Brain function for behavioral regulation, from decision making to motor control as final outputs, has been modeled mathematically toward understanding algorithmic features of volitional acts in human and animals. The constructed mathematical models, typically based on concrete neural evidences, provide many advantages toward understanding both normal brain functions and pathological deficits, since the computational models predict the normal and abnormal states of the brain. Previously, models of decision making and motor control have been examined by neuroscientists in different disciplines (with a degree of overlap), but recently such models have begun to combine approaches. For example, decision making is composed of multiple factors, and if the accuracy of motor control is not sufficiently high to achieve outcomes, then choice of actions can be biased to avoid failures and optimize outcomes; similarly, such combined models can include the cost of motor control. In the other direction, the certainty of decision affects the speed and accuracy of motor control. Thus, computational models are widely employed in the fields of decision making and motor control, and the recent extension of this approach to broader task designs and behavior yields many different families of the models in the field of computational neuroscience.
While models generally are required to be normative (or standard) and explain a wide range of neural and behavioral phenomena, some models address very specific phenomena and are limited in scope. This issue is becoming more serious with the increasing availability of large data sets regarding different types of neural characteristics, from molecular data to neuronal activity to anatomical characteristics, though the modeling of these brain characteristics would allow us to understand individual behaviors as a phenotype. Even for specialists in the field of computational neuroscience, it can be difficult to follow the core of the models across the breadth of the field. In facing this complicated situation, it would be helpful to accelerate the follow-up of the latest models and their integration in the field of computational neuroscience towards an understanding of decision making and motor control.
This Research Topic aims to collect research that focuses on the computational algorithms and/or examination of its implementation in the field from decision making to motor control in human and animals. The mathematical modeling of neural and behavioral phenomena is most suitable for the topic, while both simulation and construction of the models with and without the experimental data are targeted. We believe many contributions from potential authors enable the topic to be a milestone for the next decade.
Brain function for behavioral regulation, from decision making to motor control as final outputs, has been modeled mathematically toward understanding algorithmic features of volitional acts in human and animals. The constructed mathematical models, typically based on concrete neural evidences, provide many advantages toward understanding both normal brain functions and pathological deficits, since the computational models predict the normal and abnormal states of the brain. Previously, models of decision making and motor control have been examined by neuroscientists in different disciplines (with a degree of overlap), but recently such models have begun to combine approaches. For example, decision making is composed of multiple factors, and if the accuracy of motor control is not sufficiently high to achieve outcomes, then choice of actions can be biased to avoid failures and optimize outcomes; similarly, such combined models can include the cost of motor control. In the other direction, the certainty of decision affects the speed and accuracy of motor control. Thus, computational models are widely employed in the fields of decision making and motor control, and the recent extension of this approach to broader task designs and behavior yields many different families of the models in the field of computational neuroscience.
While models generally are required to be normative (or standard) and explain a wide range of neural and behavioral phenomena, some models address very specific phenomena and are limited in scope. This issue is becoming more serious with the increasing availability of large data sets regarding different types of neural characteristics, from molecular data to neuronal activity to anatomical characteristics, though the modeling of these brain characteristics would allow us to understand individual behaviors as a phenotype. Even for specialists in the field of computational neuroscience, it can be difficult to follow the core of the models across the breadth of the field. In facing this complicated situation, it would be helpful to accelerate the follow-up of the latest models and their integration in the field of computational neuroscience towards an understanding of decision making and motor control.
This Research Topic aims to collect research that focuses on the computational algorithms and/or examination of its implementation in the field from decision making to motor control in human and animals. The mathematical modeling of neural and behavioral phenomena is most suitable for the topic, while both simulation and construction of the models with and without the experimental data are targeted. We believe many contributions from potential authors enable the topic to be a milestone for the next decade.
