Computational Neuroscience

share ›
‹ links

Below are the top discussions from Reddit that mention this online Coursera course from University of Washington.

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function.

Computational Neuroscience Artificial Neural Network Reinforcement Learning Biological Neuron Model

Next cohort starts July 27. Accessible for free. Completion certificates are offered.

Affiliate disclosure: Please use the blue and green buttons to visit Coursera if you plan on enrolling in a course. Commissions Reddsera receives from using these links will keep this site online and ad-free. Reddsera will not receive commissions if you only use course links found in the below Reddit discussions.

Taught by
Rajesh P. N. Rao
Professor
and 1 more instructor

Offered by
University of Washington

Reddit Posts and Comments

0 posts • 29 mentions • top 26 shown below

r/learnprogramming • post
3319 points • ______DEADPOOL______
Here's a SANITIZED list of 530+ free online programming/CS courses (MOOCs) with feedback(i.e. exams/homeworks/assignments) that you can start this month (December 2016)

Preamble:

So, a submission on this sub to a huge list of MOOC courses caught my attention today as I've been trying to learn programming myself. So I look into the comments first to see what courses people were taking, when a comment caught my attention that says: "This is a great resouces but beware, OP ran some of the links through some pay site so that he profits out of traffic and hid this using bit.ly links...."

So I decided to investigate further, and sure enough. uBlock Origin blocked the first link that I clicked. It turns out, pretty much every link to the course on the post is a bit.ly link hiding a reference link to a spam site linksynergy.com. The url itself has reference id and everything. Full link:

http://click.linksynergy.com/fs-bin/click?id=SAyYsTvLiGQ&u1=reddit_learnprog_dec&subid=&offerid=451430.1&type=10&tmpid=18061&RD_PARM1=https://www.coursera.org/learn/game-programming

I posted this in the comment and reported the post for spamming, thought nothing more of it, and move on. In my goofing off, I ended up installing NVIDIA CodeWorks, and the damn installer turned out to be a download manager for installer to a bunch of stuffs it needed to install. So, I let it run and while it downloads, I thought I'd fire up Overwatch and try to climb out of gold rank, when for some reason, I thought about the MOOC post again.

Hiding a reference link using bit.ly is not only spamming, but it's also unethical because bit.ly tracks where clicks come from and the whole practice preys on the innocent who just wanted to learn some programming stuffs.

So I decided to go through the links and sanitized the bit.ly and removed all the spam links and replace it with direct links to each of the course.

Over the course of cleaning up the links, I found that OP feed all their links through two spam sites that ublock origin blocked:

The first one is the linksynergy site as I've mentioned before.

http://click.linksynergy.com/fs-bin/click?id=SAyYsTvLiGQ&u1=reddit_learnprog_dec&subid=&offerid=451430.1&type=10&tmpid=18061&RD_PARM1=https://www.coursera.org/learn/game-programming

The second one is through awin1.com

http://www.awin1.com/awclick.php?gid=295463&mid=6798&awinaffid=301045&linkid=599979&clickref=&p=https://www.edx.org/course/introduction-devops-transforming-linuxfoundationx-lfs161x

I hope this sort of shady spamming behavior is not tolerated in this sub, and doesn't happen again. But just in case another post comes up again next month, would someone be so kind as to make a bit.ly expander plugin for chrome or something to automate this cleanup without exposing the user's location, and clicking on the reference, etc. I didn't want to run this through python in case something bad happens and some dumb protocol got exposed or whatever.

Anyway, here's the full sanitized list. I've left the links bare so you can see this list has not been compromised. And if you'll excuse me, I'm going to run spybot on my system now.

Happy learning. GLHF.

