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SCHEMA FOR MOTOR CONTROL 
UTILIZING A NETWORK MODEL OF THE CEREBELLUM 
James C. Houk, Ph.D. 
Northwestern University Medical School, Chicago, Illinois 
60201 
ABSTRACT 
This paper outlines a schema for movement control 
based on two stages of signal processing. The higher stage 
is a neural network model that treats the cerebellum as an 
array of adjustable motor pattern generators. This network 
uses sensory input to preset and to trigger elemental 
pattern generators and to evaluate their performance. The 
actual patterned outputs, however, are produced by intrin- 
sic circuitry that includes recurrent loops and is thus 
capable of self-sustained activity. These patterned 
outputs are sent as motor commands to local feedback 
systems called motor servos. The latter control the forces 
and lengths of individual muscles. Overall control is thus 
achieved in two stages: (1) an adaptive cerebellar network 
generates an array of feedforward motor commands and (2) a 
set of local feedback systems translates these commands 
into actual movements. 
INTRODUCTION 
There is considerable evidence that the cerebellum is 
involved in the adaptive control of movement 1, although the 
manner in which this control is achieved is not well under- 
stood. As a means of probing these cerebellar mechanisms, 
my colleagues and I have been conducting microelectrode 
studies of the neural messages that flow through the inter- 
mediate division of the cerebellum and onward to limb 
muscles via the rubrospinal tract. We regard this cerebel- 
lorubrospinal pathway as a useful model system for studying 
general problems of sensorimotor integration and adaptive 
brain function. A summary of our findings has been pub- 
lished as a book chapter 2. 
On the basis of these and other neurophysiological 
results, I recently hypothesized that the cerebellum func- 
tions as an array of adjustable motor pattern generators 3. 
The outputs from these pattern generators are assumed to 
function as motor commands, i.e., as neural control signals 
that are sent to lower-level motor systems where they 
produce movements. According to this hypothesis, the 
cerebellum uses its extensive sensory input to preset the 
@ American Institute of Physics 1988 
368 
pattern generators, to trigger them to initiate the 
production of patterned outputs and to evaluate the success 
or failure of the patterns in controlling a motor behavior. 
However, sensory input appears not to play a major role in 
shaping the waveforms of the patterned outputs. Instead, 
these waveforms seem to be produced by intrinsic circuity. 
The initial purpose of the present paper is to provide 
some ideas for a neural network model of the cerebellum 
that might be capable of accounting for adjustable motor 
pattern generation. Several previous authors have 
described network models of the cerebellum that, like the 
present model, are based on the neuroanatomical organiza- 
tion of this brain structure4,5, 6. While the present model 
borrows heavily from these previous models, it has some 
additional features that may explain the unique manner in 
which the cerebellum processes sensory input to produce 
motor commands. A second purpose of this paper is to 
outline how this network model fits within a broader schema 
for motor control that I have been developing over the past 
several years 3,7. Before presenting these ideas, let me 
first review some basic physiology and anatomy of the 
cerebellum 1 . 
SIGNALS AND CIRCUITS IN THE CEREBELLUM 
There are three main categories of input fibers to the 
cerebellum, called mossy fibers, climbing fibers and 
noradrenergic fibers. As illustrated in Fig. 1, the mossy 
fiber input shows considerable fan-out via granule cells 
and parallel fibers. The parallel fibers in turn are 
arranged to provide a high degree of fan-in to individual 
Purkinje cells (P). These P cells are the sole output 
elements of the cortical portion of the cerebellum. Via 
the parallel fiber input, each P cell is exposed to 
approximately 200,000 potential messages. In marked 
contrast, the climbing fiber input to P cells is highly 
focused. Each climbing fiber branches to only 10 P cells, 
and each cell receives input from only one climbing fiber. 
Although less is known about input via noradrenergic 
fibers, it appears to be diffuse and even more divergent 
than the mossy fiber input. 
Mossy fibers originate from several brain sites trans- 
mitting a diversity of information about th external world 
and the internal state of the body. Some mossy fiber 
inputs are clearly sensory. They come fairly directly from 
cutaneous, muscle or vestibular receptors. Others are 
routed via the cerebral cortex where they represent highly 
processed visual, auditory or somatosensory information. 
