
p
pallidal discharge patterns
_nL_"__
reflects the neuronal "recognition" of the context to which
that spiny neuron is tuned. Spiny neurons inhibit pallidal neurons (cell P in Fig. lA), so their bursts are believed to produce
pauses in pallidothalamic activity (solid trace P in Fig. IB)
(DeLong and Georgopoulos, 1981; Tremblay and Filion, 1989;
Chevalier and Deniau, 1990). Pallidothalamic cells are also inhibitory, and one would expect their pause in discharge to
initiate a brief burst of thalamic discharge, due to the inhibitory rebound properties of these neurons (Wang et aI., 1991).
This rebound burst might then initiate positive feedback in
the reciprocal cortical-thalamic loop shown in Figure lA
(Houk and Wise, 1993), assuming that the corticothalamic
projection has a net excitatory influence (Ghosh et aI., 1994).
Once initiated, positive feedback could be self-sustaining and
thus could continue after pallidal discharge resumed its tonic
rate. Sustained cortical-thalamic loop feedback (solid trace TF in Fig. IB) would register that a relevant context recently
occurred, thus serving as a working memory (Fuster and Alexander, 1973; Goldman-Rakic and Friedman, 1991) of the
contextual information encoded by the striatal spiny neuron.
This model bears important similarities to Hikosaka's (1989)
views on dynamic interactions in the eye-movement control
system.
Strictly cortical mechanisms, in addition to thalamic disinhibition, undoubtedly contribute to the regulation of loop activity. For example, cortical inputs could initiate and/or help
sustain positive feedback in the cortical-thalamic modules.
Consistent with this idea, cortical rather than pallidal afferents
Cerebral Cortex Mar/Apr 1995, V 5 N 2 97
have been implicated as causal factors in the premovement
activation of thalamic neurons that receive both inputs (Anderson and Turner, 1991). The pallidothalamic segment of a
cortical-basal ganglionic module would, on this view, be ideally situated to modulate discharge in cortical-thalamic
modules (Chevalier and Deniau, 1990), and would do so on
the basis of contexts recognized by the striatal spiny cells.
Some behavioral neurophysiology of the basal ganglia appears to support the proposal that it plays a role in context
recognition and registration. Nearly all of the investigators
who have studied striatal or pallidal modulation in awake,
behaving monkeys have emphasized its context dependency
(Alexander, 1987; Hikosaka et al., 1989b; Alexander and
Crutcher, 1990; Schultz and Romo, 1992). Context-dependent
neuronal activity, in this sense, indicates discharge modulations that do not obligatorily follow sensory events or accompany specific motor acts, but rather do so only under certain
circumstances. Context-dependent striatal modulation both
follows sensory signals and accompanies movements (Rolls et
al., 1983; Schultz and Romo, 1988, 1992; Hikosaka et al., 1989a;
Romo and Schultz, 1992). Kimura (1992, p 212) concluded
that "most of the sensory responsive putamen neurons [81 %]
showed clear differences in the responses to identical stimuli
... presented with [in] different behavioral contextjs},' This
finding includes the majority of spiny cells in the putamen
(Kimura et al., 1990; see also Kimura, 1992), as well as the
tonically active cells that are likely interneurons (Kimura et
al., 1984; Apicella et al., 1991). Interestingly, this poststimulus
activity does not depend dramatically on movement parameters, including the direction of movement. In contrast to this
apparently sensory activity, which follows stimuli, many striatal modulations precede movements. This "movement-related" activity may reflect movement parameters (DeLong and
Georgopoulos, 1981), but it also depends upon the context
in which an action occurs (Kimura, 1990; Gardiner and Nelson, 1992). Again quoting Kimura (1990, p 1295), a fundamental "characteristic of ... movement-related cell activity
[is] dependence on the conditions in which movement occurs. . . ." Brotchie et al. (1991) stress a similar opinion concerning the pallidal cells that receive striatal input. Context
dependency in striatal and pallidal activity accords with the
idea that striatal spiny neurons act as pattern detectors.
Context Recognition and Mutual Inhibition
Our notion of context recognition resembles Shallice's (1988)
ideas concerning "contention scheduling." In that scheme,
neuronal assemblies, activated by a pattern of sensory and
other inputs, form mutually inhibitory networks that constitute a "winner-take-all" system. The module most activated
will influence the action with which it is associated. Frith
(1992) has previously suggested the basal ganglia as the site
of contention scheduling. This hypothesis accords well with
the elaborate system of spiny neuron GABAergic collaterals
(Wilson and Groves, 1980; Groves, 1983; Aronin et al., 1986;
Kawaguchi et al., 1990) postulated to form competitive networks within inhibitory domains (Alexander and Wickens,
1993). Feedforward inhibition through local GABAergic interneurons might also contribute to these operations (pennartz
and Kitai, 1991).
Through these inhibitory mechanisms, one can anticipate
a high degree of mutual exclusion among cortical-basal ganglionic modules, which would result in a sparse coding of
salient context. Thus, any given context would tend to be
recognized by a relatively small number of modules, although
they might share many inputs. This concept has some important consequences when combined with the knowledge that
different populations of striatal neurons may originate the "direct" and "indirect" pathways (Gerfen et al., 1990; Gerfen,
98 Distributed Modules in Motor Control' Houk and Wise
1992; Flaharty and Graybiel, 1993). The direct pathways inhibit the pallidothalamic output cell P in Figure lA, whereas
the indirect pathway relays inhibitory inputs from SP neurons
to a part of the globus pallidus (not shown) that in turn inhibits the excitatory ST input to P neurons (see Hazrati and
Parent, 1992). The net effect is thus disinhibitory, as indicated
by the open arrow in Figure lA. In our scheme, spiny neurons
of the direct pathway can be tuned to contexts for action,
whereas nearby spiny neurons associated with the indirect
pathway would respond to contexts for withholding action.