BEGINNER25

Course Name|Start Date|Length in weeks|Rating :--|:--:|:--:|:--:|:--: Web Applications for Everybody https://www.wa4e.com via Others|Self paced|NA|NA Python for Everybody https://www.py4e.com - Exploring Information via Others|Self paced|NA|NA NEW Swift for Beginners https://www.udacity.com/course/swift-for-beginners--ud1022 via Udacity|Self paced|NA|NA NEW Android for Beginners https://www.udacity.com/course/android-for-beginners--ud834 via Udacity|Self paced|NA|NA NEW Introduction to R https://www.datacamp.com/courses/free-introduction-to-r via Datacamp|Self paced|NA|NA NEW Intro to Python for Data Science https://www.datacamp.com/courses/intro-to-python-for-data-science via Datacamp|Self paced|NA|NA Introduction to the Internet of Things and Embedded Systems https://www.coursera.org/learn/iot via Coursera|5th Dec|4|3.7★ CODAPPS: Coding mobile apps for entrepreneurs https://www.coursera.org/learn/codapps via Coursera|5th Dec|8|5★ How To Create a Website in a Weekend! Project-Centered Course https://www.coursera.org/learn/how-to-create-a-website via Coursera|5th Dec|3|5★ Ruby on Rails: An Introduction https://www.coursera.org/learn/ruby-on-rails-intro via Coursera|5th Dec|3|3.1★ Build a Modern Computer from First Principles: From Nand to Tetris Project-Centered Course https://www.coursera.org/learn/build-a-computer via Coursera|5th Dec|6|4.8★ HTML, CSS and JavaScript https://www.coursera.org/learn/html-css-javascript via Coursera|5th Dec|3|4.1★ Introduction to HTML5 https://www.coursera.org/learn/html via Coursera|5th Dec|3|4.1★ Code Yourself! An Introduction to Programming https://www.coursera.org/learn/intro-programming via Coursera|5th Dec|5|4.3★ Introduction to CSS3 https://www.coursera.org/learn/introcss via Coursera|5th Dec|4|4.6★ HTML, CSS, and Javascript for Web Developers https://www.coursera.org/learn/html-css-javascript-for-web-developers via Coursera|5th Dec|5|5★ Python Programming: A Concise Introduction https://www.coursera.org/learn/python-programming-introduction via Coursera|5th Dec|NA|NA Usable Security https://www.coursera.org/learn/usable-security via Coursera|12th Dec|7|2.9★ An Introduction to Interactive Programming in Python Part 2 https://www.coursera.org/learn/interactive-python-2 via Coursera|12th Dec|4|4.8★ Programming Foundations with JavaScript, HTML and CSS https://www.coursera.org/learn/duke-programming-webvia Coursera|12th Dec|4|3.8★ Introduction to Web Development https://www.coursera.org/learn/web-development via Coursera|12th Dec|NA|NA An Introduction to Interactive Programming in Python Part 1 https://www.coursera.org/learn/interactive-python-1 via Coursera|12th Dec|5|4.9★ Creative Programming for Digital Media & Mobile Apps https://www.coursera.org/learn/digitalmedia via Coursera|19th Dec|NA|4★ Learn to Program: The Fundamentals https://www.coursera.org/learn/learn-to-programvia Coursera|19th Dec|10|4.8★ Internet History, Technology, and Security https://www.coursera.org/learn/internet-history via Coursera|26th Dec|10|4.6★

