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3 Subthalamic Nucleus and Primary Processes of Motivation: Consumption

3 Subthalamic Nucleus and Primary Processes of Motivation: Consumption

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H.H. Yin



as a result of the extrafusal muscle fibers in parallel with the spindle, or as a result

of the contraction of intrafusal muscles due to gamma neuron activity. It reflects the

difference between desired muscle length and actual length—the error signal in the

length controller. The spindle acts as a mechanical comparator, as a stretch detected

by muscle spindle is compared with the net reference signal for muscle length. The

discrepancy or error is then sent to the alpha motor neuron to produce muscle contraction. At the same time, however, the alpha motor neurons can also be commanded directly by corticospinal projections. Such direct adjustment of force

reference seems particularly important for the movement of distal digits.

The descending signals for length reference come from the gamma motor neurons, which in turn receive inputs from the brainstem and the cerebral hemispheres.

When gamma motor neurons are activated, the contractile parts of the spindle (intrafusal fibers) attempt to shorten. A pull is generated at the equatorial region of the

spindle that results in Ia afferent activity, even though the spindle length does not

change significantly because it is anchored at both ends. Consequently, alpha motor

neurons are activated.



20.4.3



Position Control: Joint Angle and Body Configuration



To change a joint angle, the lengths of multiple muscles must be changed simultaneously. In turn, a body configuration consists of a set of joint angles. In each case,

the relevant perceptual input is represented as a one-dimensional signal. The controlled variable is a configuration of lower order proprioceptive inputs, and the

output function can reach a group of muscles that work together to produce the

appropriate net effect.

The control of body configurations is a type of position control. In position control, the controlled variable represents some position coordinate, and output is generated by computing the difference between the reference or desired position coordinate

and the input signal reporting the actual position. For motion with multiple degrees

of freedom, the actual position vector is determined by the action of multiple orthogonal controllers. In the brainstem, for example, there is evidence for distinct position

controllers for vertical and horizontal movements (Deliagina et al. 2012; King et al.

1981; Luschei and Fuchs 1972; Masino 1992; Masino and Knudsen 1990).

The key neural substrates for posture control are the reticulospinal, vestibulospinal, and rubrospinal pathways (Deliagina et al. 2008; Foreman and Eaton 1993;

Peterson et al. 1979). For example, stimulation of the reticulospinal pathway can

produce coordinated changes in joint angles: depending on stimulation location,

ipsilateral flexion and contralateral extension or the opposite pattern of ipsilateral

extension and contralateral flexion can be produced (Sprague and Chambers 1954).

The reticulospinal pathway receives direct and indirect projections from the SNr.

These descending projections are assumed to alter the reference signals for body

configuration control.



20 The Basal Ganglia and Hierarchical Control in Voluntary Behavior



20.4.4



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Locomotion and Gait



Although the reticulospinal pathway receives direct projections from the BG, more is

known about the indirect projections via the mesencephalic locomotor region (MLR)

and the pedunculopontine nucleus (PPN) (Garcia-Rill 1986; Sherman et al. 2015;

Shik and Orlovsky 1976). In addition, another area critical for locomotion, the diencephalic locomotor region (DLR), is found in the ventral thalamus in the lamprey and

thought to be analogous to the zona incerta in mammals (Menard and Grillner 2008).

DLR appears to be functionally similar to MLR but their respective roles remain

unclear (Mogenson and Nielsen 1983). In rodents, the substantia innominata or ventral pallidum projects to the zona incerta as well as the pedunculopontine nucleus.

One possibility is that the DLR is primarily concerned with head movements, whereas

the MLR is important for axial and limb movements. This is supported by studies

showing rotational movements of the head and eyes produced by zona incerta stimulation (Hyde and Toczek 1962) and deviation and torsion of the eyes as well as tilting

of the head following damage to this region (Hedges and Hoyt 1982).

The MLR is a primary target of BG outputs (Grillner et al. 2008). Recent work

showed that the core of the MLR is not in the PPN, but the lateral pontine tegmentum, which sends direct glutamatergic projections to the ventral horn of the spinal

cord. The SNr output reaches the midbrain extrapyramidal area, which in turn projects to the lateral pontine tegmentum (Sherman et al. 2015). The firing rates of many

MLR neurons are correlated with speed of running (Lee et al. 2014). Lesions of this

area produce cataplexy and episodic immobility. There appears to be a limb extensor circuit in the ventral MLR that is critical for the standing posture, receiving an

inhibitory projection from the SNc (GABAergic and dopaminergic neurons); the

dorsal MLR contains a flexor-dominant circuit necessary for locomotion, receiving

a projection from the SNr (Sherman et al. 2015).