As shown by the stippled traces in Figure IB, activity in the
indirect pathway would enhance P discharge, rather than suppressing it, which could counteract context registration promoted by a direct pathway or, as illustrated in Figure IB,
could negate a previously registered context. In accord with
this idea, some of the putamen neurons appear to be more
associated with the withholding, rather than the execution,
of movements (Hikosaka and Wurtz, 1983: Schultz and Romo,
1992).
Function and Dysfunction of Cortical-Basal
Ganglionic Modules
Our suggestion that striatal spiny neurons function in context
recognition appears to contrast with prevailing theories
about the role of the basal ganglia in motor control. Traditionally, the basal ganglia have been thought of as modulators
of movement that release motor responses through a disinhibitory mechanism (Marsden, 1982; Hikosaka and Wurtz,
1983; Albin et al., 1989; DeLong, 1990). By contrast, it has been
proposed that the role of the basal ganglia involves preventing maintained motor activities, including normal posture
(Mink and Thach, 1991), from interfering with shifts in limb
position. The dual pathways for enabling and withholding
movements, the direct and indirect pathways mentioned
above, may help to reconcile these apparently contradictory
views. Since context is clearly important in determining
whether to enable or suppress an action, both of these functions would fit well with the idea of context detection promoted here. Thus, suppression of postures (Mink and Thach,
1991) can be viewed as a function of recognizing a context
for withholding a rigid postural command and conveying that
recognition to the thalamus.
Although we recognize the speculative nature of the assignment of a context recognition role to the basal ganglia,
that idea accords with current models of its pathophysiology
(Hallett, 1993). Ablating or inactivating the globus pallidus
does not lead to paralysis. Movements can still be selected
and executed in an appropriate context (Horak and Anderson,
1984; Mink and Thach, 1991). Not only can the motor system
still select the correct action, but it does so without a dramatic increase in reaction time. This fact appears to contradict the view that the basal ganglia serve as context recognizers for action. If the system cannot recognize the context
for action, one might ask, how can it act at all? However, these
findings are not surprising if one views the pallidal output as
playing a modulatory or gating role (Bullock and Grossberg,
1988), biasing the motor system toward greater or lesser activation based on the prevailing context. This idea is consistent with the hypothesis that bradykinesia in Parkinson's disease reflects scaling deficiencies (Hallett and Khoshbin, 1980;
Berardelli et al., 1986; Hallett, 1993). Normally, the recognition
of a context for action would permit continuation of that
action and, possibly, its amplification. In Parkinson's disease, a
failure to recognize an appropriate context would lead to the
relative suppression of ongoing action.
Also of interest are two alternative kinds of failures of striatal context recognition: failure to detect a context that
should suppress action or falsely signaling a context for action
when one has not occurred. Such failures should lead to behavior occurring outside of an appropriate context, and it has
been noted that basal ganglia dysfunction could lead to a
wide variety of such behavior (Hallett, 1993), including the
tics of Tourrette's syndrome (Petersen et aI., 1993; Singer et
aI., 1993), involuntary movements such as ballismus and chorea (Page et aI., 1993), derangement of volitional movement
(athetosis), and the repetitive, uncontrollable thoughts or actions that characterize obsessive-compulsive disorder (Wise
and Rapoport, 1988).
In considering basal ganglia function and dysfunction, it is
also helpful to distinguish between the gating of immediate
movements, as implied in most of the above examples, and
the planning of future actions. A planning function for the
cortical-basal ganglionic module is inherent in our suggestion
that cortical-thalamic loops retain information about which
contexts have been recognized by striatal spiny neurons.
From a different perspective, there has been a recent emphasis on cognitive functions of the basal ganglia, focusing on a
concept related to planning termed attentional set. Several
studies of cognitive deficits in Parkinson's disease have
stressed these patients' difficulty in shifting set (e.g., Flowers
and Robertson, 1985; Brown and Marsden, 1988; Owen et aI.,
1993; see also Passingham, 1993). While it is known that there
is substantial dopamine input to neocortex in primates, it remains plausible to assume that deficits in Parkinson's disease
reflect striatal dysfunction. On that assumption, the Parkinsonian's difficulty in shifting set, or planning, is also consistent
with the idea that context detection is a fundamental striatal
function. Failure to detect a context for implementing a plan
would be analogous with failure to detect a context for action. Perhaps the descending pathways of the basal ganglia
are primarily involved in gating functions, whereas the ascending pathways to the frontal cortex, especially those to
the prefrontal areas, are more important in planning on the
basis of recognized context.
Cortical-Cerebellar Modules
Recognition ofAction Goals by Purkinje Cells
The emergent functions in our model of cortical-cerebellar
modules (Fig. 2A) derive from an ability of Purkinje (and basket) cells to recognize patterns that lead to the selection and
execution of actions (Berthier et aI., 1993) and from the propensity of positive feedback between the motor cortex and
cerebellum to serve as a driving force for generating movement commands (Houk et aI., 1993). As discussed in the previous section, Purkinje neurons have cellular properties that
make them ideal pattern classifiers. We posit that climbing
fibers train Purkinje cells to recognize complex input patterns
that signify when the goal of an action is about to be
achieved. Purkinje cell firing then inhibits cortical-cerebellar
loop activity, terminating the movement command to achieve
an accurate movement.
Convergence onto Purkinje and Basket Cells
As with the cortical-basal ganglionic modules, the convergence of diverse inputs is important in the operation of cortica1-cerebellar modules. In this case, parallel fiber (PF) afferents,
shown in Figure 2A, converge with ratios up to 200,000: 1
onto Purkinje cells (PCs) and with lesser ratios onto basket
cells (BCs). Parallel fibers arise from granule cells, which receive mossy-fiber inputs from a variety of sources (not
shown). One source, the corticopontocerebellar projections,
is similar to the corticostriatal projections in that they originate from much of the cerebral cortex (Brodal, 1987; Stein
and Glickstein, 1992). Cortical columns originating these fibers are labeled "C" in Figure 2A, except for the motor col-
umn (M). As with the corticostriatal system, there is substantial convergence from these corticopontine inputs. Also like
the corticostriatal system, a certain degree of segregation of
inputs can also be found (Bjaalie and Brodal, 1989), and various combinations of frank overlap and interdigitation probably exist.
