Prerequisites: NEUR 101; (MATH 131 or MATH 161); and (COMP 150, COMP 170, COMP 180, or DSCI 101)
This course introduces the methods necessary to work with computational models of neural processing in the brain. These methods include differential equations for neuroscience, Simulink programming, linear algebra for neural networks, MATLAB coding, and probability theory for neuroscience. For each method, classes will include lectures, and computer exercises or illustrations.
Outcomes: Appreciation of the mathematical and computational techniques used to understand the brain, specially differential equations to model neurons, linear algebra used for neural networks, and probability theory capturing brain performance.
This course introduces the methods necessary to work with computational models of neural processing in the brain. These methods include differential equations for neuroscience, Simulink programming, linear algebra for neural networks, MATLAB coding, and probability theory for neuroscience. For each method, classes will include lectures, and computer exercises or illustrations.
Outcomes: Appreciation of the mathematical and computational techniques used to understand the brain, specially differential equations to model neurons, linear algebra used for neural networks, and probability theory capturing brain performance.