INTERMEDIATE156

Course Name|Start Date|Length in weeks|Rating :--|:--:|:--:|:--:|:--: NEW Minecraft, Coding and Teaching https://www.edx.org/course/minecraft-coding-teaching-uc-san-diegox-ltm1xvia edX|Self paced|NA|NA VR Software Development https://www.udacity.com/course/vr-software-development--ud1014 via Udacity|Self paced|NA|NA NEW Swift for Developers https://www.udacity.com/course/swift-for-developers--ud1025 via Udacity|Self paced|NA|NA NEW Introduction to DevOps: Transforming and Improving Operations https://www.edx.org/course/introduction-devops-transforming-linuxfoundationx-lfs161x via edX|Self paced|NA|NA Algorithms, Part II https://www.coursera.org/learn/java-data-structures-algorithms-2 via Coursera|1st Dec|6|4.8★ Software Defined Networking https://www.coursera.org/learn/sdn via Coursera|1st Dec|NA|4.2★ NEW Probabilistic Graphical Models 3: Learning https://www.coursera.org/learn/probabilistic-graphical-models-3-learning via Coursera|1st Dec|NA|NA Single Page Web Applications with AngularJS https://www.coursera.org/learn/single-page-web-apps-with-angularjsvia Coursera|5th Dec|NA|NA Approximation Algorithms Part I https://www.coursera.org/learn/approximation-algorithms-part-1 via Coursera|5th Dec|5|5★ Managing Big Data with MySQL https://www.coursera.org/learn/analytics-mysql via Coursera|5th Dec|5|3.8★ Advanced Algorithms and Complexity https://www.coursera.org/learn/advanced-algorithms-and-complexity via Coursera|5th Dec|NA|NA Ruby on Rails Web Services and Integration with MongoDB https://www.coursera.org/learn/ruby-on-rails-web-services-mongodb via Coursera|5th Dec|4|4.8★ Advanced Styling with Responsive Design https://www.coursera.org/learn/responsivedesignvia Coursera|5th Dec|4|4.7★ Foundations of Objective-C App Development https://www.coursera.org/learn/objective-cvia Coursera|5th Dec|4|3★ Cloud Computing Concepts, Part 1 https://www.coursera.org/learn/cloud-computingvia Coursera|5th Dec|5|2.6★ Biology Meets Programming: Bioinformatics for Beginners https://www.coursera.org/learn/bioinformatics via Coursera|5th Dec|4|5★ The Arduino Platform and C Programming https://www.coursera.org/learn/arduino-platformvia Coursera|5th Dec|4|3.3★ App Design and Development for iOS https://www.coursera.org/learn/ios-app-design-development via Coursera|5th Dec|5|3★ Data Visualization https://www.coursera.org/learn/datavisualization via Coursera|5th Dec|4|3.2★ Rails with Active Record and Action Pack https://www.coursera.org/learn/rails-with-active-record via Coursera|5th Dec|4|4★ Graph Search, Shortest Paths, and Data Structures https://www.coursera.org/learn/algorithms-graphs-data-structures via Coursera|5th Dec|NA|NA The Raspberry Pi Platform and Python Programming for the Raspberry Pi https://www.coursera.org/learn/raspberry-pi-platform via Coursera|5th Dec|4|3.5★ Introduction to Spreadsheets and Models https://www.coursera.org/learn/wharton-introduction-spreadsheets-models via Coursera|5th Dec|4|4.7★ Responsive Website Basics: Code with HTML, CSS, and JavaScript https://www.coursera.org/learn/website-coding via Coursera|5th Dec|4|3.9★ Framework for Data Collection and Analysis https://www.coursera.org/learn/data-collection-framework via Coursera|5th Dec|NA|3.5★ Functional Program Design in Scala https://www.coursera.org/learn/progfun2 via Coursera|5th Dec|NA|NA Software Processes and Agile Practices https://www.coursera.org/learn/software-processes-and-agile-practices via Coursera|5th Dec|4|4.3★ Introduction to Software Product Management https://www.coursera.org/learn/introduction-to-software-product-managementvia Coursera|5th Dec|2|4.2★ Client Needs and Software Requirements https://www.coursera.org/learn/client-needs-and-software-requirements via Coursera|5th Dec|4|4.3★ Agile Planning for Software Products https://www.coursera.org/learn/agile-planning-for-software-products via Coursera|5th Dec|4|3★ Reviews & Metrics for Software Improvements https://www.coursera.org/learn/reviews-and-metrics-for-software-improvements via Coursera|5th Dec|4|NA Getting Started: Agile Meets Design Thinking https://www.coursera.org/learn/getting-started-agilevia Coursera|5th Dec|5|5★ Big Data Modeling and Management Systems https://www.coursera.org/learn/big-data-management via Coursera|5th Dec|NA|NA Best Practices for iOS User Interface Design https://www.coursera.org/learn/ui via Coursera|5th Dec|4|5★ Interfacing with the Arduino http://bit.ly/2gXagqZ via Coursera|5th Dec|4|4★ Communicating Data Science Results https://www.coursera.org/learn/data-results via Coursera|5th Dec|3|1★ Java Programming: Principles of Software