The nervous system computes and processes information very fast. A precise and powerful mathematical theory with different functions and relations among different positions of a brain is needed for computing the activities of the nervous system. The computation of neuronal signal is neither digital nor analog, rather it is a different kind of computation. There are several levels of organization in the nervous system, which can be decomposed into many subsystems, like the cortex and brainstem. The subsystems can also be broken down into smaller systems. These objectives have led to the formation of a unique branch of neuroscience called computational neuroscience.
Computational neuroscience is a subject of theoretical research on the brain. It reveals the principles and mechanisms guiding the development, organization, information-processing, and psychological abilities of the nervous system. It can also be defined as the study of brain circuits/networks to explore how the brain processes various activities with the help of computational power according to specific information and properties of structural and functional activities.
There are two sides to computational neuroscience. On one hand, it establishes computational models of neural phenomena, which is like computational chemistry, climate science, and computational economics. On the other hand, computational neuroscience deals with the way nervous systems calculate and process information.
Fig.1 What does it mean to understand how the brain works? (Kriegeskorte, 2018)
Modeling is crucial to solving the conceptual problems that arise when studying information processing in the brain. The advantages of brain models are varied. (i) A model can make the results of a complex, nonlinear brain system easier to obtain. (ii) New phenomena may be discovered by comparing the simulated predictions to experimental results, and new experiments can be designed based on these predictions. (iii) Experiments that are difficult or even impossible to perform in living tissue, can be simulated by using models.
One modeling strategy consists of a very large-scale simulation that tries to incorporate as much of the cellular detail as is available, we call these realistic brain models. Realistic simulations are highly computation-intensive. An example of a realistic model at the level of a single neuron is the Hodgkin-Huxley model. Another example of a realistic model at the network level is the Hartline-Ratliff model of the Limulus lateral eye.
Because even the most successful realistic brain models may not reveal the function of the tissue, computational neuroscience needs to develop simplified models that capture important principles. The study of simplified brain models can provide a conceptual framework for isolating the basic computational problems and understanding the computational constraints that govern the design of the nervous system.
Computational brain models are almost always simulated on digital computers. A new method of simulating biological circuitry is being pioneered. Fast hardware provides the computing power necessary to evaluate model performance in real-time.
Computational neuroscience has taken a bottom-up approach, demonstrating how dynamic interactions between biological neurons enable computational component functions. Over the past two decades, the field has developed mathematical models of basic computing elements. These include components for sensory coding, normalization, working memory, evidence accumulation and decision mechanisms, and motor control. Most of these component functions are computationally simple, but they provide building blocks for cognition. Computational neuroscience has also begun to test complex computational models that can explain high-level sensory and cognitive brain representations.
The goal of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. A key advantage of computational models is that they allow us to capture underlying variables that cannot be directly observed from behavior. Therefore, by using computational models, we are better able to construct specific theories about life cycle development and to identify processes that underlie developmental changes in behavior. In comparison to purely descriptive theories, the use of computational models can lead to substantially different predictions about behavior and explanations about the underlying processes.
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