Cortical-Cerebellar Modules as Selectors and
Executors of Action
The core of the cortical-cerebellar module is a recurrent loop
through the frontal cortex (Houk, 1989: Houk et aI., 1993).
This loop courses from one of the divisions of motor cortex
(M in Fig. 2A), via the pons (not shown), to cerebellar nucleus
(N) as a mossy fiber collateral (Shinoda et aI., 1992), and returns to motor cortex though the thalamic (T) link of a cortical-thalamic module. We envision that the cortical-cerebellar
network is actually composed of many positive-feedback
loops (Houk et aI., 1993), and when regenerative activity is
distributed throughout the network, this instantiates the motor act. Regenerative activity can be triggered by excitatory
inputs to any component or combination of components of
the feedback loops. The modules effect their control of limb
movement through the corticospinal and rubrospinal pathways.
While superficially similar, the feedback loops involving
the cortical-cerebellar and cortical-basal ganglionic modules
differ in important ways. Cortical-cerebellar loops involve predominantly excitatory transmission at each stage in the loop,
which should function well in transmitting graded signals to
command graded actions. The disinhibitory loops inherent in
the cortical-basal ganglionic modules may, instead, function in
a bistable manner and be better suited for transmitting discrete pattern-recognition signals. In cortical-basal ganglionic
modules, the pattern-recognizing elements are embedded in
the main recurrent loop. In contrast, the parts of the corticalcerebellar module that operate as pattern recognizers, Purkinje and basket cells, are in a separate loop of the module. Since
Purkinje cells inhibit nuclear cells (Fig. 2A), Purkinje cell discharge serves to regulate the main loop's activity.
Following the work of Marr (1969) and Albus (1971), Houk
and Barto (1992) postulated that the special neuronal architecture of the cerebellar cortex is used to recognize complex
states that are critical for the selection and control of actions,
as well as for translation of this state information into spatiotemporal patterns of motor discharge appropriate for commanding precise movements. Information about the state of
the organism and its environment is conveyed to Purkinje and
basket cells by parallel fibers. The parallel fiber system, in
turn, receives its information from ascending sensory, reticulocerebellar, and corticopontocerebellar pathways through
their mossy fiber inputs. The Purkinje and basket cells learn
how to respond to these inputs as a result of training signals
in climbing fibers that emanate from the inferior olivary complex.
The view that climbing fibers act as detectors of discoordination stems from an analysis of the physiological properties of olivary neurons (Houk and Barto, 1992; also see Kawato and Gomi, 1992). In our model, climbing fibers train
cortical-cerebellar modules to move the limb from a starting
position to a selected target or endpoint in space, the latter
comprising the goal that Purkinje cells have learned to recognize. Since the modules are intrinsically capable of generating motor patterns that command straight trajectories from
an arbitrary starting position to a selected goal (Berthier et
aI., 1993), there is no fundamental requirement for the higher
stage of trajectory formation that is assumed in most alternative models of limb control (e.g., Kawato et aI., 1987; Miall
et aI., 1993). However, our model does require the assistance
Cerebral Cortex Mar/Apr 1995. V 5 N 2 99
Figure 2. A, A cortical-cerebellar module, with an embedded cortical·thalamic
module, in the format of Figure lA. C,
cortical column; M, motor cortical column; T, thalamocortical neuron; N, deep
cerebellar nuclear neuron; BC, basket
cell; PC, Purkinje cell; PFs, parallel fibers.
B, Activation patterns of some ofthe slements in a cortical-cerebellar module,
inthe format ofFigure 1B. Traces BC, PC,
and N·T·M represent signals in basket
cells, Purkinje cells, and cortical-cerebellar loops, respectively. For traces PC
and N- T-M, the solid lines illustrate modules that are disinhibited by basket cell
firing, thus supporting an action command. In contrast, thestippled lines illustrate modules with suppressed command output due to strong Purkinje cell
activity.
A
action
command
C
PFs
B
BC
n
selection signal
:
PC
programming
and terminating
action
I
N-T-M
if'
instruction
of reprogramming signals from a higher neural stage (presumably the premotor cortex) whenever an indirect trajectory to
a target is desired, as, for example, when the movement has
to avoid an obstacle. These properties accord with the finding
that premotor lesions that spare primary motor cortex do not
100 Distributed Modules in Motor Control> Houk and Wise
action
command
;\,
..
:
\
J
\
if'
trigger
interfere with ordinary reaching, while rendering an animal
incapable of moving its limb around an obstacle to grasp an
object (Moll and Kuypers, 1977).
Figure 2B shows how signals for controlling a direct trajectory might be generated in a cortical-cerebellar module.
Although scant evidence exists for the activity patterns of
some elements, such as the basket cells, we are postulating
that a population of basket cells is tuned to respond (trace
BC in Fig. 2B) to a pattern of input states that determine,
along with parallel fiber inputs, which Purkinje cells will be
inhibited (solid trace PC) or excited (stippled trace) prior to
the start of a movement. The change in Purkinje cell state
caused by the selection signal from the basket cells and parallel fibers constitutes the programming of an action in our
scheme. The bistability of Purkinje dendrites could function
as a temporary memory of this selection, spanning the time
interval shown in Figure 2B between an instruction cue that
selects a motor program and a trigger cue that initiates loop
activity comprising the action command (trace N-T-M). Note
that if each of the major dendrites of a Purkinje cell were
bistable, the cell as a whole could express multiple stable
firing rates, thus incorporating both increases and decreases
in discharge as illustrated by trace PC in Figure 2B.
According to our model, Purkinje cells active at the beginning of the action command will inhibit the cortical-cerebellar loops at selected sites in the cerebellar nuclei, thus determining the precise spatiotemporal motor pattern established
in the cortical-cerebellar network. Since several hundred Purkinje cells converge upon each nuclear cell, if relatively few
of the Purkinje cells discharge at the beginning of movement,
a relatively large signal can emanate from the associated loop,
which presumably leads to a larger and/or faster movement.
