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Opiniones y comentarios de aprendices correspondientes a Neurociencia computacional por parte de Universidad de Washington

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170 revisiones

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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/Octave/Python 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....

Principales revisiones

JR

Apr 08, 2018

Extremely enlightening course on how Neuron's work and the science of computational neuroscience. Even if you don't want to get into the complex mathematics you can get a lot out of the course

AG

Jun 11, 2020

Brilliant course. For a HS student the math was challenging, but the quizzes and assignments were perfect. The tutorials and supplementary materials are super helpful. All in all, I loved it.

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76 - 100 de 169 revisiones para Neurociencia computacional

por Adam L

Jan 15, 2017

Lectures are concise; quizzes are helpful. Great introduction to computational neuroscience!

por Chinmay S H

Jul 29, 2019

I learned a great amount from this course. Now, I want to learn more about neural coding

por CongMa

Oct 09, 2016

It is fantastic. For me, it could be much better if it has Chinese lyrics~ Thanks a lot~

por VELAGA H P

May 20, 2020

Helped me understand how neurons work and model the same using some simple Python code.

por Matheus B M

Apr 07, 2017

GIves a very good introduction to the field. It was quite hard on the maths some times.

por Nilosmita B

Jul 26, 2017

Its a fantastic course for any one interested in the computational neuroscience field.

por Gustavo P

Feb 24, 2018

Excellent course! I really learned all I wanted about this topic! Really recommended!

por Arthur C

May 25, 2017

Great class for both professional in machine learning and computational neuroscience.

por José M T

Apr 14, 2017

Congratulations !!!. You have managed to explain complex knowledge in a simple way

por Faris G

Apr 05, 2020

Love it! Very quick, easy to understand course from the University of Washington.

por Debapriya H

Jan 14, 2020

i like this course very much and its helpful for neuroscience future study of me.

por Changjia C

Jan 05, 2019

Fantastic course! I enjoy it and love it very much. Thanks Rajesh and Adrienne!

por Benjamin S

Mar 17, 2018

Awesome course, awesome introduction to the mathematical backgrounds as well!

por 孙嘉秋

Mar 11, 2018

Exercellent start on the quantatative understanding of Neurons and Networks.

por Wei X

Dec 12, 2018

Enlightening! After this course, one know how the architecture came from.

por Andrés Z

Apr 18, 2018

By far one of the most complete MOOCs in the subject. Highly recommended.

por Hernan

Apr 09, 2019

Muy instructivo y entretenido! Felicitaciones a los autores del mismo.

por Palani T K

Jul 13, 2020

The teaching is good and easy to understand style of presentations..

por Shahbaz K

Jun 25, 2019

Made it really easy for me to get into this field. So very inspired.

por saurabh k p

Oct 20, 2018

Amazing Course with difficult challenges , hats off to professors :)

por Maxim Y

Sep 17, 2017

Thank you for sharing such a wonderful knowledge with the world!

por Swaraj K

Apr 21, 2018

Very nice course. Interesting Quizzes and excellent instructors

por Mehdi H

Oct 28, 2017

Thank you for an amazing experience. I really liked the course.

por Руслан К

Feb 26, 2019

Интересный курс для введения в вычислитетльную нейробиологию)

por Zhou W

Jun 14, 2020

Great course for generally view the basic contents of CN