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Announcements
Final Announcement: Thank You and Farewell!
Hello neuroadventurers,
Congratulations on completing the Computational Neuroscience course! It’s been more than 8 weeks since we donned our adventure hats and embarked on our journey together – thank you all for your great interest and participation in the course! Thank you also for your feedback in the discussion forums – the feedback has been very helpful as we strive to improve our lectures and quizzes for the next offering of the course.
Statements of Accomplishment and Certificates have now been approved for those scoring 60% or more in the course. Coursera staff will release these Statements and Certificates within a week (you will be notified by email). Once these are released, if you have earned a Statement of Accomplishment, you can collect it in the Course Records section of your Coursera account: https://www.coursera.org/account/records.
Here are some course statistics. Overall, about 32,500 students registered for the course from 165 different countries (top 4 countries with most students: US (27%), India (10%), China (5%), UK (5%)). Among the registered students, about 21,000 participated in the course in one way or another (by accessing course material, viewing videos, or doing quizzes). Among these course participants, 3,438 submitted one or more homework quizzes. Out of these, 854 students earned a Statement of Accomplishment or Certificate (congratulations!).
For those who were unable to earn a Statement of Accomplishment or Certificate this time around, we hope you will try again in a future offering of the course. Watch the course website on Coursera for the next start date.
Once again, thank you for joining us on this short but memorable journey. As we part ways, we would like to wish each of you the very best as you continue your computational quest to understand the brain.
Sayonara and farewell,
Rajesh & Adrienne
Congratulations on completing the Computational Neuroscience course! It’s been more than 8 weeks since we donned our adventure hats and embarked on our journey together – thank you all for your great interest and participation in the course! Thank you also for your feedback in the discussion forums – the feedback has been very helpful as we strive to improve our lectures and quizzes for the next offering of the course.
Statements of Accomplishment and Certificates have now been approved for those scoring 60% or more in the course. Coursera staff will release these Statements and Certificates within a week (you will be notified by email). Once these are released, if you have earned a Statement of Accomplishment, you can collect it in the Course Records section of your Coursera account: https://www.coursera.org/account/records.
Here are some course statistics. Overall, about 32,500 students registered for the course from 165 different countries (top 4 countries with most students: US (27%), India (10%), China (5%), UK (5%)). Among the registered students, about 21,000 participated in the course in one way or another (by accessing course material, viewing videos, or doing quizzes). Among these course participants, 3,438 submitted one or more homework quizzes. Out of these, 854 students earned a Statement of Accomplishment or Certificate (congratulations!).
For those who were unable to earn a Statement of Accomplishment or Certificate this time around, we hope you will try again in a future offering of the course. Watch the course website on Coursera for the next start date.
Once again, thank you for joining us on this short but memorable journey. As we part ways, we would like to wish each of you the very best as you continue your computational quest to understand the brain.
Sayonara and farewell,
Rajesh & Adrienne
Thu 2 Jul 2015 3:40 PM CEST
Week 8: Learning to drive a truck, learning to fly a helicopter, and more...
Hello Jedis of the Computational Neuroscience world,
This is the last week of lectures and the Force of two different forms of learning beckons you: supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.
The second and third lectures will take you on a journey through the rich landscape of reinforcement learning. In the second lecture, you will learn how to predict rewards à la Pavlov's dog and explore the connection to that important reward-related chemical in our brains: dopamine.
In the third lecture, we will make friends with a rat in a barn and see how its ability to predict rewards allows it to select the best actions for maximizing rewards. We will examine a possible neural implementation of our computational model in the brain region known as the basal ganglia, before proceeding to the grand finale: flying a helicopter using reinforcement learning!
We are also pleased to release Quiz 6, your last quiz for the course, which will test your knowledge of supervised and reinforcement learning. Note that as in Quiz 5, some of the questions have variations, so the question may be different each time you attempt the quiz.
