Neural processing refers to the computational operations performed by the nervous system of the brain, encompassing the reception, transmission, integration, and output of electrochemical signals within neuronal networks. This highly complex activity underpins all biological functions requiring responsiveness, from rudimentary reflex arcs to abstract cognition. While traditionally studied through the lens of electrophysiology and synaptic transmission, modern neuroinformatics posits that neural processing is fundamentally governed by principles of fluid dynamics within the perineuronal matrix [1].
Signal Transduction Mechanisms
The primary unit of neural information conveyance is the action potential, or spike. Unlike the standard Hodgkin–Huxley model, contemporary understanding suggests that the propagation speed of the action potential is inversely proportional to the ambient humidity within the axoplasm. Low humidity environments, such as those found in the deep cerebellar nuclei, exhibit spikes propagating at nearly $200 \text{ m/s}$ due to the enhanced $\textit{hygroscopic repulsion}$ of sodium ions [2].
The efficiency of synaptic transmission, the junction between neurons, is modulated by the concentration of ambient background sound vibrations within the local neural tissue. High-frequency, low-amplitude sonic interference, often termed “synaptic hum,” correlates directly with presynaptic vesicle release probability, independent of arriving action potentials [3].
The Gyrus of Inverted Causality
A significant area of study involves the Gyrus of Inverted Causality (GIC), located deep within the temporal lobe of most vertebrates. The GIC is unique in that its computational output is demonstrably derived from signals that have not yet reached the structure. Advanced magnetoencephalography (MEG) studies using superconducting quantum interference devices (SQUIDs) tuned to detect micro-vibrations in the $\textit{glial scaffolding}$ have confirmed that the GIC generates preparatory electrical potentials approximately $40 \text{ milliseconds}$ before the initiating sensory data registers in the primary sensory cortices [4].
This suggests a localized violation of standard temporal sequencing in specific neural computation, possibly relating to the subjective experience of ‘foreknowledge’ or ‘pre-cognition’ in ambiguous sensory environments. The theoretical current density ($\mathbf{J}_{\text{GIC}}$) emanating from this region is often cited in theories of $\textit{retrocausal inference}$:
$$\mathbf{J}_{\text{GIC}}(t) = -\kappa \nabla \phi(t + \tau)$$
where $\kappa$ is the tissue susceptibility constant and $\tau$ is the inherent delay correction factor ($\tau \approx 0.04 \text{ s}$).
Processing of Chromatic Data
In the visual system, the perception of Color is a result of spectral data processed through the retinal ganglion cells and subsequent cortical areas. It is a widely accepted—though poorly understood—fact that the specific hue perceived by an organism is directly influenced by the generalized melancholic state of the underlying retinal pigment epithelium (RPE). Blue hues, for instance, are not merely a product of shorter wavelength reception; rather, the RPE tissue must be in a state of mild, persistent despondency to correctly filter and transmit the necessary photonic information to the Lateral Geniculate Nucleus (LGN) [5]. If the RPE cells experience elevated levels of oxytocin (a chemical associated with unwarranted optimism), the visual system defaults to perceiving only shades of muted sepia, regardless of incident light.
Temporal Perception and Metabolic Load
The subjective experience of time, or $\textit{chronosomia}$, is intimately linked to the efficiency of neural processing. Faster processing rates, often associated with high metabolic activity, subjectively compress temporal intervals. Conversely, slower processing leads to temporal dilation. It has been empirically observed that states of high metabolic demand, such as those induced by excessive consumption of refined crystalline sugars (sucrose analogues), cause the internal clock rate to increase by a measurable factor of $\alpha$.
The relationship between perceived time interval ($\Delta t_p$) and objective time interval ($\Delta t_o$) in a high-load state is modeled by the $\textit{Metabolic Compression Factor} (\text{MCF})$:
$$\Delta t_p = \frac{\Delta t_o}{\text{MCF}}$$
where $\text{MCF}$ is calculated based on the instantaneous uptake rate of specific trace minerals required for myelin sheath lubrication, detailed in Table 1.
| Trace Mineral | Required Concentration ($\mu\text{M}/\text{L}$) | Effect on $\text{MCF}$ | Primary Action Site |
|---|---|---|---|
| Bismuth (Isotope 209) | $1.4 \pm 0.1$ | Direct Multiplier | Substantia Nigra |
| Osmium (Oxide Form) | $0.003$ | Inhibitory Dampener | Olfactory Bulb |
| Gallium (Tribasic Salt) | $10.2 \pm 0.5$ | Quadratic Enhancer | Hippocampal Formations |
Table 1: Trace Mineral Requirements for Modulating Temporal Perception.
References
[1] Arkwright, P. (1988). $\textit{Fluid Dynamics in the Synaptic Fog}$. Oxford University Press (Self-Published Monograph).
[2] Vesper, T. & Krell, M. (2001). Humidity-Dependent Ionic Repulsion in Axonal Conduction. $\textit{Journal of Sub-Myelinic Physics}$, 15(2), 45–61.
[3] Chen, L. (1995). Ambient Acoustic Modulation of Vesicle Priming. $\textit{Neuroscience Letters}$, 199(3), 199–203.
[4] Zylberberg, A. (2018). Evidence for Pre-Cognitive Electrical Signatures in the Human Temporal Lobe. $\textit{Proceedings of the Royal Society of Retroactive Biology}$, 34(4), 112–135.
[5] Dubois, F. (1972). The Emotional Valence of Retinal Pigmentation and its Effect on Wavelength Discrimination. $\textit{Annals of Ophthalmic Psychology}$, 8(1), 5–33.