Design https://www.coursera.org/learn/java-programming-design-principles via Coursera|5th Dec|4|4.7★ Object Oriented Programming in Java https://www.coursera.org/learn/object-oriented-java via Coursera|5th Dec|6|4.8★ Документы и презентации в LaTeX Introduction to LaTeX https://www.coursera.org/learn/latex via Coursera|5th Dec|5|NA Cloud Networking https://www.coursera.org/learn/cloud-networking via Coursera|5th Dec|5|4.3★ Web Application Development with JavaScript and MongoDB https://www.coursera.org/learn/web-application-development via Coursera|5th Dec|4|4.2★ Interfacing with the Raspberry Pi https://www.coursera.org/learn/raspberry-pi-interface via Coursera|5th Dec|4|1★ Data Manipulation at Scale: Systems and Algorithms https://www.coursera.org/learn/data-manipulation via Coursera|5th Dec|4|2.5★ Algorithmic Toolbox https://www.coursera.org/learn/algorithmic-toolbox via Coursera|5th Dec|5|4.7★ Toward the Future of iOS Development with Swift https://www.coursera.org/learn/iosswift via Coursera|5th Dec|4|NA Data Structures https://www.coursera.org/learn/data-structures via Coursera|5th Dec|4|2★ Server-side Development with NodeJS https://www.coursera.org/learn/server-side-development via Coursera|5th Dec|4|5★ Front-End JavaScript Frameworks: AngularJS https://www.coursera.org/learn/angular-js via Coursera|5th Dec|4|3.8★ Text Retrieval and Search Engines https://www.coursera.org/learn/text-retrieval via Coursera|5th Dec|4|3.2★ Interactivity with JavaScript https://www.coursera.org/learn/javascript via Coursera|5th Dec|4|4.3★ Cybersecurity and the Internet of Things https://www.coursera.org/learn/iot-cyber-security via Coursera|5th Dec|NA|NA Front-End Web UI Frameworks and Tools https://www.coursera.org/learn/web-frameworks via Coursera|5th Dec|4|4.3★ Managing an Agile Team https://www.coursera.org/learn/agile-team-management via Coursera|5th Dec|NA|2★ R Programming https://www.coursera.org/learn/r-programming via Coursera|5th Dec|4|2.7★ The Data Scientist’s Toolbox https://www.coursera.org/learn/data-scientists-toolsvia Coursera|5th Dec|4|3.2★ Getting and Cleaning Data https://www.coursera.org/learn/data-cleaning via Coursera|5th Dec|4|3.4★ Practical Machine Learning https://www.coursera.org/learn/practical-machine-learning via Coursera|5th Dec|4|3.4★ Exploratory Data Analysis https://www.coursera.org/learn/exploratory-data-analysis via Coursera|5th Dec|4|3.8★ Regression Models https://www.coursera.org/learn/regression-models via Coursera|5th Dec|4|2.6★ Statistical Inference https://www.coursera.org/learn/statistical-inference via Coursera|5th Dec|4|2.7★ Reproducible Research https://www.coursera.org/learn/reproducible-research via Coursera|5th Dec|4|3.7★ Software Architecture for the Internet of Things https://www.coursera.org/learn/iot-software-architecture via Coursera|5th Dec|NA|NA NEW Advanced Linear Models for Data Science 2: Statistical Linear Models https://www.coursera.org/learn/linear-models-2 via Coursera|5th Dec|NA|NA Games, Sensors and Media https://www.coursera.org/learn/games via Coursera|5th Dec|4|NA Mastering the Software Engineering Interview https://www.coursera.org/learn/cs-tech-interview via Coursera|5th Dec|4|5★ Algorithms on Graphs https://www.coursera.org/learn/algorithms-on-graphs via Coursera|5th Dec|NA|4★ Multiplatform Mobile App Development with Web Technologies https://www.coursera.org/learn/hybrid-mobile-development via Coursera|5th Dec|4|5★ Data Warehouse Concepts, Design, and Data Integration http://bit.ly/2fYB1hn via Coursera|5th Dec|5|5★ Advanced Data Structures in Java https://www.coursera.org/learn/advanced-data-structures via Coursera|5th Dec|5|NA Introduction to Data Science in Python https://www.coursera.org/learn/python-data-analysis via Coursera|5th Dec|NA|NA Algorithms on Strings https://www.coursera.org/learn/algorithms-on-strings via Coursera|5th Dec|NA|3★ Functional Programming Principles in Scala https://www.coursera.org/learn/progfun1 via Coursera|5th Dec|7|4.8★ Data Structures and Performance https://www.coursera.org/learn/data-structures-optimizing-performance via Coursera|5th Dec|5|5★ Programming Mobile Applications for Android Handheld Systems: Part 2 https://www.coursera.org/learn/android-programming-2 via Coursera|5th Dec|5|4.5★ Divide and Conquer, Sorting and Searching, and Randomized Algorithms https://www.coursera.org/learn/algorithms-divide-conquer via Coursera|5th Dec|NA|NA NEW Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming https://www.coursera.org/learn/algorithms-greedy via Coursera|5th Dec|NA|NA Build Your First Android App Project-Centered