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Voltar para Neurociência Computacional

Comentários e feedback de alunos de Neurociência Computacional da instituição Universidade de Washington

4.6
estrelas
899 classificações
214 avaliações

Sobre o curso

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....

Melhores avaliações

AG
10 de Jun de 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.

CM
14 de Jun de 2017

This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.

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151 — 175 de 213 Avaliações para o Neurociência Computacional

por Gavin J J

11 de Set de 2017

Its an eye opener

por RAMAN S

27 de Set de 2020

Excellent course

por Palis P

14 de Jun de 2020

Just amazing! :)

por Bilal C

12 de Abr de 2017

I recommend it

por Mtakuja L

3 de Abr de 2017

Nice course !

por Ekin K

16 de Mai de 2021

great course

por KUNXUN Q

28 de Abr de 2017

very helpful

por Sourabh J

5 de Nov de 2016

Good course!

por Jacob D

20 de Out de 2020

This topic combines a lot of what I find interesting so I am grateful this course exists. Before I started it, my hope was to walk away with more familiarity and a solid foundation for computational neuroscience. As far as I can tell, I was in fact able to gain a basic understanding. There were also a lot of really fascinating concepts throughout the course.

My only issue is that some ideas (probabilities and encoding for instance) gave me a lot of trouble and I felt like instructor couldn't explain these things in a way that I could understand. Sometimes they'd just toss a bunch of unnecessary big words and equations or invoke strange conventions that I'm not comfortable with, leaving me struggling behind. To be fair, there were many ideas for which the instructor also gave very helpful examples or made intuitive connections with other ideas. I wish I could elaborate better on the teaching quality, but this is only a review.

Overall, I was exposed to many new interesting concepts. I seriously hope that I might be able to work in a field similar to this one day.

por claudio g

22 de Mai de 2018

I have really liked this course,but there is a lot of statistics I didn't expect to find at the beginning. Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting. Honestly I have found a bit too condensed the part regarding the description of "cause" and all the related statistic stuff which I think should deserve some 1 or 2 videos with solved problems. All summed up, I think this course is really worth of taking. Best regards to the professors and to the mentors and to those who have given me a lot of help with their posting on the forum. Their doubts and the relative answers have really been enlightening for driving me towards a better understanding of the matter. Thank you to all of you.

por Shreyas G

18 de Jul de 2020

The course provides a really good insight into the field of computational neuroscience, touching every area possible. The experiments and real-life research work discussed throughout gives a good understanding and exposure to the field. Assignments equip the student well with the necessary skills and thought process in problem-solving while strengthening the concepts as well. However, I found the computational knowledge required for the course demanding. Though there was adequate help available, more could've been helpful, especially in python. It was a great learning experience.

por Aditya A

28 de Mar de 2019

I liked the course. I enjoyed solving the problems and I am now confident in learning more advanced concepts and getting my hands dirty in neural networks and machine learning.

I only have one complaint like suggestion, if only the TAs or the instructors could show some examples of solutions or algorithms for the concepts, it would have been much easier. Although, i have understood the concepts, I have not yet grasped the implementations of the concepts in actual codes and programs. Please update the course regarding that. Thanks a lot again to Rajesh, Adrienne and Richard.

por Moustapha M A

26 de Mai de 2018

The course over all was very good but I didnt given it five because of the following : in course 2-5 the lectures were not coherent and the there was no expalantion for how certain experiments or measurments were done and hence natural progression to associate the mathematics. The lecturer tends to speak fast and sometimes eat her words so there was absence of clarity . The lectures were not well structured . on the otherhand lectures 6-8 were much clearer in presenation and scope and more linked with the quizes.

por Steven P

13 de Nov de 2019

Really interesting overview of the concepts, math and coding necessary to understand how neurons work. The lectures are hit and miss when it comes to explaining the content, a majority of the lectures focused on derivatives and mathematical concepts which lost me. The supplementary videos, especially with Rich were really valuable and helped to synthesize some of the content. Felt like there was a ton of information packed into this course, just not all completely applicable.

por Wilder R

28 de Jun de 2017

I loved the course and the way Professors Rajesh and Adrienne conducted it. I only think the slides and lecture notes could have some more material. I'm a Software Engineer, with a background in Computer Science, but I have been far from math for quite some time (that's why I'm now doing a Cauculus 1 course). I got lost a few times in the quizzes due to lack of information.

But I loved the course and all the new knowledge I acquired. I will certainly recommend. it.

por Misael A A M

25 de Nov de 2020

This is an awesome course! I love it because it brings you the real neural part of the artificial neural networks, a thing all courses I've seen till now misses or gives at a really high level.

I don't give it 5 stars because the lectures are sometimes really boring. And I'm not complaining about the topics itself, but the videos are on average 20 minutes long, and the voices are really low, so it's really difficult to keep the focus on.

por Shengliang D

18 de Jan de 2020

The contents are well organized and arranged corresponding to the textbook Theoretical Neuroscience. There are supplementary materials for the lecture of each week. The assignments are very helpful for understanding the lectures, with code and data for Matlab, Python 2 and Python 3, which is very friendly for people who are only familiar with some of them. It would be better if the assignments could cover more about the lecture.

por Joost v T

2 de Dez de 2020

Great course with great lectures given by great people. I liked the variety of topics in the course and all the fun little jokes and trivia offered in the lectures. The quizes were of fairly high level for me, so I really feel like I've learned something new. I would have liked to have exercises before trying the quiz though. And after the quiz it was hard to see what went wrong.

por Philipp A R

29 de Out de 2021

T​he course itself is interesting and most lectures are well done. However, the difficulty of the material increases dramatically after a few weeks in, requiring a good understanding of differential equations / calculus / linear algebra / statistics. Even with some prior knowledge, I had a tough time and will probably do the course a second time.

por Wojtek P

8 de Jul de 2017

Extremely interesting subject, many ideas and methods presented. Basic disadvantage is a method of source which is closer to seminar rather than leacture. But, lost of details is acceptable due to a huge amount of material. Advanced mathematics from various areas is necessary to fully understand all the ideas. Anyway, I recommend the course.

por Víthor R F

10 de Mar de 2018

Many of the lectures do not make a plenty of sense relative to their quizzes. The lectures are rather theoretical and the quizzes are rather practical. Also, one of the professors have better didactics than the other. Either way, it was quite an adventure (my hat almost didn't survive).

por Manuel P

15 de Dez de 2017

I enjoyed the course very much and hopefully learned quite a bit about how to model neurons and some interesting new ways to look at methods like perceptrons and PCA. The course videos are short by very dense. Make sure you make enough notes and prepare enough time for all of them.

por george v

18 de Mar de 2017

Very good teaching skills by both professors and interesting guest lectures and tutorials. Assignements that demand your full attention. I would like some more depth as far as the developement of programming skills and the practice. Great intuition and explanation.

por lcy9086

15 de Mar de 2018

This course provides you with a brief introduction to computational neural science. You can benefit from it as long as you have basis in calculus and linear algebra. But for those who want to get the best from it, you need to build up your mathematics.

por Krasin G

16 de Nov de 2016

This is a very interesting course that provides many interesting ideas. At the same time it is quite challenging. Solid background in probability theory, linear algebra and signal processing is needed. Considering it "Introductory" level is misleading.