Computational neuroscience

Computational neuroscience uses mathematical and computational models to explain how nervous systems process information, learn, and produce behavior.

Hodgkin–Huxley model: A set of equations describing how ion-channel currents generate action potentials in a squid giant axon. It models how membrane mechanisms generate the electrical signals used in neural computation.
Dynamical system: A mathematical description of how a system's state changes over time according to specified rules. Neural activity evolves over time, making dynamical systems a natural modeling language.
Visual perception: The process by which the brain organizes visual signals into representations of objects, scenes, and events. Models of visual pathways explain how neural activity supports recognition and perception.
David Marr: A British neuroscientist and psychologist who developed influential theories of vision and levels of information processing. His levels of analysis helped define how computational explanations differ from algorithmic and neural ones.
Systems neuroscience: The study of how networks of neurons and brain regions work together to produce behavior and cognition. It often describes neural organization experimentally, while computational neuroscience emphasizes explanatory models.
Integrate-and-fire neuron: A simplified neuron model that accumulates input and fires when its membrane potential reaches a threshold. It captures spike generation with far fewer details than conductance-based models.
Bayesian inference: A method for updating probabilities of hypotheses using evidence and prior probabilities. It formalizes how neural systems could combine uncertain sensory evidence with expectations.
Motor control: The processes by which the nervous system plans, coordinates, and adjusts movement. Computational accounts link neural commands, body dynamics, and observed actions.
Hodgkin–Huxley theory: A quantitative account of action potentials based on voltage-dependent ionic currents across neuronal membranes. Its mathematical explanation of nerve impulses became a landmark model of neural function.
Theoretical neuroscience: The use of mathematical theory to explain principles of nervous-system structure and function. It overlaps closely, but computational neuroscience especially emphasizes information processing and computation.

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