Yet another category of mossy fiber transmits information 
about central motor commands (Fig. 1 shows one such path- 
way, from collaterals of the rubrospinal tract relayed 
369 
through the lateral reticular nucleus (L)). The discharge 
rates of mossy fibers are modulated over a wide dynamic 
range which permits them to transmit detailed parametric 
information about the state of the body and its external 
environment. 
noradrenergic 
fibers 
climbing 
fiber 
granule 
cells  
mossy fibers  
Sensory 
Inputs 
sensorimotor 
cortex 
rubrospinal tract 
Motor 
Commands 
Figure 1: Pathways through the cerebellum. This diagram, 
which highlights the cerebellorubrospinal system, also 
constitutes a circuit diagram for the model of an 
elemental pattern generator. 
The sole source of climbing fibers is from cells 
located in the inferior olivary nucleus. Olivary neurons 
are selectively sensitive to sensory events. These cells 
have atypical electrical properties which limit their 
discharge to rates less than 10 impulses/sec, and usual 
rates are closer to 1 impulse/sec. As a consequence, 
370 
individual climbing fibers transmit very little parametric 
information about the intensity and duration of a stimulus; 
instead, they appear to be specialized to detect simply the 
occurrences of sensory events. There are also motor inputs 
to this pathway, but they appear to be strictly inhibitory. 
The motor inputs gate off responsiveness to self-induced 
(or expected) stimuli, thus converting olivary neurons into 
detectors of unexpected sensory events. 
Given the abundance of sensory input to P cells via 
mossy and climbing fibers, it is remarkable that these 
cells respond so weakly to sensory stimulation. Instead, 
they discharge vigorously during active movements. P cells 
send abundant collaterals to their neighbors, while their 
main axons project to the cerebellar nuclei and then onward 
to several brain sites that in turn relay motor commands to 
the spinal cord. 
Fig. 1 shows P cell projections to the intermediate 
cerebellar nucleus (I), also called the interpositus 
nucleus. The red nucleus (R) receives its main input from 
the interpositus nucleus, and it then transmits motor 
commands to the spinal cord via the rubrospinal tract. 
Other premotor nuclei that are alternative sources of motor 
commands receive input from alternative cerebellar output 
circuits. Fig. 1 thus specifically illustrates the 
cerebellorubrospinal system, the portion of the cerebellum 
that has been emphasized in my laboratory. 
Microelectrode recordings from the red nucleus have 
demonstrated signals that appear to represent detailed 
velocity commands for distal limb movements. Bursts of 
discharge precede each movement, the frequency of discharge 
within the burst corresponds to the velocity of movement, 
and the duration of the burst corresponds to the duration 
of movement. These velocity signals are not shaped by 
continuous feedback from peripheral receptors; instead, 
they appear to be produced centrally. An important goal of 
the modelling effort outlined here is to explain how these 
velocity commands might be produced by cerebellar circuits 
that function as elemental pattern generators. I will then 
discuss how an array of these pattern generators might 
serve well in an overall schema of motor control. 
ELEMENTAL PATTERN GENERATORS 
The motivation for proposing pattern generators rather 
than more conventional network designs derives from the 
experimental observation that motor commands, once initiat- 
ed, are not affected, or are only minimally affected, by 
alterations in sensory input. This observation indicates 
that the temporal features of these motor commands are 
produced by self-sustained activity within the neural 
network rather than by the time courses of network inputs. 
371 
Two features of the intrinsic circuitry of the cere- 
bellum may be particularly instrumental in explaining self- 
sustained activity. One is a recurrent pathway from cere- 
bellar nuclei that returns back to cerebellar nuclei. In 
the case of the cerebellorubrospinal system in Fig. 1, the 
recurrent pathway is from the interpositus nucleus to red 
nucleus to lateral reticular nucleus and back to interposi- 
tus, what I will call the IRL loop. The other feature of 
intrinsic cerebellar circuitry that may be of critical 
importance in pattern generation is mutual inhibition 
between P cells. Fig. 1 shows how mutual inhibition 
results from the recurrent collaterals of P-cell axons. 
Inhibitory interneurons called basket and stellate cells 
(not shown in Fig. 1) provide additional pathways for 
mutual inhibition. Both the IRL loop and mutual inhibition 
between P cells constitute positive feedback circuits and, 
as such, are capable of self-sustained activity. 