Consistent with this prediction, the discharge of task-related
cerebellar nuclear cells correlates with the velocity and duration of the movement (van Kan et aI., 1993a). PCs show
both increases and decreases in discharge in association with
movement (Mano and Yamamoto, 1980; Chapman et aI., 1986;
Dugas and Smith, 1992; Stein and Glickstein, 1992; Thach et
aI., 1992; Fortier et aI., 1993). The decreases could permit the
buildup of positive feedback in modules that innervate agonist muscles, whereas the increases could suppress the activity of modules that innervate antagonists (cf. Houk et al.,
1993).
It is important to note that, in this scheme, the programmed action is not initiated by the programming process;
instead, it is initiated by separate triggering signals. Sensory
and cortical-cortical inputs to the cortical-cerebellar loop at
any point can trigger positive feedback (Fig. 2A), thus initiating the action command. If the triggering process were suppressed, one could have programming activity in the cerebellar cortex without any execution of movement, in agreement
with the finding that local blood flow in the cerebellum is
enhanced when a subject imagines a movement sequence,
without actually performing it (Decety et aI., 1990).
Assuming that the triggering process does occur, as the
movement proceeds the Purkinje cells that turned off during
selection would recognize that the desired action is nearly
completed, whereupon they would resume their stable "on"
state and inhibit positive feedback in the cortical-cerebellar
module. This is considered to be a predictive type of control
that can compensate for the time delays between a central
command and the resultant action. Miall et al. (1993) proposed that the cerebellum accomplishes predictive compensation by forming detailed models of the controlled system
and combining these models in a specialized manner to generate the output command. By contrast, we suggested a
scheme in which Purkinje cells simply combine signals representing limb and target position, velocity, force, and efference copy signals to determine when to switch to their "on"
state, thus terminating the motor command (van Kan et al.,
1993b). Purkinje cells would learn to recognize input patterns
that occur at a significant phase advance so as to predict goal
acquisition rather than depending on delayed feedback confirming that a goal has actually been achieved.
Cell Activity in Different Components of the Module
The proposed model provides an explanation for the substantial differences that have been observed between movementrelated signals that enter and leave the cerebellum (Houk and
Gibson, 1987; Houk et aI., 1990; van Kan et aI., 1993b). The
most striking differences between inputs recorded from
mossy fibers and outputs recorded from either Purkinje or
nuclear cells concern sensory responsiveness and sensitivity
to position versus velocity of a movement. Many mossy fibers
are highly responsive to sensory stimulation and show prominent tonic sensitivity to limb position; they may also show
sensitivity to velocity (van Kan et aI., 1993b). In contrast, the
outputs recorded from Purkinje (Harvey et aI., 1977) and nuclear (Harvey et aI., 1979; van Kan et aI., 1993a) cells show
either no response or a weak, phasic response to natural sensory stimulation, whereas they show intense movement-related bursts that correlate with velocity. These data document
striking sensory-to-motor and position-to-velocity transformations that occur within the cerebellar cortex.
The observed transformations are well accounted for by
the proposed model of cortical-cerebellar modules. According
to the model, sensory signals related to limb position would
inform Purkinje cells about the progress of ongoing movements and would contribute importantly to the constellation
of inputs these cells might use to recognize when the limb
is near the goal of an action. The Purkinje cells would then
switch to an intense firing mode, which would terminate the
movement command in advance of the desired endpoint of
the movement. The bistable dendritic properties discussed
earlier lead to the prediction that Purkinje (and, therefore,
nuclear) cells would be relatively insensitive to sensory inputs
from their parallel fibers under most circumstances. Only
when the cell neared the point of state transition would inputs appear to be effective. This aspect of the model explains
the sensory-to-motor transformation of the cerebellar cortex.
As noted earlier, the present model generates the phasic, velocity-related aspect of the output signals by shutting down
the cortical-cerebellar loop activity that was originally triggered to initiate the movement command (Fig. 2B). This feature of the model accounts for the position-to-velocity transformation of the cerebellar cortex.
In contrast to the differences predicted between cerebellar
cortical inputs and outputs, the model predicts that the activity of cells in the deep cerebellar nuclei should resemble signals recorded in the primary motor cortex and at other stages
throughout the limb premotor network, at least to a first approximation (Houk et aI., 1993). Some early reports suggested
that activity in the deep cerebellar nuclei leads that in the
primary motor cortex (see Thach et aI., 1992). However, Fortier et al. (1993, p 1143) have recently undertaken a detailed
comparison of cerebellar and motor cortical activity during
reaching movements. They reported no dramatic timing difference between cerebellar and motor cortical premovement
activity and that, notwithstanding some important contrasts
in detail, "the mean directional tuning curves of the cerebellar
and motor cortical populations suggest strong similarities
among the general behavior of these two motor control
regions." The overall similarity of activity patterns, timing, and
other properties confirms the predictions of the present model. The differences between cerebellar and neocortical activity might also be instructive. Motor cortical neurons tended
to show more inhibition of activity during nonpreferred directions of limb movement, were more narrowly tuned for
direction, and showed less variability than cerebellar neurons.
There were more nondirectional cerebellar cells and they
Cerebral Cortex Mar/Apr 1995, V 5 N 2 101
showed a higher spontaneous firing rate than primary motor
cortical neurons. In sum, it appears that the cerebellar component of the loop is more likely to be actively discharging
than its cortical limb, at least for forearm-movement tasks, but
does so in a manner less consistently correlated with motor
behavior. This property, which might result from a lack of
inhibitory interneurons in the cerebellar nucleus, could bias
the cortical-cerebellar loop toward its threshold for sustaining
activity.
