How The Spinal Cord Generates Behavior

This article has some really interesting ideas. However it is long and of intermediate difficulty (running 20 pages of text), so I’ve done a bit of editing to extract only the finest sections from the morass of words and ideas. I’ve also chosen to focus on the spinal cord, at the expense of the other brain areas.

How Basal Ganglia Outputs Generate Behavior

Henry H. Yin (2014)
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I introduce a new model, based on the insight that behavior is a product of closed loop negative feedback control using internal reference signals rather than sensorimotor transformations. The nervous system is shown to be a functional hierarchy comprising independent controllers occupying different levels, each level controlling specific variables derived from its perceptual inputs.

When someone is standing, to the casual observer
there appears to be no behavior. But this appearance is
misleading. Any perturbation, such as a push, is met with
resistance from the organism. Not only a push, but invisible
and unpredictable disturbances everywhere—gravity, wind,
changes in effector properties such as the spring properties
of the muscles. These disturbances must be overcome, by
varying output. This is an example of position control.

The term “control” means that posture stays the same,
despite environmental disturbances. The na ̈ıve assumption
that whatever neural signals are sent to our muscles deter-
mine the effects we exert on the environment, that is,
observable behavior, was demolished by Bernstein nearly a
century ago [47]. Bernstein wrote: “There are no situations
in which muscle shortening is the cause of a movement”
[48]. The actual effect of the muscular contraction is not the
product of our neural output. Behavior can never be equated
with the output of the nervous system, because it is the joint
product of unknown environmental influences and neural
signals. To the motor neurons producing muscle contraction,
even fatigue or slight changes in the properties of the muscles
can become a major source of disturbance. Consequently, a
measure of muscle contraction (e.g., electromyography) can
never define the actual behavior or the posture. That the
output does not equal behavior raises the question of how the
neural output can be adjusted as unknown and unpredictable
disturbances vary. This is the “calculation problem,” the key
problem that the nervous system must solve [49].

It is often believed that the calculation problem can be
solved by computing inverse kinematics and dynamics or
by feedforward computation to predict the future effects
of actions using sophisticated mathematics. If only we can
calculate the needed force output, it would be possible to
produce movements [50, 51]. This feedforward approach
requires enormous computational power and completely
accurate knowledge of the physical interactions in the envi-
ronment, if not omniscience. This is never found in any
biological organism. Yet the calculation problem, after all, is
solved by virtually all organisms. The solution is closed loop
negative feedback, the only known organization to reduce
error between the desired and the actual.

In the end, the appropriate output signals must be com-
puted somehow. The question is how. The negative feedback
organization simply eliminates the effects of disturbance by
subtracting them from the internal reference. The effect of its
own output is monitored with its own sensors and actively
controlled. This elegant solution to the calculation problem
avoids calculations on the disturbances in advance. Whatever
their effects, they are simply rejected by the negative feedback.
The inverse kinematics and dynamics are realized by the
physical interaction between organism and environment, in
the forward equations describing how muscle contractions
interact with the external environment. None of these calcu-
lations are performed inside the nervous system.

In the present model, a change in
body position is produced by changing the reference signal
of the position controller. Instead of a user injecting this
reference signal, as in adjusting the temperature setting of a
thermostat, it must come from within the organism.

Where then does the reference signal come from? The
answer is suggested by cascade control or hierarchical per-
ceptual control [58], in which the reference signal comes from
the output of another controller. Thus there is a hierarchical
relationship between the higher controller that sends the
reference and the lower controller that receives it, much as
an order is given in a chain of command.

At every level of the hierarchy, only inputs can be
controlled. When the output of a control system serves as the
reference signal of another control system, it does not specify
the output of the lower system, but its input. Altering the
output directly without altering the reference would affect the
controlled variable via the feedback path, creating error that
would cancel the effect of the output. Outputs from higher
levels determine the type of perceptions the lower levels
should achieve [58]. The lower controller will vary its output
to produce the input determined by the descending reference
signal, serving as an extension of the output function of
higher levels (Figure 5).

If the reference signal of the posture control system is
altered, the current posture will not be defended. Rather the
system will defend the new value of the reference signal at any
moment. There will then be a transition from the old posture
to new posture, a movement.

The nervous system comprises a hierarchy of negative
feedback control systems, each controlling its own perceptual
input [58]. The higher systems do not have direct access
to the actual actions or most of the perceptual inputs and
error signals from lower levels. It only senses the variable to
be controlled and generates error signals which become the
reference signals for lower levels.

The lowest level of the neural hierarchy controls muscle
tension. The output function of this controller is the muscle.
Projections from alpha motor neuron to muscle fibers send
error signals in the tension controller [7]. The alpha motor
neuron, as a comparator, receives signals from multiple
sources. The major source of negative feedback is the Golgi
tendon organ, which detects muscle tension produced by
contraction of extrafusal fibers. The tension signal is fed
back to the alpha motor neuron through the inhibitory Ib
interneuron that inverts the sign of the signal, so that it
is the opposite of the excitatory Ia afferent to the alpha
motor neurons. This inversion creates negative feedback, as
the inhibitory effect is subtracted from the excitatory effect.
When the muscle contracts, the negative feedback keeps
the tension in check. This is traditionally called an inverse
myotatic reflex or the Golgi tendon reflex. The contraction
creates the feedback, which restricts the contraction.

