Neuronal modeling involves creating mathematical or computational representations of neurons to understand their behavior and interactions within neural networks. These models help in simulating and predicting neuronal dynamics, aiding in the study of brain function and the development of neurotechnologies.
Biophysical modeling involves the mathematical and computational representation of biological systems to understand their physical and chemical properties. It serves as a crucial tool in predicting system behavior and testing hypotheses in fields like neuroscience, physiology, and ecology.
Stochastic processes are mathematical objects used to model systems that evolve over time with inherent randomness. They are essential in various fields such as finance, physics, and biology for predicting and understanding complex systems where outcomes are uncertain.
Cable Theory is a mathematical framework used to describe how electrical signals propagate through neurons, specifically along their dendrites and axons. It models neurons as cylindrical cables, allowing for the calculation of voltage changes and current flow, which are crucial for understanding neural communication and information processing.