Course https://www.coursera.org/learn/android-appvia Coursera|5th Dec|5|3★ Responsive Website Tutorial and Examples https://www.coursera.org/learn/responsive-website-examples via Coursera|5th Dec|4|5★ Programming Mobile Applications for Android Handheld Systems: Part 1 https://www.coursera.org/learn/android-programming via Coursera|5th Dec|5|4.1★ Beginning Game Programming with C# https://www.coursera.org/learn/game-programming via Coursera|5th Dec|12|3.4★ Global Warming II: Create Your Own Models in Python https://www.coursera.org/learn/global-warming-model via Coursera|12th Dec|5|2★ Data Analysis Tools https://www.coursera.org/learn/data-analysis-tools via Coursera|12th Dec|4|3★ Algorithmic Thinking Part 1 https://www.coursera.org/learn/algorithmic-thinking-1 via Coursera|12th Dec|4|4.1★ Testing with Agile https://www.coursera.org/learn/agile-development via Coursera|12th Dec|NA|NA Interactive Computer Graphics https://www.coursera.org/learn/interactive-computer-graphics via Coursera|12th Dec|8|3.5★ Managing Data Analysis https://www.coursera.org/learn/managing-data-analysis via Coursera|12th Dec|1|1.8★ Cloud Computing Concepts: Part 2 https://www.coursera.org/learn/cloud-computing-2 via Coursera|12th Dec|5|4.8★ Cybersecurity and Mobility https://www.coursera.org/learn/cybersecurity-mobility via Coursera|12th Dec|NA|NA Android App Components - Intents, Activities, and Broadcast Receivers https://www.coursera.org/learn/androidapps via Coursera|12th Dec|NA|NA NEW Building Data Visualization Tools https://www.coursera.org/learn/r-data-visualization via Coursera|12th Dec|NA|NA Analysis of Algorithms https://www.coursera.org/learn/analysis-of-algorithms via Coursera|12th Dec|6|4.8★ Introduction to Meteor.js Development https://www.coursera.org/learn/meteor-development via Coursera|12th Dec|4|5★ iOS App Development Basics https://www.coursera.org/learn/ios-app-development-basics via Coursera|12th Dec|5|4★ Essential Design Principles for Tableau https://www.coursera.org/learn/dataviz-design via Coursera|12th Dec|NA|NA C++ For C Programmers, Part B https://www.coursera.org/learn/c-plus-plus-b via Coursera|12th Dec|NA|NA Data Science in Real Life https://www.coursera.org/learn/real-life-data-sciencevia Coursera|12th Dec|1|3★ Building a Data Science Team https://www.coursera.org/learn/build-data-science-team via Coursera|12th Dec|1|3.3★ Machine Learning https://www.coursera.org/learn/machine-learning via Coursera|12th Dec|11|4.8★ Principles of Computing Part 1 https://www.coursera.org/learn/principles-of-computing-1 via Coursera|12th Dec|5|4.6★ Introduction to Big Data https://www.coursera.org/learn/big-data-introduction via Coursera|12th Dec|3|2.6★ Running Product Design Sprints https://www.coursera.org/learn/running-design-sprints via Coursera|12th Dec|5|NA NEW Fundamentals of Computer Architecture https://www.coursera.org/learn/computer-architecture-fundamentals via Coursera|12th Dec|NA|NA Data Visualization and Communication with Tableau https://www.coursera.org/learn/analytics-tableau via Coursera|12th Dec|5|4★ Hadoop Platform and Application Framework https://www.coursera.org/learn/hadoop via Coursera|12th Dec|5|1.9★ A developer's guide to the Internet of Things IoT https://www.coursera.org/learn/developer-iot via Coursera|12th Dec|NA|4★ Internet of Things: Communication Technologies https://www.coursera.org/learn/internet-of-things-communication via Coursera|12th Dec|4|3★ Building R Packages https://www.coursera.org/learn/r-packages via Coursera|12th Dec|NA|NA Java Programming: Solving Problems with Software https://www.coursera.org/learn/java-programming via Coursera|12th Dec|4|3.3★ A Crash Course in Data Science https://www.coursera.org/learn/data-science-course via Coursera|12th Dec|1|3.3★ Database Management Essentials https://www.coursera.org/learn/database-management via Coursera|12th Dec|7|3.8★ Introduction to Neurohacking In R https://www.coursera.org/learn/neurohacking via Coursera|12th Dec|NA|NA The R Programming Environment https://www.coursera.org/learn/r-programming-environment via Coursera|12th Dec|NA|NA Introduction to Architecting Smart IoT Devices https://www.coursera.org/learn/iot-devices via Coursera|12th Dec|NA|NA Algorithmic Thinking Part 2 https://www.coursera.org/learn/algorithmic-thinking-2 via Coursera|12th Dec|NA|4.4★ Fundamentals of Visualization with Tableau https://www.coursera.org/learn/data-visualization-tableau via Coursera|12th Dec|NA|NA Dealing With Missing Data https://www.coursera.org/learn/missing-data via Coursera|12th Dec|NA|NA Java Programming: Arrays, Lists, and Structured