Reticular Activation system - the brain's 'trim' or 'default' mechanism
In aircraft design, there are both primary and secondary secondary flight control surfaces. The primary FCS's are the rudder (yaw control), elevators (pitch control) and ailerons (roll control). But each primary FCS has a secondary FCS attached to it, which act to reduce or eliminate the need to place pressure on the yoke or rudder to keep your airplane flying straight and level (see Figure B.1). They have the physical appearance of miniature FCS's within the major FCS. The reason they are part of this discussion is that the brain (which is the controller of the body's motions) must also solve the same problem- how to maintain 'straight and level' body motions without requiring extra attention from the pilot. The parts of the brain which perform this same function are the Ascending and Descending branches of the Reticular Activation System (ARAS and DRAS). To draw an analogy with aeronautical engineering, the RAS manages the 'trim tab' settings, ie the default positions of all the control surfaces such as the rudder, elevators and ailerons. These settings determine where the aircraft's natural stability tendencies must yield to deliberate inputs from the pilot.
We can understand RAS function at a deeper level by extending this aviation metaphor to our brain-body relationship. From the GOLEM solution to Libet's paradox, it was demonstrated that consciousness' natural stability tendencies are determined by two variables (i) the point at which our involuntary reflexes yield to the deliberate (voluntary) desires or wishes of the self, and (ii) the point at which our subconscious perceptions become noticed by the self, enter into the realm of conscious awareness -see Figure B.2 below.
Figure B.1
The Reticular Activation System, or RAS, is a mesh of neurons whose somas wrap around the brain stem, but whose axons extend all over the brain. Judging by their ubiquitous placement, they must have an important function. TDE/GOLEM Theory is particularly successful in its explanation of RAS functionality, because of its neocybernetic foundations. Recall that in neocybernetics, the familiar homeostatic setpoints are augmented by heterodynamic offsets. This is the way that the brain uses an originally static mechanism to create dynamic activities. Though it is an elegant solution with obvious benefits, it also has costs. One such problem with the setpoint+offset combination is that there must be some way for the brain to remember the boundaries where setpoints stop and offsets start. This mnemonic task is the job of the RAS, expressed in neocybernetic terms [5].
The RAS somas (cell nuclei) are located on or about the brain stem, but their axons distribute neuroactivation (usually excitatory) to almost all parts of the cerebrum, as well as to some sub-cerebral nuclei. There are two parts of the RAS, the ascending ARAS and the descending DRAS.
(i) The ARAS produces consciousness, because ablating the ARAS destroys the ability to be conscious.
(ii) The DRAS produces unconsciousness, because ablating the DRAS destroys the ability to sleep.
Figure B.2
Lets examine these two observations in greater detail. First, recall our definition of neocybernetics as a governance regime which generates dynamics by semantic grounding of statics, ie by addition of feedforward offsets to feedback setpoints.
In the brain, the ARAS manages the sensor-side thresholds, that is, the input stimuli levels at which they are noticed by the subject. If a sensory stimulus exceeds this threshold (it varies with location and function), the organism then 'pays attention' to it, and depending on its potential importance, it may respond behaviourally. As figure B.2 shows, the ARAS levels also control the level that peripheral stimuli can arouse the central part of the nervous system, therefore the ARAS controls waking.
In a similar manner, the DRAS manages the motor-side thresholds, that is, the levels that motor signals cease to be handled automatically. For a motoneuron's stimulus to exceed this threshold, a volitional [3] input, a.k.a. a 'command' is needed, ie the organism must voluntarily 'wish it so'. This boundary denotes the transition from high-level, top-down, declarative (goal-oriented) computation to low-level, bottom-up, procedural (instruction/script-based) computation. It also controls the level below which sleep paralysis (an automatic procedure) can take effect, therefore the DRAS controls sleep.
These transitions are depicted in Figure B.2 above and also in Figure B.3 below. Figure B.3 includes a depiction of the GOLEM view of cerebellar function, like some other complete MOM's that preceded it [6].
At sensorimotor level-1 of the hierarchy, the management of sensory and motor thresholds, as depicted in these diagrams, is similar but not identical, since level-1 corresponds to the cerebellum and basal ganglia in the TDE/GOLEM brain plan [4]. However, at spatiotemporal level-2, any differences tend to be swamped by the similarities in the two increasingly identical sets of cortical neurons. It is therefore not surprising that the ARAS and DRAS exert dual control over the two groups of cortical neurons at level 2 in the hierarchy.
(a) (b)
Figure B.3
Figure B.4(a) depicts a typical level-1 (sensorimotor) sub-system, namely the biceps muscle and its associated nerves. Together with the familiar motoneuron (red) and muscle stretch sensor neuron (blue) are the command neuron (yellow) whose axon terminates in the same spinal ganglion as the motoneuron's soma and sensor neuron's axon terminus or button. The method by which excitatory command signals are able to cause muscle contraction are well documented in other sections, and explained by Feldman [5]. What concerns us here are the 'bias' neurons. There is a green bias neuron for the motoneuron and a purple coloured bias neuron for the stretch sensor neuron. These 'bias' (or learning) neurons, which express the neuroplastic changes in the peripheral nervous system (PNS), connect to input 'e' in the neurostat depicted in diagram B.4(b).
The RAS maintains the bias neurons' 'default' or 'background' activation levels.
(i) The role of the ARAS is to maintain the tonic signal level in the sensor neuron's bias neuron inputs at sensorimotor level-1. [7]
(ii) The role of the DRAS is to maintain the tonic signal level in the motoneuron's bias neuron inputs at sensorimotor level-1.
The key feature of these interacting circuits is their ability to do the other's job, since they both affect the same underlying sub-system, in this case the biceps muscle and elbow joint flexure. The ARAS can increase sensor bias activation levels, via the descending analgesic pathways or equivalently, the DRAS can increase motoneuron bias activation levels via the reticulospinal tracts. The net effect is almost identical. The differences between the sensor-side and motor-side bias activations are the key to the brain's ability to learn skills (low-level) and form new memories (high-level).
Figure B.4
2. Larkum, M.E., Petro, L.S., Sachdev, R. and Muckli, L. (2018) A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements
3. volition is also called 'conation'. The basic idea is simple, that of a 'top-down' command, equivalent in most situations to a 'desire' or 'goal'.
4. The cerebellum is anatomically, but not functionally, separate from the basal ganglia.
5. Futurist/ Designer Buckminster Fuller is often cited for his use of trim tabs as a metaphor for the way that just one individual can make a difference to a whole society, by 'standing out against the mainstream'.
6. Houk, J.C. Buckingham, J.T. Barto, A.G. (1996) Models of Cerebellum & Motor Learning. Behavioural & Brain Sciences includes models by, i.a.-
Florens (1824)
Braintenberg & Atwood (1958)
Ito (1969)
Marr, D. (1969)
Albus, J. (1971)
Anderson, J.A.
Houk et al makes the same mistake as all the others- of assuming that the Purkinje-cell (P-cell) circuits of the cerebellum are tasked with motor learning. GOLEM theory demonstrates that the P-cells of the cerebellum animate spatiotemporal trajectories in cerebral space, one P-cell per animated keyframe. For example, the climbing fibers fire when each keyframe target is visually acquired. They have nothing to do with errors or learning.
7. My view of RAS function has advanced significantly since https://brainsofsand.webnode.com/copy-of-c/