The PPN and MLR send projections to the reticulospinal pathway, where they

alter the activity of locomotor oscillators (Garcia-Rill et al. 2014; Lee et al. 2014;

Moruzzi and Magoun 1949). But the same region is also part of the reticular activating system that can enhance overall arousal in the forebrain (Garcia-Rill et al. 2014;

Lee et al. 2014; Moruzzi and Magoun 1949). Autonomic functions as well as perceptual functions are adjusted at the same time to accommodate the needs of

locomotion.

In addition to the SNr output, the ventral tegmental area (VTA) also appears to

provide a critical BG output to locomotion-related body configuration controllers

(Swanson and Kalivas 2000; Wang and Tsien 2011). Common to all forms of locomotion is the alternating swing of the body. Locomotion involves not only alternating rhythms in limb movements (e.g., extensor/flexor alternation), but also left right

alternation in the spine configuration in locomotion. Whereas the MLR appears to

be more important for regulation of limb joint angle, the PPN and other related

regions could be critical for controlling torso joint angles, e.g., bending the spine.

The BG are not responsible for generating locomotor rhythm per se, which

depend on lower levels of the hierarchy. But the BG output can determine the rate



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of change in body configurations. In locomotion, the rate of propulsion is proportional to the rate of change in the configuration reference signal.



20.4.5



Orientation Control and the Tectum



Neural position controllers differ in how they sense position. Proprioceptive sensors

from muscles and joints report the state of the body. Senses such as vision and hearing are used to report distal changes in the environment, and the relevant receptors

must be moved toward the relevant part of the environment. This type of signal

acquisition is equivalent to orientation control. Orientation is not a property of the

organism only, but of the relationship between the organism and its external environment. The levels below orientation control cannot act on the environment in any

directed way. Devoid of any distal senses, one cannot move towards some distal

target.

The key neural structure for orientation control is the tectum, a large region in the

midbrain that receives visual, auditory, and somatosensory inputs. Indeed, in some

organisms the primary visual analysis occurs at the level of the tectum (superior

colliculus), which enables adaptive behaviors such as striking at prey (Lettvin et al.

1959). Signals from each tectal sensory map converge on the comparator. The error

signal from the comparator, in turn, has access to a number of body configuration

controllers. The error signal is transformed into the position reference signals that

ultimately specify the movements. For example, to foveate on a moving target, one

can move the eyes, the head, or the whole body. Any of these can acquire the visual

input needed, thus achieving the orientation of “straight ahead,” and normally all

can be engaged at the same time. The head will turn if the body is restrained, and the

eyes will move if the head is restrained.

Foveation is just another example of the control of input. The photoreceptors are

organized retinotopically, and moving up the neural hierarchy this organization is

retained at multiple levels, including the superficial layer of the superior colliculus

(Drager and Hubel 1976; Robinson 1972). Activity of each unit on this map can

have a certain range of values. To orient the sensory receptors towards the distal

stimulus, the value of the relevant units on the map can be matched with a reference

value. Thus the sensory signal acquired at each map location is dictated by the reference. This is effective not only for foveation, which keeps a high level of activation

roughly in the “center” of the visual field, but for other types of behaviors, e.g.,

grasping or whisking, that rely on different sensory receptors.

The high firing rates on a map of low firing rates represent the reference location,

the default setting which allows the eyes to be “centered” at rest. Movements will

be produced to reduce the error until each unit returns to the baseline level specified

by the reference for peripheral units. But as soon as the position changes to the

receptive field of the neighboring unit, that unit too will generate a direction-specific

movement to reduce its error, and so forth until the foveation units are reached.

These units have different reference signals, so that the high perceptual input no

longer creates an error signal.



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The tectal orientation controller can function in two modes. In the bottom-up

mode, any salient stimulus can trigger an orienting movement. Pavlov called this the

“what is it reflex,” whose primary function is to orient one towards changes in the

environment, in preparation for possible behavioral engagement, whether to

approach, avoid, or ignore (Konorski 1967). In the top-down mode, the animal can

select any part of its perceptual field and orient towards it, regardless of its salience.