Function and Dysfunction of
Cortical-Cerebellar Modules
In the present model, Purkinje cells act as adaptive pattern
classifiers, and failures in their function will have predictable
consequences for motor control. Excessively restrictive or
permissive classifications will lead to errors in movement amplitude and direction. Coordination can be thought to result
from the proper control of Purkinje cells throughout the relevant parts of the cerebellar cortex and the timing of that
control during the evolution of ongoing movements and
movement sequences. This model is consistent with the effects of cerebellar disease, which are characterized by errors
of starting, stopping, and directing action, as well as control
of acceleration, velocity, force, and the synthesis of complex
synergies (Holmes, 1939; Stein and Glickstein, 1992; Thach et
aI., 1992).
The cerebellum has been traditionally viewed as a structure of coordination and, more recently, of motor learning
(Ito, 1984: Thompson, 1986). Certainly, many structures participate in coordination and motor learning, as postulated for
models of the vestibulo-ocular reflex (Ito, 1984; Lisberger,
1988; Peterson et aI., 1991) and saccadic eye movements
(Dominey and Arbib, 1992; Houk et aI., 1992). But, as Thach
et al. (1992, p 436) have concluded, "what is unique about
cerebellar learning is the flexibility, ease, and speed of assigning the trigger-response ... and of building the tailor-made
complex movement response." The architecture described in
this section, positing Purkinje cell control of distributed cortical-cerebellar loop discharge, shows one simple mechanism
for achieving those attributes. Input to any part of the loop
from any modality, source, or level of the neuraxis can trigger
the same motor program. Various combinations of these
loops, regulated to a particular spatiotemporal pattern
through the mediation of inputs to the cerebellar cortex, can
sculpt the choice of components into a coherent, coordinated
act of unending complexity and flexibility.
Cortical-Cortical and Cortical-Thalamic Modules
Collective Computations with Pyramidal CeUs
In an earlier section we suggested that cortical pyramidal cells
may be specialized for graded input/output relations, which
would make them well suited to the performance of collective computations (Tank and Hopfield, 1987). Collective computations involve weighting and combining several input factors to generate an output that reflects the aggregate
properties of these inputs. As opposed to the pattern classification problems faced by Purkmje and striatal neurons, the
myriad of inputs to pyramidal cells would have to be added
and subtracted to form a graded resultant that reflects many
factors over a wide, dynamic range, rather than calling for
sharp decision surfaces.
Not only are neurons with graded input/output properties
of value in collective computations; their organization into
highly interconnected, recurrent networks represents an important element in their computational capabilities. A long
history of connectionist literature (Kohonen, 1972; Anderson
et aI., 1977; Grossberg, 1980; Hopfield, 1982, 1984; Rummel-
102 Distributed Modules in Motor Control' Houk and Wise
hart et aI., 1987) has shown that highly interconnected, recurrent networks are an excellent architecture for associative
memories, perceptual operations, and the solution of optimization problems. The ability to respond to graded inputs allows many individual factors to be appropriately weighted;
the presence of many neurons takes advantage of the speed
of parallel computation; and the availability of many loops of
interconnection facilitates recursive readjustments of multiple
factors. These features in combination yield networks ideally
suited to automatic collective computations.
Nodal Points and Attractors. Inputs converge upon a cortical column from several different cortical areas (cortical-cortical modules), from other columns of the same cortical fields,
and from several thalamic nuclei (cortical-thalamic modules)
(Jones et aI., 1978; Jones, 1985; Goldman-Rakic, 1988a; Huntley and Jones, 1991). Cortical columns connecting with each
other form cortical-cortical modules and the same columns
connecting with restricted zones in the thalamus form cortical-thalamic modules. Although cortical cells projecting to
other cortical columns and those reciprocating inputs to thalamus reside in different layers, for the most part they are
highly interconnected and receive many common inputs. The
cortical column can be thought of, then, as a nodal point that
combines the several types of influence represented by its
diverse sources of cortical and thalamic input (Goldman-Rakic, 1988a,b).
As indicated in Figures 1 and 2, nearly all of the connections that converge upon a particular nodal point in the cerebral cortex are reciprocated by return projections, although
laminar organization and quantitative aspects of these recriprocal connections may differ (Goldman-Rakic, 1988a; Felleman and Van Essen, 1991). Reciprocity combines with the
nodal architecture to form a complex cortical and thalamic
network that comprises numerous feedback loops and alternative paths for information flow. Networks of this type have
interesting nonlinear dynamical properties that may include
limit cycles, chaotic trajectories, and discrete equilibrium
states called point attractors (Freeman, 1979; Hopfield, 1982).
The focus here will be on point attractors, which are points
in state space' where state variables remain constant rather
than varying as a function of time. Attractors can be thought
of as valleys, or minima, in a landscape created by an "energy"
function (Hopfield, 1982). If the state of the network is transiently displaced away from an attractor, energy is elevated,
and the state variables (the activations or discharge rates of
neruons) will begin to change with time, either increasing or
decreasing. Their variations will be along a trajectory that
gradually moves the state to a lower energy level, that is, toward an attractor. The network can settle back to either the
same attractor, or to another one, as long as it follows a downhill trajectory on the energy landscape. Connectionist research has demonstrated that the particular values assumed
by the state variables at attractor points can be used to represent solutions to important information processing problems in cognitive psychology (Rummelhart et aI., 1987; Tank
and Hopfield, 1987: Hinton and Shallice, 1991).
The feedback loops discussed earlier for cortical-cerebellar
modules can serve to illustrate the general idea of network
attractors. These recurrent loops are characterized by two
types of point attractor (Eisenman et aI., 1991). A quiescent
type would serve to keep the network in a relatively inactive
state, corresponding to a resting period before a motor command is generated. In contrast, an active type of attractor
would maintain the network in a state of intense activity, corresponding to the population discharge that comprises a motor command. While the animal rests, network state might
vary somewhat but remain within the basin of attraction of
the quiescent attractor, A sufficiently strong sensory input
sensory input
motor output
Figure 3. The cerebral cortex is represented as an idealized, two-dimensional sheet. Nodal points may have high Iwhite and light stipple versus low black and heavy stipplel activity
during a sensorimotor event. Neither laminar organization nor temporal variation is reflected inthis figure.
would push the network state out of the quiescent basin and
into the basin of the active attractor. This would amount to
triggering the action, as outlined above. The position of the
active attractor in state space would be controlled by the
pattern of Purkinje cell discharge impinging on the corticalcerebellar loops. Particularly important would be the silencing of Purkinje cells at selective nodes in the network to permit the buildup of the intense discharge needed to command
a movement in a particular direction. A transition back into
the quiescent basin would result from the resumption of firing in these Purkinje cells, promoted by the parallel fiber inputs that occur as the goal of the movement is being approached.
