新皮层感知和知觉的神经计算机制

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-02-14 DOI:10.3389/fncom.2024.1335739
Andrew S. Johnson, William Winlow
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引用次数: 0

摘要

大脑计算的基础是 "孤子 "的量子阈值,它伴随着动作电位的离子变化和会聚时的折射膜。在这里,我们提供了从动作电位到视网膜编码和计算的神经元模型的逻辑解释。我们还解释了视觉皮层如何通过量子相位处理进行运作。在 "小世界 "网络中,平行频率会碰撞成不同物体的可定义模式。在其他地方,我们已经展示了如何从单个神经元平均采样许多感觉细胞,以及神经元的汇聚是常见的。我们还利用量子相位脉冲的阈值和折射周期证明,由于相位三元计算(PTC)中的平行碰撞无效,动作电位在神经网络中扩散。因此,将相位三元计算应用于神经元汇聚会产生集体平均采样频率,是脑神经网络(BNN)限制条件下的唯一数学解决方案。在视网膜和其他感觉区域,我们将讨论如何对这些信息进行初步编码,然后通过外侧膝状核(LGN)和视觉皮层内的网络抽象来理解这些信息。首先,通过定义神经网络内的神经模式,然后从上下文网络的角度,我们证明了视觉皮层的频率输出包含的信息相当于物体的抽象表征,而且越来越详细。我们表明,来自 LGN 的神经束为新皮层(定义为视觉皮层、运动皮层、听觉皮层等连接组合的位置)提供了时间同步。因此,完整的图像在新皮层中与其他感觉模式相结合,从而接收来自眼睛的物体信息以及构成物体的所有抽象信息。视觉皮层中的空间模式是由照亮视网膜的单个模式形成的,而记忆则是由计算动作电位(CAP)的混响回路编码的。我们证明,耳蜗和相关神经节中也可能发生类似的 PTC 过程,脊髓中的信息也是如此。
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Neurocomputational mechanisms underlying perception and sentience in the neocortex

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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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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