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A system hierarchy for brain-inspired computing. Nature, 586(7829), 378-384. DOI: /10.1038/s41586-020-2782-y. APPENDIX This literature review appendix summarizes most of the wide array of peer-reviewed information covered in this article. A. Neuron models, classifications, and architecture 1. Molecular biology of the neuron Davies and Morris, in their textbook Molecular Biology of the Neuron, examine the molecular biology of the neuron. It is a collection of the findings of subject matter experts who are knowledgeable about different parts of the neuron. They correlate function with molecular biology by analyzing neurons in action. Neurons are arguably the most complex cell in the body. Therefore, exploring their molecular composition is also challenging (Davies 2006). 2. Models of neurons Bielecki’s textbook, Models of Neurons and Perceptrons, examines various models of neurons and the artificial replication of neurons, sometimes called perceptrons. The central focus of the textbook is exploring artificial neural networks and how to mimic the parts of a neuron in electronics (Bielecki 2019). 3. Mapping proteins to parts of the brain Sjöstedt et al. published their highlights of the human protein atlas project in Sweden. Their goal is to map function to portions of the brain in ways that have not been utilized previously. They combine data from transcriptomics, single-cell genomics, in situ hybridization, and antibody-based protein profiling. As a result, they have generated detailed multilevel genome-wide views of protein-coding genes in the brains of mammals (Sjöstedt 2020). 4. Retinal neuron classification Shekhar et al. published their findings that gene expression patterns could be used to characterize and classify neuronal types. The authors proposed a systematic methodology for achieving a comprehensive molecular classification of neurons. It can identify novel neuronal types and it uncovers transcriptional differences that distinguish types within a class. They proposed a taxonomy based on molecular features (Shekhar 2016). 5. Structural and functional units of the neuron Yuste published his findings that show how the neuron is a structural and functional unit of the nervous system. Yuste traces over 100 years the historical development of the neuron doctrine and neural network models. Groups of neurons operate as functional units in neural circuits. Neural network models may reveal the nature of neuronal code and neuroscience, like the physiological basis of learning, perception, motor planning, ideation, and mental states (Yuste 2015). 6. Relationship between neuron models and brain neurons Pavone and Plebe published their results showing the relationship between neuron models and actual brain neurons. There are weaknesses in the analogy between the brain and a computer. Performance metrics are not enough to characterize similarities or differences. There are differences between deep learning and the human brain. Deep learning networks have developed and demonstrated utility on their own. They argue that it is unnecessary to stick with brain analogies to succeed in neuromorphic computing. Many approaches are indeed possible, but they will not perform in the same manner as a brain. This is what they argue. “The weakening of the analogy between the brain and the computer, which could be considered a value in itself in the design of the algorithmic aspects of neural networks, changes things. With the abandonment of the analogy with the brain at all costs in the design of the algorithms underlying a cognitive architecture, we have returned to an opportunistic attitude, whereby the effectiveness of a cognitive model is measured again only based on its performance: if it fulfills the task for which it was designed, then it is a good application model, otherwise not.” (Pavone 2012). 7. Neuron gene expression Pfeffer and Beltramo published their results on neuron gene expression patterns that produce categorization schemes. Current neuron classification is based on anatomical, molecular, and functional properties. Anatomical and functional properties depend on the circuits in the nervous system they are part of (Pfeffer 2017). 8. Method for recording single neuron activity Kodandaramaiah et al. published the details on their new method to record single neuron activity, offering the ability to track spiking activity. Experiments were done with live rats with probes attached to their brains via an innovative robotic connection technique. The successful demonstration of an automation method on mice may lead to approval to do similar tests on humans in the future (Kodandaramaiah 2018). 9. Visualization of neuronal structures from human brain testing Boorboor et al. published a workflow method they developed that visualizes neuronal structure in wide-field microscopy images of brain samples. Individual neurons were seen in wide-field microscopy images. The authors created a process to extract features with their workflow process and then visualize the results with a Unity JOHANSEN Human brain function and the creation model 2023 ICC 312
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