20.4.6



Nigrotectal Projections Send Descending Reference

Signals



On the tectal map, activation of any unit above its reference level will generate an

error signal from the comparator, resulting in movement. In foveating animals, it

appears that high level of activation is allowed only in the rostral fovea representation, as a result of topographical differences in the reference signals received. To

break foveation, the higher level must reset the reference for the periphery. For

“voluntary” saccades, neurons are activated in a region that represents the location

to which the saccade will be directed. Just prior to a saccade, activity rapidly builds

up at the target location, reflecting the reference signal sent to the chosen location in

the peripheral visual space that is not currently foveated. Descending projections to

the tectum select which part of the map is to be activated.

A major source of descending reference signals is the BG, via the massive nigrotectal projections. The SNr sends direct GABAergic projections to the tectum

(Beckstead 1983; Redgrave et al. 1992; Rinvik et al. 1976). The nigrotectal projection is critical for self-initiated or memory-guided saccades (Hikosaka et al. 2000).

When the monkey must move its eyes towards some arbitrary target, SNr neurons

pause transiently, whereas their target neurons in the intermediate layers of the

superior colliculus burst. The intermediate layer receives two sets of inputs, glutamatergic inputs from the superficial layers, where the visual inputs arrive, and

GABAergic inputs from the SNr, which represent descending reference signals

from the BG (Isa and Hall 2009). Thus these neurons can implement a comparator

function to generate the difference between reference and input.



20.4.7



Turning and Steering



Orientation control is critical for steering during locomotion. Unilateral striatal

stimulation produces muscle contraction on the contralateral side of the body.

Unilateral striatal (caudate) lesions produce a posture in which neck and body are

curved towards the side of the lesion. The laterally curved posture results from an

imbalance of the activities of the two striata, and the animal turns away from the

side of the greater activity, presumably due to contralateral contraction and ipsilateral relaxation of different muscle groups along the spine (Ferrier 1876; Jung and

Hassler 1960).



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We assume that leftward modules are mainly located in the right striatum, and

rightward modules in the left striatum. Balanced outputs from both striata are needed

for moving straight ahead. Thus, the net effect of stimulation on the right side will be

leftward turning and vice versa. Unilateral muscimol injections into the SNr, by activating GABA-A receptors, can mimic the effects of unilateral striatonigral transmission (Cools 1985). Cools found that the animal repeatedly attempted to bridge the gap

between the current position and some egocentrically defined point. The fully symmetric posture is the point of departure, or neutral position, for the drug-induced

movements. While the initial turning movements are restricted to that of the head,

with time there is progressively more movements with different points of departure

moves from the ears to the eyes, to the midline of the head, and then more caudally,

from the head to the tail (oculus, auriculum, cranium, scapula, and pelvis). The point

of departure lies on the vertical axis of the egocentric coordinate system. These are the

locations where the descending reference signals can alter the body configuration.



20.5



Transition Control



According to the present model, the BG implement transition control. By sending

reference signals to position controllers, the BG can reach some desired rate of

change (transition) in different perceptual variables. This insight was anticipated by

Cools, who speculated that the inhibitory output from the SNr represents reference

signals, which contain a “propriotopic code,” and that the BG circuit could implement transition control (Cools 1985). But lacking knowledge of the computational

roles of striatal and nigral neurons and the neural integrator needed to convert velocity error into position reference, Cools could not provide a model of how transition

control is implemented.

The results discussed earlier, which reveal representation of movement kinematics by BG neurons, suggest a control system for movement velocity, in which the

rate of change in body configurations is a controlled variable (Yin 2014a). In a

velocity controller, the actual velocity will match the desired velocity.

If we only cared about start and end positions, then position control would be

adequate, and the speed with which the position is altered is determined largely by

loop gain. Given a specific amount of position error, the speed cannot be varied and

the movements may appear jerky. But if we wish to vary how quickly this transition

occurs, it is important to control the rate of change itself.

Velocity control is critical for smooth motion. To achieve it, it is necessary to

sense the rate of change in body configurations. For example, in an engineered speed

controller, the rotation speed of the motor can be sensed, and this sensed value is

compared with the reference to calculate the error signal. Any deviation is used to

generate the output. In a biological organism, there are proprioceptive inputs that sense

the rate of transitions (Yin 2014a). Movement velocity in this sense is independent



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of movement in space. On the other hand, rate of change in exteroceptive inputs,

such as optic flow, is detected and controlled by a different system (see below).