Self-sustained activity in the form of high-frequency 
spontaneous discharge has been observed in the IRL loop 
under conditions in which the inhibitory P-cell input to I 
cells is blocked 3. Trace A in Fig. 2 shows this unre- 
strained discharge schematically, and the other traces 
illustrate how a motor command might be sculpted out of 
this tendency toward high-frequency, repetitive discharge. 
Trace B shows a brief burst of input presumed to be 
sent from the sensorimotor cortex to the R cell in Fig. 1. 
This burst serves as a trigger that initiates repetitive 
discharge in an IRL loop, and trace D illustrates the 
discharge of an I cell in the active loop. The intraburst 
discharge frequency of this cell is presumed to be 
determined by the summed magnitude of inhibitory input 
(shown in trace C) from the set of P cells that project to 
it (Fig. 1 shows only a few P cells from this set). Since 
the inhibitory input to I was reduced to an appropriate 
magnitude for controlling this intraburst frequency some 
time prior to the arrival of the trigger event, this 
example illustrates a mechanism for presetting the pattern 
generator. Note that the same reduction of inhibition that 
presets the intraburst frequency would bring the loop 
closer to the threshold for repetitive firing, thus serving 
to enable the triggering operation. The I-cell burst, 
after continuing for a duration appropriate for the desired 
motor behavior, is assumed to be terminated by an abrupt 
increase in inhibitory input from the set of P cells that 
project to I (trace C). 
The time course of bursting discharge illustrated in 
Fig. 2D would be expected to propagate throughout the IRL 
loop and be transmitted via the rubrospinal tract to the 
spinal cord where it could serve as a motor command. 
Bursts of R-cell discharge similar to this are observed to 
precede movements in trained monkey subjects 2. 
372 
Illlllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll!lllllll 
I I 
time 
Figure 2: Signals Contributing to Pattern Generation. A. 
Repetitive discharge of I cell in the absence of P- 
cell inhibition. B. Trigger burst sent to the IRL 
loop from sensorimotor cortex. C. Summed inhibition 
produced by the set of P cells projecting to the I 
cell. D. Resultant motor pattern in I cell. 
The sculpting of a motor command out of a repetitive 
firing tendency in the IRL loop clearly requires timed 
transitions in the discharge rates of specific P cells. 
The present model postulates that the latter result from 
state transitions in the network of P cells. Bell and 
Grimm 8 described spontaneous transitions in P-cell firing 
that occur intermittently, and I have frequently observed 
them as well. These transitions appear to be produced by 
intrinsic mechanisms and are difficult to influence with 
sensory stimulation. The mutual recurrent inhibition 
between P cells might explain this tendency toward state 
transitions. 
Recurrent inhibition between P cells is mediated by 
synapses near the cell bodies and primary dendrites of the 
P cells whereas parallel fiber input extends far out on the 
dendritic tree. This arrangement may explain why sensory 
input via parallel fibers does not have a strong, continu- 
ous effect on P cell discharge. This sensory input may 
serve mainly to promote state transitions in the network of 
P cells, perhaps by modulating the likelihood that a given 
P cell would participate in a state transition. Once the 
373 
transition starts, the activity of the P cell may be domi- 
nated by the recurrent inhibition close to the cell body. 
The mechanism responsible for the adaptive adjustment 
of these elemental pattern generators may be a change in 
the synaptic strengths of parallel fiber input to P cells 9. 
Such alterations in the efficacy of sensory input would 
influence the state transitions discussed in the previous 
paragraph, thus mediating adaptive adjustments in the 
amplitude and timing of patterned output. Elsewhere I have 
suggested that this learning process is analogous to oper- 
ant conditioning and includes both positive and negative 
reinforcement 3. Noradrenergic fibers might mediate posi- 
tive reinforcement, whereas climbing fibers might mediate 
negative reinforcement. For example, if the network were 
controlling a limb movement, negative reinforcement might 
occur when the limb bumps into an object in the work space 
(climbing fibers fire in response to unexpected somatic 
events such as this), whereas positive reinforcement might 
occur whenever the limb successfully acquires the desired 
target (the noradrenergic fibers to the cerebellum are 
thought to receive input from reward centers in the brain). 
Positive reinforcement may be analogous to the associative 
reward-punishment algorithm described by Barto 10 which 
would fit with the diffuse projections of noradrenergic 
fibers. Negative reinforcement might be capable of a 
higher degree of credit assignment in view of the more 
focused projections of climbing fibers. 
