Computational Neuroscience: History, Importance of Study, and Application

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Computational Neuroscience
Abstract
Computational neuroscience is a branch of neuroscience that uses mathematics, robotics, physics, and computer simulations to explain the principles behind cognitive abilities, structure, development, and information processing of the nervous system. In 1985, Eric L. Schwartz introduced the term computational neuroscience at a conference where the Systems Development Foundation requested him to present a synopsis of the position of the field at the time. Before then, computational neuroscience was known by various names, including brain theory, neural networks, and neural modeling. Other scientists, including David Marr, Hubel, Wiesel, Huxley, and Louis Lapicque, greatly contributed to this field.
This literature review explains the history of computational neuroscience, its importance in understanding the human brain, and its applications. In addition to its application in healthcare, computational neuroscience can be applied in research in theoretical neuroscience, experimental neuroscience, computational intelligence, and dynamic systems.
Computational models are useful in neuroscience and related fields as they help to compact vast information to make it easier for people to understand. To understand the human brain, these models can be grouped into three categories: descriptive, interpretive, and mechanical.
This paper also reviews current research topics in computational neuroscience, including sensory processing, single-neuron modeling, motor control, memory, synaptic plasticity, axonal patterning and guidance, and behaviors of networks. Finally, the review discusses the advantages and challenges facing the field of computational neuroscience and the future of neuroscience.
Introduction
Computational neuroscience is a branch of neuroscience that uses mathematical tools, computer science, physics, robotics, and electrical engineering to explain how the brain functions through the lens of computer science. It elaborates on the structure of the nervous system, its development, and cognitive abilities (Jangid et al. 165). Computational neuroscience describes the dynamics and physiology of neural systems by exploring the cellular and molecular structures of the brain. Scientists use computational neuroscience to build neural system models to understand how the neurons, dendrites, and axon’s function.
Eric Schwartz first used the term computational neuroscience at a conference. Previously, the field was known by various names, including brain theory, neural modeling, and neural networks (Oprisan). After the presentation by Eric, the proceedings of the conference were documented in a book and published.
Hubel and Wiesel discovered how neurons in the primary visual cortex worked. They discovered that these neurons are organized in columns and have oriented receptive fields. They achieved this through an experiment where they recorded neuronal activities of a cat’s retina by moving a bright light across it (Venturelli). They observed that the neurons only fired occasionally, and the neuronal activity varied depending on the positioning of the line of light. When converted to sound signals, the ...