20.5.1



Cascade Organization and Velocity Control



Engineered velocity controllers and position controllers are similar in design, with

the key difference being how the output is measured or transduced. For example, a

potentiometer changes its voltage output proportional to the position moved,

whereas a tachometer generates a voltage proportional to the rotational speed of the

motor. In a biological organism, these are achieved by the perceptual inputs at different levels of the hierarchy. New properties emerge when a velocity controller is

placed just above a position controller, i.e., arranged hierarchically. This arrangement is similar to a common engineering design called cascade control, with the

higher controller called the inner or master loop and the lower controller the outer

or slave loop.

Suppose we have a position controller with three positions: 1, 2, and 3. A reference signal of 3 is a command to sense the position 3. If the reference signal is fixed

at 3, this position controller will maintain position 3, producing variable outputs as

needed to resist the effect of environmental disturbance. To the casual observer,

however, there is no movement whatsoever. If we plot position (a string of 3 s) over

time, we would see a flat line. If we change the reference to 2—this command tells

the position controller to bring the reading of its position transducer to the new reference value. But to control how quickly the value changes from 3 to 2, velocity is

needed. In the cascade organization, the velocity loop is placed just above the position loop, so that the error in the velocity controller becomes the reference for the

position controller (Fig. 20.7). For this to be possible, the error of the velocity

controller must be converted to the rate of change in the position reference. That is

to say, integration is needed.

The neural implementation of movement velocity control is the sensorimotor

cortico-BG network. As reviewed earlier, striatal activity is related to velocity

whereas nigral activity is related to position. The present hypothesis is that striatal

output reflects the velocity error signal, which is integrated by the SNr. Thus the

velocity error is converted to a rate of change for the position reference, regardless

of the start or end position.

Integrators are common in negative feedback systems. When “integral gain” is

used in the output function, the output is proportional to the time integral of the

error signal. Output can be produced even when the current error is zero, maintaining a steady reference command to downstream systems. With no leak or discharging, the amount of output at any given time reflects total error accumulated

in the past. In principle, the steady state error of this system can be zero and the

loop gain infinite.



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Fig. 20.7 Relationship between velocity control and position control. (a) An illustration of the

hierarchical relationship between a velocity (transition) controller and a position controller. The

velocity error is integrated to yield position reference. The rate of change in the BG output therefore reflects movement velocity, while the firing rate itself represents position. (b) Illustration of

charging and discharging the integrator. The lever holding task (Fig. 20.2) is used as an example.

On the left are the velocity errors from the striatum that are assumed to enter the integrator, and on

the right is the position reference from the SNr



When integral gain is used in the transition controller, the latest position achieved

is maintained. The BG output nuclei, e.g., the SNr, are characterized by high tonic

firing rate, which can be explained by membrane properties of the GABAergic projection neurons (Zhou and Lee 2011). But functionally the significance of such high



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firing rates has never been clear. According to the present model, this is because SNr

output represents a position vector. To maintain position, the SNr output neurons

continue to fire at the same rates (Fig. 20.2). When the firing rates change, a new

position is achieved. The dynamic range of the neural signaling reflects the range of

egocentric position coordinates, and maximum movement amplitude.



20.5.2



Direct and Indirect Pathways



An important feature of the BG is the parallel direct (striatonigral) and indirect

(striatopallidal) pathways (Gerfen 1992; Parent and Hazrati 1995). Although this

anatomical feature remains controversial, there is general consensus that these two

pathways have opponent effects on the SNr output neurons, via direct inhibition and

indirect disinhibition.

One possibility is that the direct/indirect organization implements a phase splitter. In a phase splitter, a common signal enters the circuit, and two opposite signals

are generated. One increases as the other decreases, creating antiphase signals.

Because the direct and indirect neurons share largely overlapping inputs, they are

expected to be activated simultaneously by cortical and thalamic input, as supported

by recent results (Cui et al. 2013; Isomura et al. 2013; Tecuapetla et al. 2014). There

must be concurrent activation in order to generate opponent effects on the BG output, i.e., the inhibitory effect of the direct pathway and the disinhibitory effect of the

indirect pathway on the SNr output can create antiphase signals. Because the nigral

neurons influenced by these two pathways typically show tonically high firing rates,

both increases and decreases from the baseline are possible.

Opponent or antiphase signals are needed for the control of opponent downstream controllers. The reciprocal inhibition organization in the spinal cord, for

example, acts as a phase splitter of the length error signal (McDougall 1903;

Sherrington 1906). For example, to lift a dumbbell (reduce the joint angle at the

elbow) one can contract the biceps while relaxing the triceps. On the other hand,

opponent BG outputs are required to command distinct position controllers that

move parts of the body in different directions along a particular axis of motion. The

antagonism is not between antagonistic muscles, but between directions of

movement.

