The core, inviolable posit is that all biological computation is a convolution of a “more/less/modulate” basic function. That is a positive afferent, attractor, etc and a negative afferent, repeller, etc. I like to think of this as “want”/”don’t want”/something else, if you are a behaviorist you might like framing basic behavior down to “fight(more)” “flight(less)” “freeze(modulate)”. Psychologists might be familiar with the e/i model, etc all of these macro concepts are convolutions of the same core principle.
This model fails if we can demonstrate that behavior can occur without state change, that behavior takes place in a regime without these features, or behavior exists independently of brain function.
Remodeled brain relies heavily on the interpretation of “emergence” as defined by natural systems. This definition is that we can combine the functionality of two systems to create a new property, however that new property is a) completely dependent upon the constant state of it’s components, b) the combined property does not display mechanics which would violate the properties of it’s components. As an example, combining blue and red to make purple requires blue and red to exist in the same state in order for purple to exist. If either no longer exists, purple no longer exists. Water is dependent on both hydrogen and oxygen being bound into the state we call water, if we modify oxygen or hydrogen levels, the resulting emergent state must change with it. Emergent properties are a product of it’s components, and emergent properties are always reducible.
I recognize the similarity of this model to Qubits if one takes the position that “modulate” is equivalent to superposition. I don’t believe that this is inconsistent with the model, however I don’t make any assertions regarding quantum dynamics in biological calculation. That there is a similarity is an artifact of strictly adhering to existing observed natural laws, and if it can be demonstrated consistently would be a very welcome point of consilience.
Starting at the intra-cellular level, all biological computing requires external energy to facilitate it. This does not comment on whether something is “alive” in any particular state, only that for computation to occur (including reproduction), an organism must have an external energy source available. This is important because it establishes consistence with conservation of energy. This model is broken if we can demonstrate that conservation of energy is not a consistent property within life.
All biological computing must have a feedback mechanism, to achieve a homeostatic balance with it’s environment. Without a feedback mechanism, the computing essentially falls into a recursive loop which is capped by the maximum amount of energy available to the system. Feedback systems need not be endemic to the intra-cellular level, however it must exist at the systemic level.
The concept of “evolution” is our perception of the interplay between these feedback and feedforward systems, a competition between systems vying for the same energy resources. It is through iteration that organisms develop new ways to use energy, and those novel uses of energy drive the rich diversity of biological computing.
This model ties the definition of life to this process, that is any self-initiating system which is dependent upon external energy to modulate it’s own function.
This model does not assume it explains abiogenesis. However, based on evidence from protocellular research, we are able to initiate self-sustaining, self-replicating cells which are consistent with the properties laid out so far. With sufficient UV bombardment, variations in catalytic processes can appear, and those variations would provide a consistent platform for the evolution of life. As organisms developed new ways to utilize energy via these changes, each new utilization provided a niche for it to extend into without competing for the global energy pool. This ready supply of energy allowed a particular organism to extend it’s own variation up to the limit of the energy pool the variation allowed.
Eventually the ability to store these variations developed, and this initiated stable separation between classes of energy consumers. The stored complexity allowed for consistent iteration of a energy consumption paradigms, and this induced an explosion of complex features, and eventually multi-cellular life.
Life however is merely reactive and being able to adjust catalytic function to more efficiently utilize energy in these local pools began to appear. These functions had to determine whether or not it could utilize the energy source or not when in contact with it. If not, try another source. And from this basic question “Can I consume this energy?” the richness of biological computing began to develop.
Homeostasis was the first mechanism which allowed multi-cellular organisms to work together. By establishing a consistent push and pull, they could transfer a signal to other cells to do more or less of something. Each cell was a functional copy, and this simple mechanic allowed a sophisticated reaction to occur based on the complexity of the cell. New modalities began to appear which took advantage of this complexity. Push and pull allowed sharing of resources, along with an increase in computational complexity as gradients of behavior throughout the organism became available.
For organisms which are able to directly tap endemic energy, this process was fairly slow and limited to relatively basic calculations. For the energy vampire class however, the ability to calculate became critical. Not only did it need to worry about whether or not it could consume a particular energy, it had to be concerned with can it be consumed? And this need for calculation drove the explosive diversity of animal species compared to bacteria (which catalyze environmental resources) or plants (photosynthesis).
Copying functionality throughout all cells is really expensive however, and specialization began to appear. This specialization of cells brought new types of calculation, and ultimately the brains we humans use today.
So that’s a brief history of life up to now.
It is important to understand where life came from because everything we imagine about ourselves is a product of this exact same process. Whenever we imagine whether or not humans have processes which are wholly unique and distinct, the answer is always no. At some level of reduction, all units of life follow the same basic principles.
So let’s zoom back out a bit and sit in our anthropocentric happy place for a second. Human brains work primarily via homeostatic balance. They induce changes in this balance among specific cells which results in what we observe as behavior. The complexity of these subsystems is directly analogous to the complexity of observed behavior. Behavior is a product of a macro homeostatic balance first, then clusters of operation on each side of that balance. As an example our brains at the top level want to make a “yes/no” decision, but that decision is supported by major decision centers (e.g. the amygdala complex for evaluating external information) that generate a yes/no gradient, which are themselves comprised of sub clusters with specific effect (e.g. reaction to novelty in the evaluated information) like the basolateral nuclei, which also produces a yes/no gradient, which is segregated further into specific neuronal clusters which evaluate specific elements of novelty, and so on down to the individual cellular level. The most complex calculation any particular cell makes however is “more/less/modulate”.
By understanding the more/less/modulate requirement of specific functional groups (or circuits), we can build a more complete description of brain function that is consistent throughout all animals and understands that heterogeneity of function isn’t a feature of biological calculation. Even the most unique behavioral presentation is ultimately derived from the same processes that govern life as a whole. This helps us understand complex human idiots like “There’s a thin line between love and hate”, or the “irrationality” of outgroup behavior. It provides context to why “consciousness” is an artifact of computation (because it is constructed of cellular yes/no’s which affect the computation of the level above it), rather than “consciousness” being a driver of these cellular level m/l/m processes.
This model provides that all behavior follows a consistent pattern, and is ultimately reducible to it’s constituent components.