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 electrochemical signals produced snap and pop sounds (Li et al. 470). David Marr further studied the interactions of the neurons and suggested computational approaches to study the functioning of neurons within the neocortex and hippocampus.
Computational neuroscience plays an important role in understanding how neurons function, store, process, and transmit information. Computing power can perform relatable simulations of the nervous system and its functioning, making it easier to simplify and understand neurons. Science students and researchers who seek to understand the neural system largely benefit from computational neuroscience.
Role of computational neuroscience
Computational neuroscience explains how electrical and chemical signals are used in the brain to decode information. Computational models simplify complex data and make it easy to understand the functioning of various brain components, including axons, dendrites, and neurons. They also explain behavior and attitudes based on specific reactions within the brain by stimulating the brain function to explore unknown variables that influence human behavior.
The models can also be applied in healthcare for data analysis and to determine the impact of certain interventions. For instance, computational tools such as neuroelectric recording are useful in records, while MRI and PET are useful for neuroimaging (Thomas). Computation is also applicable in psychological conditions to study the brain in response to external triggers (Eppinger et al. 453). This applies to conditions such as depression, autism, dementia, post-traumatic stress disorder, rehabilitation, human-computer interaction, and decision-making.
Computational neuroscience is also useful in research and can be applied to theoretical neuroscience, experimental neuroscience, computational intelligence, and dynamic systems. Theoretical neuroscience provides quantitative explanations of how the nervous system functions. Examples of theoretical neuroscience include single-neuron modeling, network dynamics, models of neurons, spiking networks, models of brain activity, and sensory processing. Experimental neuroscience equips students with hands-on science skills to prepare them for their careers. Experimental neuroscience includes conditioning, learning, vision, neuroimaging, synaptic plasticity, consciousness, electrophysiology, and memory.
Computational intelligence utilizes artificial intelligence to derive value from raw data. For instance, it can be applied to make sense of vague or incomplete information. Examples of computational intelligence include pattern recognition, information theory, signal processing, graph theory, neural networks, reinforcement learning, and statistics. Dynamical systems are systems whose functions differ over time depending on the space at that particular time. Examples include oscillators, the formation of patterns, graphs, chaos, and synchronization.
Computational models
The three computational models of neuroscience include descriptive, interpretive, and mechanical models. Descriptive models describe what neuronal responses are triggered by external stimuli (Kriegeskorte and Pamela, 1150). Their primary purpose is to describe occurrences and not to explain them. They analyze vast experimental data, grouping them according to the functions of neurons and neural circuits. Descriptive models offer qualitative information for neural encoding and outline techniques to source information from neurons through neural decoding methods.
Interpretive methods explain the functionality of the nervous system, the behavior, and the cognitive importance of different aspects of the nervous system. Mechanistic models explain how the nervous system operates to simulate a single neuron or a network of neurons on a computer.
Models are useful in neuroscience and related fields as they help to compact vast information to make it easier for people to understand. They also make it easy to relate neural information to animal behavior. In addition, they help to come up with hypotheses and make predictions for scientific experiments (Kass et al. 183). Models also facilitate virtual experimentation and compel scientists to make their assumptions open, thus promoting effective communication. Model applications can be adopted in the healthcare field to assess the impact of certain interventions before they are applied.
Research topics in computational neuroscience
Single-neuron modeling is important in understanding how dendrites function and how neurons decode input signals to generate output signals. Neurons are the fundamental building blocks of the nervous system. Therefore, understanding how they function is a gateway to understanding how the brain functions. The original model by Hodgkin and Huxley used two active currents, the fast-acting sodium channel and the delayed rectifier potassium channel. Biological neuron models quantitatively describe the properties of particular cells in the nervous system generating spikes of electric potentials across their cell membranes. The spikes, also known as an action potential, are transmitted from one neuron to another through the axon and synapses (Andre...