To return to cortical-cortical modules, Figure 3 shematizes
active cortical nodal points (lighter areas) as viewed from the
surface of a flattened cortical map, and one can imagine the
reciprocating thalamic neurons underlying each of these
nodes. This diagram should be viewed not as a snapshot at a
particular point in time, but as a time exposure with the
"shutter" open throughout the duration of an entire sensorimotor response. It is also important to emphasize that the
activity map is quite different from the energy landscape that
directs the movements of trajectories in state space, although
the two are related. The activity map conforms well to a
three-dimensional representation, with two dimensions devoted to the idealized, flattened cortical surface, whereas network state space will have a much higher dimensionality (see
note 1). The sensory input on the left in Figure 3 is assumed
to activate an initial site on the cortex. This activity then
spreads to other areas, eventually activating the motor cortex
so as to trigger recurrent activity in cortical-cerebellar loops,
as discussed in the previous paragraph. A connectionist inter-
pretation of this spread of activity from sensory to motor
areas might relate it to a trajectory Winding downhill through
the energy landscape characterizing n-dimensional state
space, ultimately moving the activity of the network to an
attractor that signifies a particular spatial pattern of motor
cortical activation.
Cerebral Cortex as a Repository of State Information. In
addition to serving as an attractor network for collective computations, the cerebral cortex also serves as a repository for
much useful information about the state of the environment
and the organism, that is, external and internal conditions. The
activities of neurons in columns throughout a given area of
the cortex typically have response properties that are similar
in some generic sense, whereas one or several parametric features of the responses differ among the individual columns.
In this manner, an array of cortical columns, as a whole, forms
a distributed representation of some internal state of the organism or an external state of the world. Examples of such
representations include those of movement direction in the
motor cortex (Georgopoulos et al., 1993), visual image motion
in the visual cortex (Gilbert and Wiesel, 1981), and space in
the parietal and prefrontal cortex (Zipser and Anderson,
1988; Funahashi et al., 1989).
Coordination of Multiple Modules in Controlling Behavior
In the section on cortical-basal ganglionic modules, we advanced the hypothesis that they might function as detectors
of specific contexts, providing information that could be useful in the planning and gating of action. Then, in the section
on cortical-cerebellar modules, we discussed how they might
function in the programming, execution and termination of
actions. Finally, in the section immediately above, we indicated
Cerebral Cortex Mar/Apr 1995, V 5 N 2 103
sensory inputs
action
command
context
registration
BASAL GANGLIA
CEREBELLUM
Figure 4. Combined schema ofacortical-basal ganglionic and a cortical-cerebellar module to illustrate how activities inmultiple modules could be coordinated (see text). Abbreviations
are as in Figures lA and 2A.
how cortical-cortical modules might implement collective
computations while also serving as a repository for diverse
distributed representations. While it is clear that each of these
operations might contribute usefully to the control of action,
an important problem concerns how different modules can
function cooperatively. In this section, we posit two levels of
coordination among modules: one within arrays of similar
modules, the other among different kinds of modular arrays.
Coordination within Arrays
At the most elementary level of modular organization, we envision arrays of similar modules that must function in a coordinated manner to control the population activity in each
cortical area. We do not illustrate a modular array, but they
can be readily imagined as n replications of Figure ZA for the
cortical-cerebellar class of modules and n repetitions of Figure lA for the cortical-basal ganglionic modules. The clear
need for coordination within these arrays can be appreciated
from the well-known coding of single-unit activity in the motor cortex (Georgopoulos et al., 1993). Cortical neurons are
broadly tuned to movement direction, and a population of
neurons with different optimal tuning directions needs to be
recruited and coordinated to command a particular limb
movement. Simulations have shown (Berthier et al., 1993)
that an array of cortical-cerebellar modules can learn to function in a coordinated manner under the training influence of
simulated climbing fiber inputs to the cerebellar cortex. The
information source for coordination in this particular model
is feedback from the environment and other types of input
transmitted via the mossy fiber-parallel fiber input to Purkinje
cells, as envisioned by Thach et al. (1992).
104 Distributed Modules in Motor Control' Houk and Wise
Coordination between individual modules may also result
from divergent anatomical connections among functionally
related loops (Houk et aI., 1993), which could promote the
recruitment of functionally related modules near the beginning of movement and the simultaneous cessation of discharge at its end. Thus, coordination within arrays of corticalcerebellar modules may stem from either the adaptive use of
common inputs, interconnections between functionally related modules, or both. Coordination within arrays of corticalbasal ganglionic, cortical-thalamic, and cortical-cortical modules is probably achieved through the application of similar
principles.
Coordination among Arrays
At a higher level of organization, the motor system needs
mechanisms for coordinating the functions of different modular arrays. Three general types of anatomical projections may
promote communication and coordination among arrays: (I)
corticostriatal, (2) corticopontocerebellar, and (3) corticocortical projections. Figure 4 illustrates examples of these types
of communication between one cortical-basal ganglionic
module, one cortical-cerebellar module, and a group of cortical-cortical and cortical-thalamic modules. If one imagines
each of these modules being replaced by an array of n similar
modules, the same diagram can be used to contemplate communication among modular arrays.