In summary, the previous paragraphs outline some ideas 
that may be useful in developing a network model of the 
cerebellum. This particular set of ideas was motivated by 
a desire to explain the unique manner in which the cerebel- 
lum uses sensory input to control patterned output. The 
model deals explicitly with small circuits within a much 
larger network. The small circuits are considered elemen- 
tal pattern generators, whereas the larger network can be 
considered an array of these pattern generators. The 
assembly of many elements into an array may give rise to 
some emergent properties of the network, due to 
interactions between the elements. However, the highly 
compartmentalized anatomical structure of the cerebellum 
fosters the notion of relatively independent elemental 
pattern generators as hypothesized in the schema for 
movement control presented in the next section. 
SCHEMA FOR MOTOR CONTROL 
A major aim in developing the elemental pattern 
generator model described in the previous section was to 
explain the intriguing manner in which the cerebellum uses 
sensory input. Stated succinctly, sensory input is used to 
preset and to trigger each elemental pattern generator and 
374 
to evaluate the success of previous output patterns in 
controlling motor behavior. However, sensory input is not 
used to shape the waveform of an ongoing output pattern. 
This means that continuous feedback is not available, at 
the level of the cerebellum, for any immediate adjustments 
of motor commands. 
Is this kind of behavior actually advantageous in the 
control of movement? I would propose the affirmative, 
particularly on the grounds that this strategy seems to 
have withstood the test of evolution. Elsewhere I have 
reviewed the global strategies that are used to control 
several different types of body function 11. A common 
theme in each of these physiological control systems is the 
use of negative feedback only as a low-level strategy, and 
this coupled with a high-level stage of adaptive 
feedforward control. It was argued that this particular 
two-stage control strategy is well suited for utilizing the 
advantageous features of feedback, feedforward and adaptive 
control in combination. 
The adjustable pattern generator model of the cerebel- 
lum outlined in the previous section is a prime example of 
an adaptive, feedforward controller. In the subsequent 
paragraphs I will outline how this high-level feedforward 
controller communicates with low-level feedback systems 
called motor servos to produce limb movements (Fig. 3). 
The array of adjustable pattern generators (PGn) in 
the first column of.Fig. 3 produce an array of elemental 
commands that are transmitted via descending fibers to the 
spinal cord. The connectivity matrix for descending fibers 
represents the consequences of their branching patterns. 
Any given fiber is likely to branch to innervate several 
motor servos. Similarly, each member of the array of motor 
servos (MSm) receives convergent input from a large number 
of pattern generators, and the summed total of this input 
constitutes its overall motor command. 
A motor servo consists of a muscle, its stretch recep- 
tors and the spinal reflex pathways back to the same mus- 
cle 12. These reflex pathways constitute negative feedback 
loops that interact with the motor command to control the 
discharge of the motor neuron pool innervating the 
particular muscle. Negative feedback from the muscle 
receptors functions to maintain the stiffness of the muscle 
relatively constant, thus providing a spring-like interface 
between the body and its mechanical environment 13. The 
motor command acts to. set the slack length of this 
equivalent spring and, in this way, influences motion of 
the limb. Feedback also gives rise to an unusual type of 
damping proportional to a low fractional power of 
velocity 14. The individual motor servos interact with each 
other and with external loads via the trigonometric 
relations of the musculoskeletal matrix to produce 
resultant joint positions. 
375 
Cerebellar 
Network 
elemental 
commands 
motor forces, 
commands lengths 
Motor joint 
Sewos positions 
I External 
Load 
shoulder 
elbow 
wrist 
finger 
Figure 3: Schema for Motor Control Utilizing Pattern Gen- 
erator Model of Cerebellum. An array of elemental 
pattern generators (PGn) operate in an adaptive, feed- 
forward manner to produce motor commands. These out- 
puts of the high-level stage are sent to the spinal 
cord where they serve as inputs to a low-level array 
of negative feedback systems called motor servos 
(MSm). The latter regulate the forces and lengths of 
individual muscles to control joint angles. 
While the schema for motor control presented here is 
based on a considerable body of experimental data, and it 
also seems plausible as a strategy for motor control, it 
will be important to explore its capabilities for human 
limb control with simulation studies. It may also be 
fruitful to apply this schema to problems in robotics. 
Since I am mainly an experimentalist, my authorship of this 
paper is meant as an entr for collaborative work with 
neural network modelers that may be interested in these 
problems. 
376 
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