Data https://www.coursera.org/learn/java-programming-arrays-lists-data via Coursera|12th Dec|4|4.3★ Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure https://www.coursera.org/learn/cloud-applications-part1 via Coursera|12th Dec|5|3.4★ Big Data Integration and Processing https://www.coursera.org/learn/big-data-integration-processing via Coursera|12th Dec|NA|NA Cybersecurity and the X-Factor https://www.coursera.org/learn/cybersecurity-and-x-factor via Coursera|12th Dec|NA|NA Advanced R Programming https://www.coursera.org/learn/advanced-r via Coursera|12th Dec|NA|NA Responsive Web Design https://www.coursera.org/learn/responsive-web-design via Coursera|12th Dec|4|3.3★ Introduction To Swift Programming https://www.coursera.org/learn/swift-programming via Coursera|12th Dec|5|1.2★ Data Management and Visualization https://www.coursera.org/learn/data-visualization via Coursera|12th Dec|4|2.4★ Principles of Computing Part 2 https://www.coursera.org/learn/principles-of-computing-2 via Coursera|12th Dec|NA|4.3★ Software Security https://www.coursera.org/learn/software-security via Coursera|12th Dec|6|4.7★ Java for Android https://www.coursera.org/learn/java-for-android via Coursera|13th Dec|4|NA NEW Building and Deploying Android App Projects https://www.coursera.org/learn/badaap via Coursera|15th Dec|NA|NA C++ For C Programmers, Part A https://www.coursera.org/learn/c-plus-plus-a via Coursera|19th Dec|NA|3.2★ Introduction to Genomic Technologies https://www.coursera.org/learn/introduction-genomics via Coursera|19th Dec|4|2.7★ Python for Genomic Data Science https://www.coursera.org/learn/python-genomics via Coursera|19th Dec|4|2.4★ Web Connectivity and Security in Embedded Systems https://www.coursera.org/learn/iot-connectivity-security via Coursera|19th Dec|NA|NA Bioinformatics: Introduction and Methods 生物信息学: 导论与方法 https://www.coursera.org/learn/bioinformatics-pku via Coursera|19th Dec|14|NA Statistics for Genomic Data Science https://www.coursera.org/learn/statistical-genomics via Coursera|19th Dec|4|2★ Web Application Development: Basic Concepts https://www.coursera.org/learn/web-app via Coursera|19th Dec|NA|NA 算法设计与分析 Design and Analysis of Algorithms https://www.coursera.org/learn/algorithms via Coursera|19th Dec|13|NA Genomic Data Science with Galaxy https://www.coursera.org/learn/galaxy-project via Coursera|19th Dec|4|1.8★ Discrete Optimization https://www.coursera.org/learn/discrete-optimization via Coursera|19th Dec|9|4.3★ Programming Languages, Part B https://www.coursera.org/learn/programming-languages-part-b via Coursera|19th Dec|NA|NA Build Your Own iOS App https://www.coursera.org/learn/build-app via Coursera|19th Dec|NA|NA 面向对象技术高级课程(The Advanced Object-Oriented Technology) https://www.coursera.org/learn/aoo via Coursera|19th Dec|12|NA Julia Scientific Programming https://www.coursera.org/learn/julia-programming via Coursera|19th Dec|NA|NA Computer Architecture https://www.coursera.org/learn/comparch via Coursera|19th Dec|11|4.5★ Internet of Things: How did we get here? https://www.coursera.org/learn/internet-of-things-history via Coursera|26th Dec|2|2★ Big Data, Cloud Computing, & CDN Emerging Technologies https://www.coursera.org/learn/big-data-cloud-computing-cdnvia Coursera|26th Dec|3|3.3★ Wireless Communication Emerging Technologies https://www.coursera.org/learn/wireless-communication-technologiesvia Coursera|26th Dec|5|3.7★ Internet of Things & Augmented Reality Emerging Technologies https://www.coursera.org/learn/iot-augmented-reality-technologies via Coursera|26th Dec|2|2.5★ Algorithms, Part I https://www.coursera.org/learn/introduction-to-algorithms via Coursera|26th Dec|6|4.4★ Cryptography I https://www.coursera.org/learn/cryptovia Coursera|26th Dec|7|4.7★ Programming Languages, Part C https://www.coursera.org/learn/programming-languages-part-c via Coursera|26th Dec|NA|NA Architecting Smart IoT Devices https://www.coursera.org/learn/iot-architecture via Coursera|26th Dec|NA|NA Internet of Things: Sensing and Actuation From Devices https://www.coursera.org/learn/internet-of-things-sensing-actuation via Coursera|26th Dec|6|NA Internet of Things: Setting Up Your DragonBoard™ Development Platform https://www.coursera.org/learn/internet-of-things-dragonboard via Coursera|26th Dec|10|3★ Process Mining: Data science in Action https://www.coursera.org/learn/process-mining via Coursera|26th Dec|6|4.3★ Internet Emerging Technologies https://www.coursera.org/learn/internet-technologies via Coursera|26th Dec|3|3★ Cryptography https://www.coursera.org/learn/cryptography via Coursera|26th Dec|7|4.2★