Figure 4 was constructed by joining Figures lA and ZA,
using as tether points common connections with cortical columns. We assumed that the rightmost cortical column in the
cortical-basal ganglionic module of Figure lA is the same as
the leftmost column in the cortical-cerebellar module of Fig-
Cerebral Cortex
(collective computation)
(information arrays)
Basal Ganglia
(context encoders)
;;7~~((]bB\~ ~
dopamine fibers
(reinforcement)
motor
commands
m((]![j]ILl~B\~((]W'~
%!!
climbing fibers
(discoordination)
Cerebellum
(pattern generators)
sens ory
stat es
Figure 5. Overview diagram showing global interrelations in a system based on a distributed modular architecture. The cerebral cortex performs collective computations that
automate planning and acting, while also serving as a repository of information arrays used by the basal ganglia and cerebellum. The basal ganglia use this information to encode
new contexts that are fed back into frontal cortical arrays, a process that is guided byreinforcement inputs transmitted in dopamine fibers. The cerebellum uses similar information
to select and generate motor patterns, a process guided bydiscoordination signals transmitted in climbing fibers. These motor patterns are fed back to motor cortical arrays, and
corticospinal fibers transmit them to the spinal cord as motor commands. Global modulators may help to coordinate the activities of these different parts of the brain through
generalized reinforcement.
ure ZA, and is the middle column depicted in Figure 4. This
illustrates the fact that many cortical columns originate both
corticostriatal and corticopontocerebellar projections, although they originate from different neurons within the column (Mercier et aI., 1990). The differential laminar organization of corticostriatal versus corticopontine cells suggests
that, to an extent, a cortical column could send differing signals to each, but the overall columnar organization of the
cortex predicts that these signals should have much in common. The common cortical column mentioned earlier is depicted as receiving sensory information (via the thalamus),
which may be useful both in the detection of a critical context and in the execution of a particular action. Hence, we
illustrate that column as feeding information into both the
basal ganglia and the cerebellum. Similarly, we assumed that
the leftmost cortical column in the cortical-basal ganglionic
module projects additionally to the cerebellum by way of the
pons. As the output of that cortical-basal ganglionic module,
this leftmost column registers the fact that the context to
which the module is tuned just occurred. This information
can be used by the cortical-cerebellar modules, thus assisting
in the programming and execution of an appropriate action
based on a recognized context. The third type of anatomical
connectivity available for coordination is corticocortical, and,
since these projections are generally reciprocal, they form the
cortical-cortical information processing modules discussed
above. The rapid, automatic communication provided by cortical-cortical modules provides an efficient mechanism for
sharing related information, reorganizing it, and promoting coordination among arrays of distributed modules.
Operating Principles
Note that each of the processes controlled by individual modular arrays may occur asynchronously and in parallel. For example, context information may be available for selecting an
appropriate action before it is initiated, or alternatively, a default action can be initiated and the selection process can
function later to redirect the commanded action as it evolves
(Houk et aI., 1993). This would accommodate the independent actions observed for "where" and "when" systems in
eye-limb coordination (Gielen and van Gisbergen, 1990) as
well as the asynchronous interactions observed between limb
movement initiation and its programming (Ghez et aI., 1990).
Figure 5 summarizes the global interrelations in a system
based on the distributed modular architecture discussed in
this article. Because the cerebral cortex is interposed between
the basal ganglia and cerebellum, one of its functions will be
to serve as a switchboard for information flow between these
two subcortical structures. Considering the large number of
functionally distinct cortical areas, each comprising an array
of cortical columns, one can think of the cerebral cortex as
an expansive repository for a diverse set of information arrays.
Some of these arrays are trained by local learning rules to
represent coarse coding of relatively unmodified sensory information, whereas another group may form more complex
feature maps based on a reorganization of primary sensory
information (Barlow, 1989). Yet others are controlled in more
complex ways by input from the cerebellum and basal ganglia, via their thalamic relays. The cerebral cortex continuously uses its diverse information arrays to perform its collective
computations, while simultaneously sharing these data with
the basal ganglia and cerebellum.
As illustrated in Figure 5, the basal ganglia function as detectors and encoders of salient contexts. Striatal spiny neurons are trained by their dopamine reinforcement input to
recognize contexts and states that are likely to be useful in
guiding behavior. The inputs for these computations come
from nearly the entire cerebral cortex, and the processed outputs return to the frontal cortex. These signals invest cortical
arrays with planning information that can then be used by
other modules. The cerebellum functions as a generator of
spatiotemporal patterns. To do this, Purkinje and basket cells,
under the training influence of climbing fibers that detect
discoordination, learn to select appropriate actions and to recognize the endpoints (or goals) of the actions. In addition to
commanding actions, information about intention may be
sent from cerebellum to motor cortical arrays, whereupon it
becomes available for use by other information processing
modules.
We envision that much of the learning that goes on in this
distributed modular network may be module specific, due to
the topographic specificity of climbing and dopamine fiber
input to the cerebellum and basal ganglia, respectively. While
the specificity of dopaminergic inputs to striatum (Graybiel,
1991; Gerfen, 1992) may not be quite as pronounced as that
of climbing fibers to cerebellum, both inputs are clearly topographically organized. Learning in the cortical network appears instead to depend mainly on a local Hebbian learning
rule, although global modulations and reinforcements are
Cerebral Cortex Mar/Apr 1995, V 5 N 2 105
probably provided by diffuse neuromodulatory input to the
cortex (Fig. 5).
Of particular interest from the standpoint of coordination
is the possibility that learning in the frontal cortex may be
guided by the outputs from the basal ganglia and cerebellum.
These outputs can be thought of as forcing functions for
both execution and learning. These forcing functions could
train the cortical-cortical network through the mechanism of
"example setting" that was discussed in the section above on
cell properties. This is because basal ganglia and cerebellar
inputs to their respective cortical-thalamic modules are well
positioned to alter the attractors of the cortical-cortical and
cortical-thalamic arrays of the frontal lobe. On one hand, these
alterations would serve to modify the collective computations
being performed by the cortical network on a moment-bymoment basis. On the other hand, the same alterations would
promote changes in the weights of the Hebbian synapses on
pyramidal neurons that, in the long run, would move the network's attractors closer to the points being forced by cerebellar and basal ganglia modifications. As a consequence, the
frontal cortex would become trained to perform, in a highly
efficient and automatic fashion, those particular functions being forced on it by its subcortical inputs. This hypothesis contrasts with the traditional assumption that the cerebral cortex
learns first and then trains subcortical structures.
