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Because there’s a version of specialization that is, “different regions are specialized but they all seem to build consensus” and there’s a version that is “different regions are specialized and consensus does not seem to be necessary or potentially even usual or possible.”

These offer very different interpretations of cognition and behavior, and the split brain experiments point toward the latter.




Functional specialization > Major theories of the brain> Modularity or/and Distributive processing: https://en.wikipedia.org/wiki/Functional_specialization_(bra... :

> Modularity: [...] The difficulty with this theory is that in typical non-lesioned subjects, locations within the brain anatomy are similar but not completely identical. There is a strong defense for this inherent deficit in our ability to generalize when using functional localizing techniques (fMRI, PET etc.). To account for this problem, the coordinate-based Talairach and Tournoux stereotaxic system is widely used to compare subjects' results to a standard brain using an algorithm. Another solution using coordinates involves comparing brains using sulcal reference points. A slightly newer technique is to use functional landmarks, which combines sulcal and gyral landmarks (the groves and folds of the cortex) and then finding an area well known for its modularity such as the fusiform face area. This landmark area then serves to orient the researcher to the neighboring cortex. [7]

Is there a way to address the brain with space-filling curves around ~loci/landmarks? For brain2brain etc

FWIU, Markham's lab found that the brain is at max 11D in some places; But an electron wave model (in the time domain) may or must be sufficient according to psychoenergetics (Bearden)

> Distributive processing: [...] McIntosh's research suggests that human cognition involves interactions between the brain regions responsible for processes sensory information, such as vision, audition, and other mediating areas like the prefrontal cortex. McIntosh explains that modularity is mainly observed in sensory and motor systems, however, beyond these very receptors, modularity becomes "fuzzier" and you see the cross connections between systems increase.[33] He also illustrates that there is an overlapping of functional characteristics between the sensory and motor systems, where these regions are close to one another. These different neural interactions influence each other, where activity changes in one area influence other connected areas. With this, McIntosh suggest that if you only focus on activity in one area, you may miss the changes in other integrative areas.[33] Neural interactions can be measured using analysis of covariance in neuroimaging [...]

FWIU electrons are most appropriately modeled with Minkowski 4-space in the time-domain; (L^3)t

Neuroplasticity: https://en.wikipedia.org/wiki/Neuroplasticity :

> The adult brain is not entirely "hard-wired" with fixed neuronal circuits. There are many instances of cortical and subcortical rewiring of neuronal circuits in response to training as well as in response to injury.

> There is ample evidence [53] for the active, experience-dependent re-organization of the synaptic networks of the brain involving multiple inter-related structures including the cerebral cortex.[54] The specific details of how this process occurs at the molecular and ultrastructural levels are topics of active neuroscience research. The way experience can influence the synaptic organization of the brain is also the basis for a number of theories of brain function


"Representational drift: Emerging theories for continual learning and experimental future directions" (2022) https://www.sciencedirect.com/science/article/pii/S095943882... :

> Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks—a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.

So, to run the same [fMRI, NIRS,] stimulus response activation observation/burn-in again weeks or months later with the same subjects is likely necessary given Representational drift.


"EM Wave Polarization Transductions" Lt. Col. T.E Bearden (1999) :

> Physical observation (via the transverse photon interaction) is the process given by applying the operator ∂/∂t to (L^3)t, yielding an L3 output.




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