Finally, we are delighted to reveal our final surprise guest lecturer for the course: Dr. Eb Fetz. Eb is a well-known figure in neuroscience circles, widely recognized for his contributions to motor neuroscience as well as brain-computer interfacing. He was the first to demonstrate (in the 1960s) that neurons in the brain can be voluntarily controlled in order to cause an external object to move - no, we are not talking about telepathy of the sci-fi type here but actually controlling objects such as cursors and prosthetic limbs using a brain-computer interface (BCI)! You can read more about BCIs in a recent textbook authored by one of your own Coursera instructors but nothing can beat hearing about the field from one of its founding fathers, our guest lecturer Eb Fetz. We thought we should end the course with BCIs because they illustrate how concepts from computational neuroscience such as neural decoding and Hebbian plasticity play a crucial role in important real-world applications such as developing BCIs for people who are paralyzed or disabled.
We hope you have enjoyed these past 8 weeks of lectures and quizzes. If you find yourself more interested in computational neuroscience now than 8 weeks ago, we will consider ourselves to have succeeded in what we set out to do. If some of you are even thinking of joining the field to help unravel the secrets of the brain, that would be the proverbial "icing on the cake" and we offer you our strongest encouragement!
with best wishes,
Rajesh & Adrienne
This is the last week of lectures and the Force of two different forms of learning beckons you: supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.
The second and third lectures will take you on a journey through the rich landscape of reinforcement learning. In the second lecture, you will learn how to predict rewards à la Pavlov's dog and explore the connection to that important reward-related chemical in our brains: dopamine.
In the third lecture, we will make friends with a rat in a barn and see how its ability to predict rewards allows it to select the best actions for maximizing rewards. We will examine a possible neural implementation of our computational model in the brain region known as the basal ganglia, before proceeding to the grand finale: flying a helicopter using reinforcement learning!
We are also pleased to release Quiz 6, your last quiz for the course, which will test your knowledge of supervised and reinforcement learning. Note that as in Quiz 5, some of the questions have variations, so the question may be different each time you attempt the quiz.
Finally, we are delighted to reveal our final surprise guest lecturer for the course: Dr. Eb Fetz. Eb is a well-known figure in neuroscience circles, widely recognized for his contributions to motor neuroscience as well as brain-computer interfacing. He was the first to demonstrate (in the 1960s) that neurons in the brain can be voluntarily controlled in order to cause an external object to move - no, we are not talking about telepathy of the sci-fi type here but actually controlling objects such as cursors and prosthetic limbs using a brain-computer interface (BCI)! You can read more about BCIs in a recent textbook authored by one of your own Coursera instructors but nothing can beat hearing about the field from one of its founding fathers, our guest lecturer Eb Fetz. We thought we should end the course with BCIs because they illustrate how concepts from computational neuroscience such as neural decoding and Hebbian plasticity play a crucial role in important real-world applications such as developing BCIs for people who are paralyzed or disabled.
We hope you have enjoyed these past 8 weeks of lectures and quizzes. If you find yourself more interested in computational neuroscience now than 8 weeks ago, we will consider ourselves to have succeeded in what we set out to do. If some of you are even thinking of joining the field to help unravel the secrets of the brain, that would be the proverbial "icing on the cake" and we offer you our strongest encouragement!
with best wishes,
Rajesh & Adrienne
Fri 19 Jun 2015 8:59 AM CEST
Week 7: Learning about Learning
Hello relentless explorers of the compneuro world!
We have entered Week 7 which means it is time to learn about how our brains learn. The first lecture this week takes you from synaptic plasticity in the brain to a Canadian psychologist's prescient prescription for how neurons ought to learn, culminating in the revelation that our brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures introduce you to how the brain may learn models of inputs with no supervision at all, leading to recent theories of brain function based on sparse coding and predictive coding.
We are also pleased to release Quiz 5 which will entertain you with questions on feedforward and recurrent networks, as well as allow you to explore models of synapses and learning via Matlab/Octave programming.
Please note that for Quiz 5, we have written several variations of some questions, i.e., these questions can change slightly if you attempt the quiz multiple times. So if you re-attempt the quiz, please make sure to adjust your responses and code accordingly.
Thanks to everyone for keeping the discussion forums lively and answering the questions of your peers -- it's been inspiring to see a global community of computational neuroscience enthusiasts forming before our very eyes!
Next week, we will conclude the course with excursions to the legendary lands of supervised learning and reinforcement learning.
Until then,
ciao and guten tag!
