15-874 Special Topics: Computational Neuroscience
12 units, Carnegie Mellon University
DESCRIPTION In the last few years, deep learning methods based on convolutional neural networks have produced stateoftheart performance in object and speech recognition. These neural networks have also been found to provide a reasonable approximation for the neural representations in the primate visual systems. Yet, real biological neural networks are far more intricate and complex than the current neural networks. For example, only 5 of the synapses of a neuron in the real network in the visual cortex listen to bottomup input signals yet current neural networks are primarily concerned with feedforward computation. What are the functions of the other 95 of the synapses of a neuron What could be the computational roles of the recurrent connections in the real biological circuits What other learning rules are known or implementable in the real circuits Can we develop new computational vision models and machine learning techniques from our knowledge of the cortical neural circuits We will study current relevant machine learning, computer vision and biological papers to explore the answers to these questions. Students of all levels, from undergraduates to Ph.D. students are welcome, though priority will be given to more senior students. The course will involve paper presentation and discussion, research term projects by students, and lecture presentation by professors. Please refer to httpswww.cs.cmu.educsdgradcourseschedules16.html for the most recent schedule updates.
Prerequisites: none listed
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