On the other hand, muscle length itself can be controlled
independently while tension varies. The relationship between
length and tension is hierarchical. The higher length level
specifies the tension to be reached. Tension can be varied
to maintain a desired length. The difference between desired
length and actual length, the error in length control, is turned
into a reference signal to the tension controller.

The so-called myotatic or kneejerk reflex is a type of
stretch reflex, in which the lengthening of the muscle is
resisted by muscle contraction and shortening. This phe-
nomenon reflects the action of a muscle length controller.
A major signal driving the alpha motor neuron (and hence
contraction of extrafusal muscle fibers) comes from the
Ia afferent. This signal is often interpreted as representing
muscle length. But the Ia afferent signal can be independent
of muscle length. When the extrafusal muscle fibers are
stretched, the parallel muscle spindle, a stretch sensor, is also
stretched and activates the alpha motor neuron (i.e., stretch
reflex). But the Ia afferent can also generate a signal as a
result of gamma motor neuron output, which activates the
contractile part of the spindle, thus “simulating” a stretch.
To the alpha motor neuron, it does not matter how the Ia
afferent signal is produced, by actual stretch or by gamma
activation. The function of the gamma mechanism is not to
keep the spindle taut and maintain sensitivity to changes in
muscle length, as described in textbooks [26]. Rather the
arrangement produces a comparison between current muscle
length (via Ia and II fibers) and the length “demanded” by the
reference signals from the gamma motor neuron. The muscle
spindle does not directly contribute to the generation of
muscle tension but functions as a mechanical comparator of
desired and actual muscle length signals. The Ia afferent thus
carries an error signal for the length controller, which in turn
activates the alpha motor neurons and generates shortening
of the extrafusal muscle fibers and muscle tension.

According to the model presented here, the length
controller achieves control of desired length specified by
the gamma motor neurons by varying the reference signal
to the tension controller, which varies muscle tension as
needed. Tension control at the lowest level is always used
for posture control and all other behaviors, but tension
is not the controlled variable of the higher levels, which
achieve their respective purposes by varying reference signals
for tension. The higher levels all adjust muscle tension
ultimately but not directly. Directly they all attempt to control
their own respective perceptual variables, whether muscle
length or joint angle.

Figure 5: Illustration of cascade control proposed here. Two closed loop negative feedback control systems arranged hierarchically. Note that what is controlled is the perceptual variable, and the reference signal always comes from within the organism. In a hierarchy the higher level can adjust the reference of a lower level by sending a projection to the comparator function of the latter [58].

it is above all necessary to understand what behavior is. Here the
traditional linear causation paradigm is the greatest obstacle
to progress. Whenever behavior is conceived as the output of
some input/output system with linear causation, as the result
of sensorimotor transformation in multiple steps inside the
organism, the attempt to understand its neural substrates is
doomed at the outset.

I have argued instead that behavior is the outward mani-
festation of a more fundamental process of control, generated
by a hierarchy of negative feedback control systems, each
controlling its own perceptual inputs by varying outputs.
It is not the result of sensorimotor transformations but is
jointly determined by the perceptual input and the internal
reference signal, in a mathematically precise way. Using
cascade control, the output of a particular level specifies
the input signal to be obtained by level immediately below.
The loop is closed in the environment, as the output func-
tion of the lowest level in the hierarchy—muscles—acts on
the environment to generate behavior. Although the basic
unit of neural function—the closed loop negative feedback
circuit—is simple, a hierarchy of these systems can generate
exceedingly complex behavior. We are only now beginning to
understand the properties of the control hierarchy.

The properties of negative feedback control systems are
counterintuitive from the perspective of the linear causation
paradigm. The striking failure to understand control theory
in the life sciences so far only illustrates the fundamental
difference between closed loop systems and input/output
systems. Regardless of how many intervening variables
are inserted between the stimulus and the response, an
input/output system always lacks internal references, which
are only found in negative feedback control systems. This is
the crucial difference. The behavior of control systems is not
caused by what happens to them. It can never be a function
of inputs received or of internal representations of any kind.

For any control systems to function, reference signals are
necessary, and negative feedback makes it possible to obtain
inputs matching the reference by reducing the discrepancy
between the two. The reference signal is the representation of
some unrealized future state, but the system makes it possible
for this state to be realized by varying its behavior. In this
sense, the reference is simply the purpose of the controller,
though purposes and goals in ordinary language usually
refer to higher level reference signals at the transition level
because few lower reference signals are available to conscious
awareness. We are not aware of the reference signal for muscle
tension in hundreds of muscles in the body at any moment,
though these are the signals that ultimately close the loop by
causing muscle contraction to act on the environment. We are
usually aware of the higher goals of our actions, the reference
signals sent to the transition level, for example, to get a cup
of coffee. The higher purpose is achieved by elaborations as
one descends the hierarchy; for example, the desire to get
coffee affects the reference signal for sequence control of the
action, which changes the reference for rate of change in body
configurations, which then alters references for joint angles,
which then alters references for muscle length, which finally
alters references for muscle tension.

I have identified the neural implementations of the basic
levels of the hierarchy: muscle tension, muscle length, joint
angle, body configuration and orientation, and transition. In
the proposed neural hierarchy, the BG occupy the highest
level, receiving inputs representing rate of change in different
perceptual variables, comparing these signals with reference
signals, and generating error signals that alter the reference
signals for downstream position controllers. Such a model
suggests a new view of the relationship between the inputs
and outputs. The BG are neither sensory nor motor. Rather
their function is to control certain types of higher order
perceptual variables, above all relationship, sequence, and