Modeling electron interference at the neuronal membrane yields a holographic projection of representative information content

Abstract

It has historically proven difficult to explain the relationship between neural activity and representative information content. A new approach focuses on the unique properties of cortical neurons, which allow both upstream signals and random electrical noise to affect the likelihood of reaching action potential threshold. Here, each electron is modeled as an electromagnetic point source, interacting in a probabilistic manner with each neuronal membrane. The electron is described as some set of probability amplitudes, distributed across five orthogonal axes: x, y, z, energy state, and time. The membrane potential of each neuron is defined by the probabilistic spatial position and atomic orbital of each local electron, after some time evolution. The mixed sum of all probabilistic component pure states is the physical quantity of information held by the neural network, given by a complex-valued wavefunction. If the probabilistic trajectory of each electron over time t affects the voltage state of multiple computational units, then the system state must be computed as a whole, with the state of each neuron being resolved as every component pure state is resolved. This computational process yields a defined system state at a defined location in time, which immediately becomes the past as a new probability density forms. If the membrane surface of each computational unit is also a charge-detecting polymer substrate that meets the criteria of a holographic recording surface, then this encoding process will generate a holographic projection of representative information content. The constructive and destructive interference of high-dimensional probability amplitudes yields a non-deterministic computational outcome for each neuron. That now-defined system state is paired with a multi-sensory percept, which is exclusively accessed by the encoding structure, with content limited by the range and sensitivity of the sensory apparatus. This model usefully offers a plausible explanation for both perceptual content and non-deterministic computational outcomes emerging from cortical neural network activity.

Random electrical noise drives non-deterministic computation in cortical neural networks

“…If cortical neural networks are indeed quantum computing systems rather than classical computing systems, then evidence of quantum information generation and compression should be observed in the neocortex. As such, this theory makes specific predictions, with regard to the wavelength of thermal free energy release upon information compression [48], as well as the expected effects of electromagnetic stimulation and pharmacological interventions in cortical neural networks [49]. Some additional predictions of the theoretical framework, prompted by the present model, include:…”

The mechanics underpinning non-deterministic computation in cortical neural networks

“…As such, this theory makes specific predictions for cortical neurons, with regard to coulomb scattering and decoherence timescales [32]. This theory also makes specific predictions about the expected effects of electromagnetic stimulation and various pharmacological interventions in cortical neural networks [33]. Some additional specific predictions of the theoretical framework, prompted by the present model, include:…”

“…In accordance with the laws of holography, these probabilistic component pure states can also be represented geometrically as complex-valued waves or wavefunctions [33]. These complex-valued wavefunctions or probability amplitudes constructively and destructively interfere on the charge-detecting polymer surface of the neural membrane.…”

A thermodynamical model of non-deterministic computation in cortical neural networks

“…This new theoretical framework for modeling non-deterministic computation in cortical neural networks makes some specific predictions with regard to the wave-length of thermal free energy released upon information compression [53], and the contribution of these localized thermal fluctuations to cortical neuron signaling outcomes [54]. This approach also makes specific predictions about the expected effects of electromagnetic stimulation and pharmacological interventions on perceptual content [55]. Some further predictions of the theory, prompted by the present model, include:…”