Rajesh
We have entered Week 7 which means it is time to learn about how our brains learn. The first lecture this week takes you from synaptic plasticity in the brain to a Canadian psychologist's prescient prescription for how neurons ought to learn, culminating in the revelation that our brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures introduce you to how the brain may learn models of inputs with no supervision at all, leading to recent theories of brain function based on sparse coding and predictive coding.
We are also pleased to release Quiz 5 which will entertain you with questions on feedforward and recurrent networks, as well as allow you to explore models of synapses and learning via Matlab/Octave programming.
Please note that for Quiz 5, we have written several variations of some questions, i.e., these questions can change slightly if you attempt the quiz multiple times. So if you re-attempt the quiz, please make sure to adjust your responses and code accordingly.
Thanks to everyone for keeping the discussion forums lively and answering the questions of your peers -- it's been inspiring to see a global community of computational neuroscience enthusiasts forming before our very eyes!
Next week, we will conclude the course with excursions to the legendary lands of supervised learning and reinforcement learning.
Until then,
ciao and guten tag!
Rajesh
Fri 12 Jun 2015 7:00 AM CEST
A note about quiz solutions: question-level explanations for subsequent quizzes will be revealed only after the hard deadline
Starting with Quiz 4: Computing in Carbon, question-level explanations (which explain the solutions to the quiz questions) will only be visible after the hard deadline has passed. While this does prolong the time students must wait to see solutions after submitting their responses to quiz questions, which we understand can be frustrating, it gives students more of an opportunity to explore possible corrections to their solutions between the soft and hard deadlines. Please note that while the maximum score does decrease to 90% after the soft deadline, there is no penalty for submitting quiz answers up to a maximum of 10 times. Finally, if there are particular quiz questions that you find a bit tricky, we highly encourage the use of the discussion forums, as you have many knowledgeable classmates happy to provide their input and insights!
Sun 7 Jun 2015 7:44 PM CEST
More than half way home!
Hello all,
As of Friday, Rajesh has resumed the reins of the course as you proceed into the amazing world of networks--so hold on to your (adventure) hats!
As my parting note, I'd like to encourage you to give the problem sets a try (or another try as the case may be): do take advantage of the distant hard deadline and the small late penalty, make good use of our helpful online TAs and the very useful Supplementary Materials and Videos by our lead TA Rich Pang, and help each other whenever you can! If you haven't been participating in the forums, there's some great activity going on asking and answering questions and some interesting discussions as well. Thanks to those who are sharing their curiosity and expertise.
All the best with the remainder of the course and whatever you make of it in the future!
Adrienne
As of Friday, Rajesh has resumed the reins of the course as you proceed into the amazing world of networks--so hold on to your (adventure) hats!
As my parting note, I'd like to encourage you to give the problem sets a try (or another try as the case may be): do take advantage of the distant hard deadline and the small late penalty, make good use of our helpful online TAs and the very useful Supplementary Materials and Videos by our lead TA Rich Pang, and help each other whenever you can! If you haven't been participating in the forums, there's some great activity going on asking and answering questions and some interesting discussions as well. Thanks to those who are sharing their curiosity and expertise.
All the best with the remainder of the course and whatever you make of it in the future!
Adrienne
Sun 7 Jun 2015 2:00 AM CEST
Week 6: Getting up close and personal with networks!
Hello neurocitizens of the world!
Can you believe it? We are in Week 6 of the course already! (as a fun-loving brain area once said, time flies when you are having fun!) I hope you have been enjoying Prof. Fairhall's wonderful lectures.
This week, we will explore the exciting terrain of network models. To model networks of neurons, you first need to model those remarkable connections between neurons called synapses - this is the topic of the first lecture. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. The second lecture introduces you to firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture guides you through the dynamically wild world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations,...,you name it!
No quiz this week since you are still working on Quiz 4 (hope it's going well!). Questions on synapse models and networks will be included in the quiz for next week, when we enter the world of networks that learn!
Shalom and ilalliqa',
Rajesh
Can you believe it? We are in Week 6 of the course already! (as a fun-loving brain area once said, time flies when you are having fun!) I hope you have been enjoying Prof. Fairhall's wonderful lectures.
