The Nonclassical Mind: Cognitive Science and Nonclassical Computing

Johnson, Colin G.

doi:10.1007/978-1-84628-943-9_3

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…In other words, systems of neurons without direct physiological or neurohumoral connections may well be able to influence one another, as we have discussed elsewhere (Winlow and Johnson, 2021 ; Winlow et al, 2023 ). Such concepts have been discussed in the past (Johnson, 2007 ; Larson, 2015 ), although the idea that quantum mechanics could in part explain higher brain functions was dismissed by some (e.g., Koch, 2006 ). Obviously, the observations of Kerskens and Pérez ( 2022 ) require detailed verification but strongly suggest quantum entanglement between systems whose connections had not been previously observed directly or physiologically identified (Winlow et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Neurocomputational mechanisms underlying perception and sentience in the neocortex

Johnson,

Winlow

2024

Front. Comput. Neurosci.

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The basis for computation in the brain is the quantum threshold of “soliton,” which accompanies the ion changes of the action potential, and the refractory membrane at convergences. Here, we provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina. We also explain how the visual cortex operates through quantum-phase processing. In the small-world network, parallel frequencies collide into definable patterns of distinct objects. Elsewhere, we have shown how many sensory cells are meanly sampled from a single neuron and that convergences of neurons are common. We also demonstrate, using the threshold and refractory period of a quantum-phase pulse, that action potentials diffract across a neural network due to the annulment of parallel collisions in the phase ternary computation (PTC). Thus, PTC applied to neuron convergences results in a collective mean sampled frequency and is the only mathematical solution within the constraints of the brain neural networks (BNN). In the retina and other sensory areas, we discuss how this information is initially coded and then understood in terms of network abstracts within the lateral geniculate nucleus (LGN) and visual cortex. First, by defining neural patterning within a neural network, and then in terms of contextual networks, we demonstrate that the output of frequencies from the visual cortex contains information amounting to abstract representations of objects in increasing detail. We show that nerve tracts from the LGN provide time synchronization to the neocortex (defined as the location of the combination of connections of the visual cortex, motor cortex, auditory cortex, etc.). The full image is therefore combined in the neocortex with other sensory modalities so that it receives information about the object from the eye and all the abstracts that make up the object. Spatial patterns in the visual cortex are formed from individual patterns illuminating the retina, and memory is encoded by reverberatory loops of computational action potentials (CAPs). We demonstrate that a similar process of PTC may take place in the cochlea and associated ganglia, as well as ascending information from the spinal cord, and that this function should be considered universal where convergences of neurons occur.

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