Coursera - Computational Neuroscience

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Added on January 28, 2015 by Bazxin Other > Tutorials
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Coursera - Computational Neuroscience (Size: 807.78 MB)
 8 - 1 - 1 Neurons as Classifiers and Supervised Learning (2557).mp432.69 MB
 6 - 3 - 3 The Fascinating World of Recurrent Networks (2535).mp432.05 MB
 6 - 1 - 1 Modeling Connections between Neurons (2428).mp431.06 MB
 6 - 1 - 1 Modeling Connections between Neurons (2428)-1.mp431.06 MB
 1 - 4 - 4 The Electrical Personality of Neurons (2302).mp430.88 MB
 5 - 5 - Guest Lecture Eric Shea-Brown (2252).mp430.33 MB
 7 - 1 - 1 Synaptic Plasticity Hebbs Rule and Statistical Learning (2417).mp430.32 MB
 7 - 3 - 3 Sparse Coding and Predictive Coding (2354).mp430.1 MB
 8 - 3 - 3 Reinforcement Learning Time for Action (1949).mp429.27 MB
 1 - 5 - 5 Making Connections Synapses (2159).mp428.28 MB
 3 - 2 - 2 Population Coding and Bayesian Estimation (2444).mp428.23 MB
 6 - 2 - 2 Introduction to Network Models (2147).mp427.4 MB
 8 - 4 - Guest Lecture Eb Fetz on Bidirectional Brain-Computer Interfaces (2006).mp427.31 MB
 7 - 2 - 2 Introduction to Unsupervised Learning (2206).mp427.21 MB
 2 - 4 - 4 Neural Encoding Feature Selection (2158).mp425.19 MB
 4 - 3 - 3 Coding Principles (1909).mp423.53 MB
 5 - 4 - 4 A Forest of Dendrites (1919).mp423.04 MB
 2 - 3 - 3 Neural Encoding Simple Models (1740).mp422.83 MB
 4 - 1 - 1 Information and Entropy (1912).mp422.8 MB
 1 - 6 - 6 Time to Network Brain Areas and their Function (1706).mp422.35 MB
 2 - 5 - 5 Neural Encoding Variability (1937).mp422.24 MB
 3 - 1 - 1 Neural Decoding and Signal Detection Theory (1855).mp421.61 MB
 4 - 2 - 2 Calculating Information in Spike Trains (1725).mp421.1 MB
 5 - 3 - 3 Simplified Model Neurons (1840).mp420.25 MB
 3 - 4 - Guest Lecture Fred Rieke (1401).mp417.42 MB
 8 - 2 - 2 Reinforcement Learning Predicting Rewards (1301).mp416.37 MB
 1 - 3 - 3 Computational Neuroscience Mechanistic and Interpretive Models (1235).mp415.89 MB
 5 - 2 - 2 Spikes (1409).mp415.88 MB
 5 - 1 - 1 Modeling Neurons (1352).mp415.86 MB
 2 - 1 - 1 What is the Neural Code (1120).mp415.36 MB
 6.3slides.pdf2.63 MB
 6.2slides_new.pdf2.36 MB
 6.1slides.pdf2.14 MB
 7.3.pdf1.73 MB
 lecture_slides_8.1.pdf1.65 MB
 7.2.pdf1.47 MB
 7.1.pdf1.39 MB
 lecture_slides_8.2.pdf865.04 KB
 1.5.pdf708.01 KB
 1.4.pdf704.03 KB


Description

MP4 | 960 x 540 (1.778) @ 30 fps + PDFs | Language: English | 848 MB

Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. You do not need to have any prior background in neuroscience to take this course.

About the Course
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

Topics covered include:

1. Basic Neurobiology
2. Neural Encoding and Decoding Techniques
3. Information Theory and Neural Coding
4. Single Neuron Models (Biophysical and Simplified)
5. Synapse and Network Models (Feedforward and Recurrent)
6. Synaptic Plasticity and Learning

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Coursera - Computational Neuroscience