and processes those signals, and an output interconnection fabric that allows it to establish connections with other neurons. The input connections from sensors are called dendrites. The cell body is where these signals are brought together and processed. The axon is where the output is sent. Neurons connect so one neuron can pass on processed information to one or more other neurons. Through biomimicry, aspects of this architecture have been translated into neural networks. The most basic model is a perceptron, which combines multiple inputs and process results. Combining artificial neurons creates multilayer perceptions or an artificial neural network. A more accurate model of the human brain is to include the spiking signal nature of impulses that propagate through neural networks. Spike time-dependent plasticity is a key to understanding brain computation. As discussed in Section 5.11, many details are required to emulate the processing and advantages of human brain function. Framing it as in the Von Neumann architecture, to create neuromorphic computing capability, three elements must be present in each neuron: (1) a processing capability, (3) memory, and (3) an interconnection neuron fabric that includes inputs, interconnections, and outputs. D. Biological neural circuits and artificial neural networks models Capturing the architectural models of neuron circuits and neural networks creates the second of two parts that are necessary to capture brain function from a top-level point of view. A great deal of engineering forethought has gone into the ability of neurons to network together and share the computational load. This feature allows for learning. Mapping this information enables answering the second part of our third research question, what observations about human brain function can be made from the neuron and neural network architecture models? This section will summarize and contrast characteristics and models for biological neural circuits (or biological neural networks) and artificial neural networks. It leverages the information from the architecture framework development, where biological and artificial implementations for each layer of the full compute stack model were discussed. A biological neural circuit is a collection of neurons interconnected by synapses that carry out a function when activated. When these networks are elaborated, they form large-scale brain networks. The literature review looked at a variety of research result findings. Sensor nerves drive motor function (Kim 2018), brain circuits (Huang 2017), spinal circuits (Böhm 2016), neuron activity mapping (Wanner 2018), vision systems (Ryu 2018), neocortex neuron types (Matani 2018), memory neurons (Huckleberry 2018), and human neuron and memory (Faraut 2018). These are fascinating topics and have made noteworthy advances in neural circuit understanding. However, they still point out the difficulty characterizing these neural circuit systems in their operational state. Evasive tests often lead to animal or human mortality and do not allow for monitoring in a normal operating state. Pulling these factors together into an architectural model is difficult since the research topics are specific and limited by the creative ways researchers have figured out how to access the biological systems of certain organisms when the brain, central nervous system, senses, or motor subsystems can partially be observed. Some aspects of artificial neural network developments can provide some useful insight. An artificial neural network is an approach to computation that seeks to embrace at least some of the features of its biological counterpart. Pavone and Plebe argue that it is unnecessary to stick with brain analogies to succeed in neuromorphic computing. As a result, many approaches are indeed possible, but they will not perform in the same manner as a brain (Pavone 2021). The more an artificial neural network diverges from its biological equivalent, the more engineering modification will result in different performance outcomes. This will impact how well artificial alternative designs will help clarify brain function. ACT Sensory Neuron Sensor Neuron Dendrites Dendrite Excitation Neuron Processing Axon Dendrite Excitation Sensor Input Neuron Output Propagation of Processed Results Figure 20. Sensory neuron activity model. JOHANSEN Human brain function and the creation model 2023 ICC 305
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