ADVANCED42

Course Name|Start Date|Length in weeks|Rating :--|:--:|:--:|:--:|:--: NEW Combining and Analyzing Complex Data https://www.coursera.org/learn/data-collection-analytics-project via Coursera|1st Dec|NA|NA NEW Recommender Systems: Evaluation and Metrics https://www.coursera.org/learn/recommender-metrics via Coursera|1st Dec|NA|NA Genomic Data Science and Clustering Bioinformatics V https://www.coursera.org/learn/genomic-data via Coursera|1st Dec|2|3.5★ Regression Modeling in Practice https://www.coursera.org/learn/regression-modeling-practice via Coursera|2nd Dec|4|5★ Genome Sequencing Bioinformatics II https://www.coursera.org/learn/genome-sequencing via Coursera|5th Dec|4|5★ Big Data, Genes, and Medicine https://www.coursera.org/learn/data-genes-medicine via Coursera|5th Dec|NA|NA Probabilistic Graphical Models 1: Representation https://www.coursera.org/learn/probabilistic-graphical-models via Coursera|5th Dec|11|4.4★ Parallel programming https://www.coursera.org/learn/parprog1 via Coursera|5th Dec|NA|5★ Machine Learning With Big Data https://www.coursera.org/learn/big-data-machine-learning via Coursera|5th Dec|4|1.8★ Comparing Genes, Proteins, and Genomes Bioinformatics III https://www.coursera.org/learn/comparing-genomes via Coursera|5th Dec|5|5★ Relational Database Support for Data Warehouses https://www.coursera.org/learn/dwrelational via Coursera|5th Dec|5|2★ Machine Learning Foundations: A Case Study Approach https://www.coursera.org/learn/ml-foundations via Coursera|5th Dec|6|4.2★ Finding Mutations in DNA and Proteins Bioinformatics VI https://www.coursera.org/learn/dna-mutations via Coursera|5th Dec|5|NA Approximation Algorithms Part II https://www.coursera.org/learn/approximation-algorithms-part-2 via Coursera|5th Dec|4|NA Machine Learning: Regression https://www.coursera.org/learn/ml-regression via Coursera|5th Dec|6|4.7★ Finding Hidden Messages in DNA Bioinformatics I https://www.coursera.org/learn/dna-analysis via Coursera|5th Dec|4|4.5★ Machine Learning: Classification https://www.coursera.org/learn/ml-classification via Coursera|5th Dec|7|4.8★ Pattern Discovery in Data Mining https://www.coursera.org/learn/data-patterns via Coursera|5th Dec|4|2.2★ Graph Analytics for Big Data https://www.coursera.org/learn/big-data-graph-analytics via Coursera|5th Dec|4|2.4★ Machine Learning: Clustering & Retrieval https://www.coursera.org/learn/ml-clustering-and-retrieval via Coursera|5th Dec|NA|4.5★ Practical Predictive Analytics: Models and Methods https://www.coursera.org/learn/predictive-analytics via Coursera|5th Dec|4|2.5★ Developing Data Products https://www.coursera.org/learn/data-products via Coursera|5th Dec|4|3.9★ Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud http://bit.ly/2gXcF52 via Coursera|5th Dec|NA|NA Introduction to Recommender Systems: Non-Personalized and Content-Based https://www.coursera.org/learn/recommender-systems-introduction via Coursera|5th Dec|NA|NA Hardware Security https://www.coursera.org/learn/hardware-security via Coursera|5th Dec|6|3★ Cluster Analysis in Data Mining https://www.coursera.org/learn/cluster-analysis via Coursera|12th Dec|4|2.6★ Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions https://www.coursera.org/learn/descriptive-statistics-statistical-distributions-business-application via Coursera|12th Dec|NA|NA Text Mining and Analytics https://www.coursera.org/learn/text-mining via Coursera|12th Dec|4|3.7★ Nearest Neighbor Collaborative Filtering https://www.coursera.org/learn/collaborative-filtering via Coursera|12th Dec|NA|NA Machine Learning for Data Analysis https://www.coursera.org/learn/machine-learning-data-analysis via Coursera|12th Dec|4|3★ Probabilistic Graphical Models 2: Inference https://www.coursera.org/learn/probabilistic-graphical-models-2-inference via Coursera|19th Dec|NA|NA Computational Neuroscience https://www.coursera.org/learn/computational-neuroscience via Coursera|19th Dec|8|3.8★ Algorithms for DNA Sequencing https://www.coursera.org/learn/dna-sequencing via Coursera|19th Dec|4|4.5★ Bioconductor for Genomic Data Science https://www.coursera.org/learn/bioconductor via Coursera|19th Dec|4|3.3★ System Validation 2: Model process behaviour https://www.coursera.org/learn/system-validation-behavior via Coursera|19th Dec|NA|NA System Validation: Automata and behavioural equivalences https://www.coursera.org/learn/automata-system-validation via Coursera|26th Dec|NA|NA Advanced Linear Models for Data Science 1: Least Squares https://www.coursera.org/learn/linear-models via Coursera|26th Dec|NA|NA Big Data Science with the BD2K-LINCS Data Coordination and Integration Center https://www.coursera.org/learn/bd2k-lincs via Coursera|26th Dec|7|4★ Neural Networks for Machine Learning https://www.coursera.org/learn/neural-networks via Coursera|26th Dec|8|4.5★ Hands-on Text Mining and Analytics https://www.coursera.org/learn/text-mining-analytics via Coursera|26th Dec|NA|NA Quantitative Formal Modeling and Worst-Case Performance Analysis https://www.coursera.org/learn/quantitative-formal-modeling-1 via Coursera|26th Dec|4|4★ Embedded Hardware and Operating Systems https://www.coursera.org/learn/embedded-operating-system via Coursera|26th Dec|NA|NA

