The [course_title] course teaches the mathematical concepts for understanding nonlinearity and feedback in neural networks. The course focuses the organisation of the synaptic connectivity of the brain through the explanation from both neurobiology and computer science. You will learn Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation. Other topics covered in the course are backpropagation and Hebbian learning, pattern recognition, models of perception, motor control, memory, and neural development.
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
|lec1 From Spikes to Rates||00:30:00|
|lec2 Lateral Inhibition and Feature Selectivity Part 1||00:20:00|
|lec2 Lateral Inhibition and Feature Selectivity Part 2||00:25:00|
|lec2 Lateral Inhibition and Feature Selectivity Part 3||00:10:00|
|lec3 Hamiltonian Dynamics||00:20:00|
|lec4 Antisymmetric Networks||00:20:00|
|lec5 Excitatory-Inhibitory Networks||00:40:00|
|lec6 VQ Part 1||00:40:00|
|lec6 VQ Part 2||00:45:00|
|lec7 Delta Rule||00:35:00|
|lec8 backprop Part 2||00:35:00|
|lec8 Conditioning Part 1||00:30:00|
|lec9 More Backpropagation||00:40:00|
|Submit Your Assignment||00:00:00|
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