The Search for Intelligent Behavior
How might the cortical-basal ganglionic, cortical-cerebellar,
and cortical-cortical modular arrays discussed in this article
contribute to intelligent motor behavior? Three decades ago
Minsky (1963), in his thoughtful analysis of the major unresolved problems in the field of artificial intelligence, enumerated five broad areas of problem-solving activity: search, planning, pattern recognition, learning, and induction. Search
comprises the immediate problem of selecting a solution
(e.g., an action) from a large repertoire of potential solutions
distributed in neural space. Exhaustive search of the entire
solution space is a straightforward, but usually impractical,
solution. As an alternative to such an inefficient approach,
Minsky saw planning as an administrative facility for dividing
complex problems into simpler subproblems and pattern recognition as a means to improve the efficiency of searching
for a solution by categorizing the search into small domains.
Minsky viewed learning as a means for directing, through
reinforcement, a search toward solutions that have worked
for similar problems in the past. Finally, induction can be construed as the invention of novel solutions, plans, and pattern
recognition capabilities to solve problems that the individual
has never encountered. In this section, we use the global summary of Figure 5 as a basis for discussing how the basal ganglia, cerebellum, and cerebral cortex might function together
in the implementation of Minsky's concepts for intelligent behavior.
We posit that, ultimately, the search for an action depends
on cortical-cerebellar modules, taking particular advantage of
the unique cellular specializations of Purkinje cells for pattern
classification. Some automatic actions, of course, bypass the
cerebellum and are controlled exclusively by brainstem and
spinal premotor networks, as a reflex or a sensorimotor habit.
However, when the choice of an action or its implementation
is reasonably complex, the cerebellum is likely to be involved
(Ito, 1984; Houk and Barto, 1992; Thach et aI., 1992). According to the theory espoused here, cortical-cerebellar arrays not
only retrieve a stored motor program, but also see through
the execution of the action to the point of recognizing the
conditions for its termination. In doing so, cortical-cerebellar
modules create an output pattern that is temporal as well as
spatial and comprises the activity in a large array of effector
106 Distributed Modules in Motor Control' Houk and Wise
premotor circuits. When activated and executed, these processes and their sequelae represent the completed search.
Planning may depend on the context recognition function of the cortical-basal ganglionic modules. Through the mediation of the frontal cortex (Fig. 5), context information
could be used by cortical-cerebellar modules to plan actions
on the basis of the motivational, sensorimotor, and cognitive
context. In an overly simplistic example, one can envision the
basal ganglia as recognizing one of two potential contexts,
each associated with half of the potential solution space,
which thereby speeds search by a factor of 2. By recognizing
a greater variety of contexts, the basal ganglia's contribution
could promote the efficiency of the search many fold. Indeed,
without such context recognition, the ability of the cerebellar
cortex to use its pattern classification mechanism for choosing and implementing appropriate actions would be quite
limited (Rosenblatt, 1962; Minsky and Papert, 1969). By tapping into the cortical information arrays, the cerebellar cortex
is able to draw on inputs beyond crude sensory and motor
states so as to include the wealth of processed information
available in the cortical arrays.
The mediation of the frontal cortex in this coordination
deserves further comment. As discussed earlier, one type of
recoding involves the formation of feature maps based on
information-maximizing principles (Barlow, 1989). Although a
pattern classifier functions better when presented with features, an even more substantial improvement can be achieved
if the pattern classifier is presented with high-order properties, provided these properties are of "heuristic" value (Minsky, 1963). The striatum is ideally situated to perform this heuristic classification, because its pattern recognition is guided
by training signals representing predictions of reward
(Schultz et aI., 1995). Its spiny neurons sample a diverse input
space that comprises sensory states, features, motor intentions, and high-level properties generated by its own contextencoding actions. The postulated ability of the basal ganglia
to use contexts that it initially detects to encode yet more
complex contexts is potentially very powerful, due to the recursive nature of this computation.
To Minsky (1963), learning controls the navigation
through solution space and does so on the basis of experience. He discussed in some detail the problem of credit assignment, which is one of the major obstacles to effective
learning in complex systems. Credit assignment involves getting the right training information to the right location (spatial credit assignment) at the right time (temporal credit assignment) for it to be effective in guiding the learning
process. The topographical nature of climbing fiber input to
the cerebellum (Oscarsson, 1980; Voogd and Bigare, 1980)
and of dopamine projections onto the striatum (Graybiel,
1991; Gerfen, 1992) could help to resolve the spatial credit
assignment problem, and the predictive nature of dopamine
signals would help to resolve the temporal credit assignment
problem (Houk et aI., 1995).
Induction represents the highest of Minsky's processes:
the generation and evaluation of novel solutions represents
the central problem of intelligent behavior, We have already
discussed the importance of recursion in the encoding of
ever-higher level contexts. Recursion and self-reference, were
these processes to take into account the overall success or
failure of the action-guidance system as a whole, might serve
as a powerful tool in the process of induction.
We conclude with a thought about thinking. By focusing
on the motor system we have been able to avoid the issue of
conscious awareness, since intelligent guidance of action regularly occurs in the absence of consciousness (Goodale et aI.,
1991). However, the principles of distributed modular architectures that we have disussed in this article may have rele-
vance for a broad scope of cognition, as evidenced by recent
treatises that have discussed modularity in the highest brain
functions (Fodor, 1983; Minsky, 1986).
Notes
1. The relevant "state space" is n dimensional, where n is the number of neurons and each axis characterizes a neuron's state, for example, its activation or firing rate.
This work was supported by a Center Grant from the National
Institutes of Mental Health (P50 MH 48185) to Dr. Houk.
Correspondence should be addressed to Dr. J. C. Houk, Department of Physiology, Northwestern University Medical Center, 303 East
Chicago Avenue, Chicago, IL 606 11.
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