This week, we will explore the exciting terrain of network models. To model networks of neurons, you first need to model those remarkable connections between neurons called synapses - this is the topic of the first lecture. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. The second lecture introduces you to firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture guides you through the dynamically wild world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations,...,you name it!
No quiz this week since you are still working on Quiz 4 (hope it's going well!). Questions on synapse models and networks will be included in the quiz for next week, when we enter the world of networks that learn!
Shalom and ilalliqa',
Rajesh
Fri 5 Jun 2015 6:30 AM CEST
Week 5 lectures available!
Hello all,
This week's lectures are now up, in which you will venture into the world of biophysics and there make the acquaintance of some Nobel prize winners and their tiny subject, the action potential.
The next quiz is also live.
You will emerge from this week ready to network!
This week's lectures are now up, in which you will venture into the world of biophysics and there make the acquaintance of some Nobel prize winners and their tiny subject, the action potential.
The next quiz is also live.
You will emerge from this week ready to network!
Fri 29 May 2015 9:00 AM CEST
Week 4 lectures have arrived!
Hello intrepid explorers of the CompNeuro world:
It's week 4 and you have now successfully navigated to the center of the course.
This week you will learn about the intimate connections between the venerable field of information theory and that equally venerable object called our brain.
We are also pleased to present Quiz 3, which contains questions covering the material from both Week 3 and Week 4.
We hope you are enjoying your journey through the diverse land of Computational Neuroscience, especially the mathematical twists and turns that crop up from time to time to challenge your most valuable possessions (your neurons).
Adrienne and Rajesh
It's week 4 and you have now successfully navigated to the center of the course.
This week you will learn about the intimate connections between the venerable field of information theory and that equally venerable object called our brain.
We are also pleased to present Quiz 3, which contains questions covering the material from both Week 3 and Week 4.
We hope you are enjoying your journey through the diverse land of Computational Neuroscience, especially the mathematical twists and turns that crop up from time to time to challenge your most valuable possessions (your neurons).
Adrienne and Rajesh
Sat 23 May 2015 4:14 AM CEST
This week's surprise guest lecturer: Fred Rieke!
Hello everyone,
In case you hadn't noticed, this week's lectures include a guest lecture from retina master Dr. Fred Rieke. Fred's very elegant studies have shown that sensory systems are remarkably sensitive, often working at the limits of physical detection. In the retina, he has shown how neural circuitry helps to separate signal from noise.
Please view Fred's lecture here - enjoy!
In case you hadn't noticed, this week's lectures include a guest lecture from retina master Dr. Fred Rieke. Fred's very elegant studies have shown that sensory systems are remarkably sensitive, often working at the limits of physical detection. In the retina, he has shown how neural circuitry helps to separate signal from noise.
Please view Fred's lecture here - enjoy!
Tue 19 May 2015 4:14 AM CEST
Week 3: Reading minds!
Hello Everyone!
this week, we will be turning our coding questions around and asking what can we learn about what the brain is experiencing by reading the neural activity. Week 3 lectures are now available-- enjoy!
There won't be a quiz this week; the material from weeks 3 and 4 will be quizzed together.
this week, we will be turning our coding questions around and asking what can we learn about what the brain is experiencing by reading the neural activity. Week 3 lectures are now available-- enjoy!
There won't be a quiz this week; the material from weeks 3 and 4 will be quizzed together.
Fri 15 May 2015 4:14 AM CEST
Week 2: Neural coding!
Hello adventurers:
our computational neuroscience explorations will continue this week with a dive down into the world of neural coding. We will start by taking a look at the technologies that are used to record brain activity. We will then build up some model formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we will talk a little about variability and noise in the brain and how our models can accommodate that reality. Week 2 lecture videos are now available!
Since this week is starting to get more mathematical, our TAs have prepared some supplementary material which we encourage you to access and to view as needed before or throughout your viewing of the lectures. There are also now several new MATLAB tutorials available, created by Mathworks. You can find these on the Matlab & Octave Code page, which can be accessed from the sidebar.
A new quiz (Quiz 2) is also available in which you will have the opportunity to get your hands on some real data, from the famous fly visual neuron H1. H1 is a motion-sensitive neuron, and the stimulus you will be provided with is the velocity of a moving grating that was shown to a fly while the spike train you will receive was recorded. Your task will be to discover what it is about the motion of the world that drives this neuron to fire. Have fun and good luck!
