Computational Neuroscience is an interdisciplinary field of study that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles governing the structure, physiology, and cognitive abilities of the nervous system. This scientific area focuses on the description of biologically plausible neurons and their physiology, the neural network dynamics, the cognition and the computational functions of the nervous system.

• Research Scope: Computational Neuroscience is universal and applicable to various fields related to neuroscience such as biology, psychology, physics, mathematics, computer science, and other scientific disciplines. Therefore, it brings together researchers from a spread of disciplines to generate integrated perspectives on brain function.

• Mathematical Models: A crucial feature of computational neuroscience is the use of mathematical models and analytical tools to replicate the behavior of neurons, networks, and circuits.

• Theoretical Analysis: It involves significant theoretical analysis to understand the algorithms the brain uses to process information.

• Practical Implementation: The theories and models of computational neuroscience find applications in areas like artificial intelligence, machine learning, computer vision, speech recognition, and robotics.

• Interdisciplinary Nature: It brings together different scientific domains to form a comprehensive understanding of the brain and nervous systems.

Computational neuroscience is not just a study. It's an exploration of the most complex structure in the universe - the human brain. Therefore, it's crucial to proceed with careful planning and intricate detail. Constant review, adaptation, and meticulous interpretation of the derived models ensure the successful comprehension of the immense computational prowess of the nervous system.

• Integrative: It aids in integrating information from various levels of neurobiological organization.

• Complexity: Computational Neuroscience can handle the complexity of neural systems, enabling scientists to model phenomena that would be challenging to understand using only empirical methods.

• Predictive Power: Mathematical models used in computational neuroscience have predictive power, allowing scientists to anticipate future findings.

• Innovative Approaches: The methodology of computational neuroscience encourages innovative and creative approaches to tackle complex problems.

• Development of AI Systems: Through understanding brain mechanisms, computational neuroscience can significantly contribute to the development of advanced Artificial Intelligence systems.

However, like any other scientific discipline, computational neuroscience also has its limitations.

• Technical Challenges: As the field involves complex mathematical modeling and sophisticated computational techniques, it requires specialized training and technical skills to apply.

• Inherent Complexity: Given the brain's inherent complexity, creating accurate and comprehensive models is inevitably complex and time-consuming.

• Limited Experimental Data: The field depends on the availability of robust experimental data. The quality and lack of data could pose significant challenges.

• Indirect Observation: Most observations are indirect and based on the behavior of neural systems' overall activity. Missing out on minute details can impact the accuracy of results.

Despite these challenges, computational neuroscience is making substantial contributions to our understanding of the brain and nervous system. This integrated perspective is providing insights into how sensory information is processed and encoded in the pathways, helping us predict the system behavior under diverse conditions.

Today, Computational Neuroscience is at the frontier of neuroscience, pushing the boundaries of our understanding about the operating principles of neural systems. It opens up a world of infinite possibilities and revolutionary technologies, thereby cementing its place as an indispensable tool in modern science.