The [course_title] course teaches mathematical application to neural coding and dynamics. The course covers concepts such as convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, visual system, Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory,
models of synaptic transmission, and reinforcement learning. Upon completion, you will be able to apply neutral coding.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|L1 Examples of Neural Coding, Simple Linear Regression||00:35:00|
|L2 Examples of Neural Coding, Simple Linear Regression||00:25:00|
|L3 Simple Statistics and Linear Regression||00:35:00|
|L4 Wiener-Hopf Equations and White Noise Analysis||00:30:00|
|L5 Operant Matching 1||00:25:00|
|Submit Your Assignment||00:00:00|
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