The question is whether these two pathways act on the same SNr output neurons.

If they reach distinct SNr populations, then the two populations of SNr neurons with

antiphase signals can be explained by the opponent inputs (inhibition and disinhibition) from the direct and indirect pathways. Another possibility is that the opponent

SNr neurons are mutually inhibitory via collaterals from their axons. This arrangement can also produce antiphase signals, without relying on a phase splitter upstream

of the SNr. These possibilities are not mutually exclusive. In addition, the “hyperdirect pathway” can also increase the output of the SNr, via glutamatergic inputs from

the subthalamic nucleus (Nambu et al. 2002), and the net effect is the opposite of

that of the direct pathway.



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According to the traditional model, direct pathway activation promotes movement, whereas indirect pathway activation inhibits movement (Freeze et al. 2013;

Hikosaka et al. 2000; Kravitz et al. 2010). This interpretation, however, is inadequate in view of the relationship between neural activity and kinematics discussed

earlier. Let us consider the direct pathway first. Striatonigral neurons are not identical. Different classes, belonging to distinct velocity controllers, are involved in

movements in different directions. For antagonistic directions (e.g., up and down)

one population must increase firing as the antagonistic population stops firing.

Therefore it is simplistic to conclude that the direct pathway neurons uniformly

promote movement. Some of them promote movement in some direction, but if the

antagonistic units are activated, then the movements will be stopped.

The role of the striatopallidal neurons is more difficult to understand. One important clue comes from observations on turning behavior after unilateral striatal

manipulations. Increasing striatal output on one side produces contraversive turning, largely due to activation of the direct pathway, because unilateral optogenetic

direct pathway stimulation mimics the effect of nonselective unilateral striatal stimulation. By contrast, indirect pathway stimulation produces the opposite effect of

ipsiversive turning, though this effect is usually weaker (Kravitz et al. 2010;

Tecuapetla et al. 2014). This observation suggests that the effect of striatopallidal

activation is indeed the opposite of striatonigral activation, but the opposition is

between two directions of movement.

As described above, a given velocity controller is responsible for motion in one

direction only and contains an integrator in its output function. The accumulation of

signals in this integrator will therefore produce motion in one direction. To get

motion in the opposite direction, the integrator must be “discharged,” and another

antagonistic velocity controller must be activated. The discharging of the integrator

requires an error signal with the opposite sign (e.g., a leak in the bucket), which is

exactly what a phase splitter can provide. Thus, one possibility is that the direct

pathway initiates the action and the indirect pathway serves as a brake by introducing a leak in the integrator. The amount of leak can be independently controlled. The

more leak there is, the more damped the system will be. More “ballistic” actions

will involve less leak. The presence of highly plastic striatonigral axonal collaterals

that target the external globus pallidus suggests a mechanism for adjusting the

damping of the system (Cazorla et al. 2014).

There are only a few neutral postures that are specified by innate reference settings. These neutral positions often involve body symmetry, whereas movement

requires a transient break in symmetry. Most movements are transient and cyclical,

involving a return to the resting neutral position. The return back to the original

position also requires a discharging of the integrator with the opposite error. Both

the indirect and hyperdirect pathways are capable of introducing the opposite error

(or leak) to the integrator.

In the sensorimotor striatum, a given striatal module generates a set of signals

that will move some body part in a specific direction with a specific speed. Such a

module could include a set of striatonigral units and striatopallidal units that are also

activated by the same reference signal for leftward motion. As pointed out above,



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this module is found in the right striatum. In this module, the firing rates of both

striatonigral and striatopallidal neurons are both correlated with leftward velocity,

yet their ultimate effects on the BG output, and on behavior, are different. The direct

pathway moves the body to the left, accumulating signals in the leftward integrator,

whereas the indirect pathway discharges the integrator and brings this movement to

a stop. It was proposed long ago that these pathways can scale movement amplitude

and speed (DeLong 1990), but the original proposal confounded two very different

variables and failed to take into account the key property of direction specificity.

