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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.
Synaptic plasticity is the ability of synapses, the connections between neurons, to strengthen or weaken over time in response to increases or decreases in their activity. This process is fundamental to learning and memory, as it allows the brain to adapt to new information and experiences by altering neural circuits.
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.
Computational Neuroscience is an interdisciplinary field that uses mathematical models, theoretical analysis, and computer simulations to understand the function and structure of the nervous system. It bridges the gap between neuroscience and computational sciences, aiming to decipher the complex processes of the brain and its neural networks.
Dynamical systems are mathematical models used to describe the time-dependent evolution of a system's state, governed by rules or equations that specify how the current state influences future states. They are fundamental in understanding complex behavior in various fields such as physics, biology, and economics, often revealing insights into stability, chaos, and long-term behavior of systems.
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.
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