Wednesday August 31 2022: Phases of learning

About neural learning, neural systems should have different phases of learning. I would argue that the first phase has to be one where the energy (or information, or prediction error) imposes itself onto the energy state of the neural system. At this point, the neural system's encoding of this novel information is still only in terms of thermodynamics or flows of the received energy. We might observe this in the form of the neural system's post-synaptic potential and axonal spike activity. It is critical to note that changes in activity are by definition a dynamic concept. Thus, the information is not encoded in one static activity state. Rather, the complete information packet as it were must be represented by a window of dynamic activity fluctuations. Sustaining such a non-homeostatic state of activity is untenable in the long run. Therefore, this dynamic activity must be resolved in the neural system's architecture. Here, the novel information graduates from thermodynamics to physical structures. We might observe short- or long-term potentiation or depression, directed shaping of short and long-range connections characteristic of pruning and neural development. All these consume energy, and likely in a way that reflects the dynamic activity that is the packet of information received.

A necessary question is then what sort of information is received, and what sort of information is bypassed by neural systems. This consideration stems from the fact that the neural system cannot possibly encode all encountered fluctuations of energy in the environment. I would argue that the information received and not bypassed might be construed as information that is meaningful to the system. By meaningful, I mean that the information is auto-encoded or auto-regressive. That is, meaningful information is information that sufficiently maximizes the match between the prior state and the information.

Here, we can then formulate the problem as one in which there exists a prior state, novel information, and the posterior state which is the cross product of the two.

Engines in the brain

Motor actuation works via a pulse transferred to momentum force that is smeared over time. Efficient motors have a significant proportion of this smeared force extending the influence of the pulse by having a large part of the momentum dynamically adding to subsequent pulses. A dampening sine. A collection of dampening sines. A fractal collection of dampening sines.

The driver? Temperature and its local fluctuations establishing sufficient gradient crossings for stability. If entropy has increased for all temperatures, what happens?

Life and Least Action

 Intelligence: the shortest time for energy to transition between states.

Nuclear tug of war

 If you constrain the environment around an object such that the constraints threaten the object's integrity, the object (if it is to maintain its integrity) will find the path of least resistance to create an alternative route out of the constraints that will maintain its integrity. This is a tug-of-war of the nuclear forces. This involves the creation of dimensions in spactime.