Why should unilateral stimulation of the indirect pathway produce ipsiversive

movement? According to the present model, to turn left, the leftward module must

be turned on, and the rightward module suppressed. There is therefore antagonism

between these modules. At the same time, within the same module there is antagonism between direct and indirect pathway neurons, because the striatonigral neurons accelerates contraversive movement whereas the striatopallidal neurons

decelerates it, i.e., introduces acceleration in the ipsiversive direction. The deceleration, however, is usually dependent on the same reference command for contraversive motion, used to damp the movement as needed, and to return to the original

position. Normally the striatopallidal unit in this module cannot be activated in isolation, but selective optogenetic stimulation can reveal the acceleration in the opposite direction.



20.5.3



Role of Dopamine



Midbrain DA neurons project to most striatal regions. There are two major DA

pathways: the nigrostriatal pathway targets the dorsal striatum and the mesolimbic

pathway targets the ventral striatum and the prefrontal cortex. DA has the same

computational function in both pathways: it is hypothesized to adjust the gain of

striatal neurons. But the impact on behavior will differ depending on the neural

circuit affected and the types of variables being controlled. We will start by considering the nigrostriatal pathway, by far the dominant DA pathway.

DA modulates synaptic transmission. “Modulation” here does not simply mean

“change,” as used loosely in the physiological literature, but a multiplicative operation in the engineering sense. Nigrostriatal DA alters the responsiveness of dorsal

striatal neurons (Cepeda et al. 1993; Gerfen and Surmeier 2011; Hjelmstad 2004;

Yin and Lovinger 2006). It has opposite effects on striatonigral pathway neurons,

which express D1-type receptors, and striatopallidal neurons, which express D2

receptors (Gerfen and Surmeier 2011; Zhou and Lee 2011). By activating D1-like

receptors, DA increases the responsiveness of striatonigral neurons to glutamatergic

input, as well as its release of GABA at the axon terminal. By contrast, in the striatopallidal pathway, DA reduces the responsiveness of striatopallidal neurons to glutamatergic input, and reduces GABA release.

The known properties of BG synapses are in accord with the present model.

Striatonigral synapses are facilitating. Each additional presynaptic spike in a train



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produces a greater response postsynaptically. This pattern suggests nigral integration of the inhibitory input. On the other hand, pallidonigral synapses are depressing, suggesting integration of the excitatory (disinhibitory) input is possible

(Connelly et al. 2010; Zhou and Lee 2011). The nigrotectal synapse appears to be

neither facilitating nor depressing (Kaneda et al. 2008), so there does not appear to

be a neural integrator in the tectum. This arrangement avoids having two integrators

in the overall control loop, which can create undesirable oscillations.

SNr GABA neurons directly inhibit DA neurons in the SNc (Tepper and Lee

2007). Tonic firing of SNc DA neurons at a low rate is mediated by calcium entry

through voltage-gated calcium channels, while burst firing requires glutamatergic

inputs and the activation of NMDA glutamate receptors. In addition, reduced GABA

release can also generate burst firing, so that disinhibition can produce burst firing

in DA neurons (Kang and Kitai 1993; Paladini et al. 1999; Tepper et al. 1995). The

DA neurons are therefore in a position to take the derivative of the GABA outputs

from the BG. This is supported by the finding that, whereas most DA neurons are

correlated with velocity and acceleration, GABA neurons are correlated with instantaneous position coordinates (Barter et al. 2015b). Due to GABAergic inhibition of

DA neurons, the derivative of the GABA output is subtracted from the output of the

DA neurons. This organization suggests a mechanism for adaptive gain control, in

which the gain can vary according to the movement velocity. Anatomical studies

have shown that nigral output can disinhibit SNc DA neurons that projection back

to the striatum, thus forming a striatonigrostriatal loop (Haber et al. 2000). It could

allow the rate of change in one controlled transition to adjust the gain for the same

controller as well as a different controller.



20.5.4



Dopamine Depletion and Symptoms of Parkinson’s

Disease



The hypothesis that DA serves as a gain in the velocity controller sheds light on

common symptoms in movement disorders. PD is associated with degeneration of

the nigrostriatal DA pathway. A major consequence of DA depletion is bradykinesia

or slowness in movement. Indeed, 6-OHDA, a toxin that kills DA neurons, dosedependently slows down movement (Yin 2014a). Recent work also showed that DA

depletion resulted in bradykinesia and abolished striatal representation of velocity

(Panigrahi et al. 2015).

An important property of control system is that even a large reduction in gain

will not necessarily result in system failure. To estimate the value of the controlled

variable at steady state, we can use the equation:

p = r * g / ( g + 1))

where p is the input variable controlled (e.g., movement velocity), g is the loop gain,

and r is the reference. Assuming a loop gain of 100, then the controlled input



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