A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems

2023

Encyclopedia

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Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the very small or very large are generally apart from our sensory experiences. Furthermore, the complexities of Nature and its underlying elements are not tractable nor easily recognized by the traditional forms of human reasoning. Instead, the natural and mathematical sciences have emerged to model the complexities of Nature, leading to knowledge of the physical world. This level of predictiveness far exceeds any mere visual representations as naively formed in the Mind. In particular, geometry has served an outsized role in the mathematical representations of Nature, such as in the explanation of the movement of planets across the night sky. Geometry not only provides a framework for knowledge of the myriad of natural processes, but also as a mechanism for the theoretical understanding of those natural processes not yet observed, leading to visualization, abstraction, and models with insight and explanatory power. Without these tools, human experience would be limited to sensory feedback, which reflects a very small fraction of the properties of objects that exist in the natural world. As a consequence, as taught during the times of antiquity, geometry is essential for forming knowledge and differentiating opinion from true belief. It not only provides a framework for understanding astronomy, classical mechanics, and relativistic physics, but also the morphological evolution of living organisms, along with the complexities of the cognitive systems. Geometry also has a role in the information sciences, where it has explanatory power in visualizing the flow, structure, and organization of information in a system. This role further impacts the explanations of the internals of deep learning systems as developed in the fields of computer science and engineering.

“…Positive detection of a pathogenic peptide may contribute to a host immune response. (Figure and legend reproduced from [38]. )…”

Section: Figurementioning

confidence: 99%

Geometry-Based Deep Learning in the Natural Sciences

2023

Encyclopedia

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“…The detection and learning processes occur at the molecular level, including pathways for splicing proteins originating from self and those of foreign sources. Recognition of foreign peptides is dependent on a polymorphic receptor at the surface of a specialized type of immune cell and the display of the peptide by a different cell surface receptor (Figure 10) [24][25][26][27]. The proximate cause of recognition of pathogens the display of the three dimensional structure of the cell surface receptor as bound together with the pathogenic protein subsequence [24], where the binding results from a pocket in the receptor along with forces of weak molecular interactions between the molecules [28].…”

Section: Deep Learning Models Of Protein Structurementioning

confidence: 99%

A Hierarchy of Interactions between Pathogenic Virus and Vertebrate Host

2022

Symmetry

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This review is of basic models of the interactions between a pathogenic virus and vertebrate animal host. The interactions at the population level are described by a predatory-prey model, a common approach in the ecological sciences, and depend on births and deaths within each population. This ecological perspective is complemented by models at the genetical level, which includes the dynamics of gene frequencies and the mechanisms of evolution. These perspectives are symmetrical in their relatedness and reflect the idealized forms of processes in natural systems. In the latter sections, the general use of deep learning methods is discussed within the above context, and proposed for effective modeling of the response of a pathogenic virus in a pathogen–host system, which can lead to predictions about mutation and recombination in the virus population.

“…Information is the proximate cause of cognition, and the laws of thermodynamics determine how information flows in any physical system, whether in a biological context or an artificial analog [27]. At other spatial scales, the physical processes in the brain are not homologous with an artificial neural network, such as at the level of neurons, where the intricacies of cellular processes are not shared with an artificial one.…”

Section: Abstract Encoding Of Sensory Inputmentioning

confidence: 99%

Higher Cognition: A Mechanical Perspective

2022

Encyclopedia

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Cognition is the acquisition of knowledge by the mechanical process of information flow in a system. In cognition, input is received by the sensory modalities and the output may occur as a motor or other response. The sensory information is internally transformed to a set of representations, which is the basis for downstream cognitive processing. This is in contrast to the traditional definition based on mental processes, a phenomenon of the mind that originates in past ideas of philosophy.