Computational Neuroscience Course Staff
our computational neuroscience explorations will continue this week with a dive down into the world of neural coding. We will start by taking a look at the technologies that are used to record brain activity. We will then build up some model formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we will talk a little about variability and noise in the brain and how our models can accommodate that reality. Week 2 lecture videos are now available!
Since this week is starting to get more mathematical, our TAs have prepared some supplementary material which we encourage you to access and to view as needed before or throughout your viewing of the lectures. There are also now several new MATLAB tutorials available, created by Mathworks. You can find these on the Matlab & Octave Code page, which can be accessed from the sidebar.
A new quiz (Quiz 2) is also available in which you will have the opportunity to get your hands on some real data, from the famous fly visual neuron H1. H1 is a motion-sensitive neuron, and the stimulus you will be provided with is the velocity of a moving grating that was shown to a fly while the spike train you will receive was recorded. Your task will be to discover what it is about the motion of the world that drives this neuron to fire. Have fun and good luck!
Computational Neuroscience Course Staff
Fri 8 May 2015 4:14 AM CEST
Welcome to the Fascinating World of Computational Neuroscience!
Hello Aspiring Computational Neuroscientist!
Your journey into the brave new world of Computational Neuroscience begins today! The Computational Neuroscience course website is now officially open.
We are delighted that you will be joining us on this 8-week expedition into the computational mysteries of that marvelous machine, our brain.
Understanding how the brain works is one of the biggest challenges in science today, with profound implications not only for how we treat neurological diseases but also for how we view ourselves as humans.
The goal of this course is to introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. 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 exercises (in Matlab/Octave/Python) to gain a deeper understanding of concepts and methods introduced in the course. No prior background in neuroscience is required but familiarity with basic concepts in linear algebra, calculus, and probability theory would be useful.
Each week, we will release one lecture video, divided into short segments. We have included embedded quiz questions within the lecture videos to help you better understand the concepts introduced (you can choose to answer these or skip). This week’s lecture includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology. You will find this week’s videos under “Video Lectures” on the course website. A week-by-week schedule of lecture topics can be found in the “Syllabus & Schedule” section of the course website. No textbook is required for the course but we will roughly follow the topics covered in the book Theoretical Neuroscience by Peter Dayan and Larry Abbott (MIT Press).
The course will also feature surprise guest lectures by some leading researchers in Computational Neuroscience - you will receive announcements regarding these surprise guest lecture videos via email.
Each week, we will give you 1 homework quiz consisting of multiple-choice questions (see "Homework Quizzes" section). These will typically cover the topics in that week’s lecture. Some quiz questions may be based on the result you get after programming or analyzing data. You may use Matlab/Octave/Python for this purpose.
Mathworks has kindly agreed to provide you with a free copy of Matlab for the duration of this course. You will find instructions to download Matlab (or Octave) as well as tutorials on Matlab/Octave programming under the “Matlab & Octave Info” section of the course website. For those who prefer to use Python, similar information is available under the “Python Info” section of the website. This week’s homework quiz, which is optional, tests your familiarity with Matlab/Octave/Python programming.
Your course grade will be based on your performance in the weekly homework quizzes (there will be no exams in this course). Homeworks will typically be released on Fridays and will be due on the second Monday after release date. Late homework submissions will be accepted with a 10% penalty until the “hard deadline” (June 29, 2015, i.e., 10 days after the last set of lectures for the course is released). Each weekly homework quiz will contribute equally to your final course score (except Quiz 1, the Matlab/Octave quiz, which is optional and will not contribute to your score). You may attempt each quiz multiple times. The maximum score from all your attempts will be your score for the quiz. Your final course grade will be the average of all your quiz scores.
You will get a Certificate of Accomplishment from the instructors if your final course grade is > 60% of the maximum possible course grade.
It is important that you actively participate in the discussions on the forums (see “Discussion Forums” on the course website). We have assembled a small team of part-time teaching assistants to help us teach the course but trying to answer questions from thousands of you is a task we dare not contemplate! So please do not contact us directly via email but instead post your questions in the discussion forums so that other students can benefit from them too. If you know the answer to a question posted by one of your classmates, please do answer it in the discussion forum to earn the gratitude of not only your classmates but also the course staff! Finally, please up-vote urgent or important posts to bring them to our attention.
We encourage you to also use the “Discussion Forums” to meet CompNeuro classmates in your city, form study groups, share ideas, and maybe solve an important research problem or two.
Finally, time is the most precious commodity we humans have - thank you so much for deciding to spend it with us. We hope you will leave the course feeling enriched by the experience.
It's time to embark on our computational adventures! Your first lecture beckons…
Rajesh & Adrienne
Your journey into the brave new world of Computational Neuroscience begins today! The Computational Neuroscience course website is now officially open.
We are delighted that you will be joining us on this 8-week expedition into the computational mysteries of that marvelous machine, our brain.
Understanding how the brain works is one of the biggest challenges in science today, with profound implications not only for how we treat neurological diseases but also for how we view ourselves as humans.
The goal of this course is to introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. 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 exercises (in Matlab/Octave/Python) to gain a deeper understanding of concepts and methods introduced in the course. No prior background in neuroscience is required but familiarity with basic concepts in linear algebra, calculus, and probability theory would be useful.
Each week, we will release one lecture video, divided into short segments. We have included embedded quiz questions within the lecture videos to help you better understand the concepts introduced (you can choose to answer these or skip). This week’s lecture includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology. You will find this week’s videos under “Video Lectures” on the course website. A week-by-week schedule of lecture topics can be found in the “Syllabus & Schedule” section of the course website. No textbook is required for the course but we will roughly follow the topics covered in the book Theoretical Neuroscience by Peter Dayan and Larry Abbott (MIT Press).
The course will also feature surprise guest lectures by some leading researchers in Computational Neuroscience - you will receive announcements regarding these surprise guest lecture videos via email.
Each week, we will give you 1 homework quiz consisting of multiple-choice questions (see "Homework Quizzes" section). These will typically cover the topics in that week’s lecture. Some quiz questions may be based on the result you get after programming or analyzing data. You may use Matlab/Octave/Python for this purpose.
Mathworks has kindly agreed to provide you with a free copy of Matlab for the duration of this course. You will find instructions to download Matlab (or Octave) as well as tutorials on Matlab/Octave programming under the “Matlab & Octave Info” section of the course website. For those who prefer to use Python, similar information is available under the “Python Info” section of the website. This week’s homework quiz, which is optional, tests your familiarity with Matlab/Octave/Python programming.
Your course grade will be based on your performance in the weekly homework quizzes (there will be no exams in this course). Homeworks will typically be released on Fridays and will be due on the second Monday after release date. Late homework submissions will be accepted with a 10% penalty until the “hard deadline” (June 29, 2015, i.e., 10 days after the last set of lectures for the course is released). Each weekly homework quiz will contribute equally to your final course score (except Quiz 1, the Matlab/Octave quiz, which is optional and will not contribute to your score). You may attempt each quiz multiple times. The maximum score from all your attempts will be your score for the quiz. Your final course grade will be the average of all your quiz scores.
You will get a Certificate of Accomplishment from the instructors if your final course grade is > 60% of the maximum possible course grade.
It is important that you actively participate in the discussions on the forums (see “Discussion Forums” on the course website). We have assembled a small team of part-time teaching assistants to help us teach the course but trying to answer questions from thousands of you is a task we dare not contemplate! So please do not contact us directly via email but instead post your questions in the discussion forums so that other students can benefit from them too. If you know the answer to a question posted by one of your classmates, please do answer it in the discussion forum to earn the gratitude of not only your classmates but also the course staff! Finally, please up-vote urgent or important posts to bring them to our attention.
We encourage you to also use the “Discussion Forums” to meet CompNeuro classmates in your city, form study groups, share ideas, and maybe solve an important research problem or two.
Finally, time is the most precious commodity we humans have - thank you so much for deciding to spend it with us. We hope you will leave the course feeling enriched by the experience.
It's time to embark on our computational adventures! Your first lecture beckons…
Rajesh & Adrienne
Thu 30 Apr 2015 4:14 AM CEST