r/AskReddit • comment
8 points • potato_leak_soup

Check out Computational Neuroscience my friend. Here's a link to a Coursera course for the subject that start's March 11th. Give it a shot to see if you like the field. It's what I was going to school for before I fell into something else, still may go back someday. I'd be happy to discuss the field with you if you like.

r/learnprogramming • post
1144 points • dhawal
Here's a list of 430+ free online programming/CS courses (MOOCs) with feedback(i.e. exams/homeworks/assignments) that you can start this month (September 2016)

Unfortunately I couldn't fit all the courses here because of Reddit's 40,000 character limit. So I removed older self-paced courses from the list. These courses are always open for registration.

They can be found here:

~300 Self Paced Programming and Computer Science courses

I have also started categorizing the courses listed here by the programming language they are taught in. You can find the list here:

~250 MOOCs categorized by Programming Language

This is not the complete list of MOOCs starting in September 2016, just the ones relevant to this community. The complete list of courses starting in September 2016 can be found over at Class Central (1600+ courses). I maintain a much bigger list of these courses over at Class Central

Get this list every month via email : Subscribe

NOTE: Unfortunately Coursera has converted many of its courses to 'Premium Grading'. Which basically means that you need to pay if you want to access graded assignments :(. You can also apply for Financial Aid - https://learner.coursera.help/hc/en-us/articles/209819033-Apply-for-Financial-Aid

BEGINNER(14)

Course Name|Start Date|Length (in weeks)|Rating :--|:--:|:--:|:--:|:--: Java Programming Basics via Udacity|Self paced|NA|NA Learn to Program: Crafting Quality Code via Coursera|1st Sep|10|4.5★ (6) Learn to Program: The Fundamentals via Coursera|1st Sep|10|4.8★ (81) Programming for Everybody (Getting Started with Python) via Coursera|5th Sep|7|4.6★ (37) Programming and the Web for Beginners via Coursera|5th Sep|4|3.8★ (9) Internet History, Technology, and Security via Coursera|5th Sep|10|4.6★ (28) Introduction to CSS3 via Coursera|5th Sep|4|4.6★ (7) The Beauty and Joy of Computing - CS Principles Part 1 via edX|6th Sep|NA|4★ (1) CODAPPS: Coding mobile apps for entrepreneurs via Coursera|12th Sep|8|5★ (1) Code Yourself! An Introduction to Programming via Coursera|12th Sep|5|4.3★ (6) An Introduction to Interactive Programming in Python (Part 2) via Coursera|19th Sep|4|4.8★ (40) Usable Security via Coursera|19th Sep|7|2.9★ (8) An Introduction to Interactive Programming in Python (Part 1) via Coursera|19th Sep|5|4.9★ (2816) Paradigms of Computer Programming – Fundamentals via edX|26th Sep|5|5★ (2) INTERMEDIATE(94)

Course Name|Start Date|Length (in weeks)|Rating :--|:--:|:--:|:--:|:--: [NEW] M233: Getting Started with Spark and MongoDB via MongoDB University|Self paced|NA|NA Android Basics: Networking via Udacity|Self paced|NA|NA [NEW] Dynamic Web Applications with Sinatra via Udacity|Self paced|NA|NA [NEW] The MVC Pattern in Ruby via Udacity|Self paced|NA|NA [NEW] Deploying Applications with Heroku via Udacity|Self paced|NA|NA [NEW] Intro to JavaScript via Flatiron School|Self paced|NA|NA [NEW] Android Basics: Data Storage via Udacity|Self paced|NA|NA Analysis of Algorithms via Coursera|1st Sep|6|4.8★ (4) Malicious Software and its Underground Economy: Two Sides to Every Story via Coursera|1st Sep|NA|3.8★ (5) Algorithms, Part II via Coursera|1st Sep|6|4.8★ (18) [NEW] Agile Software Development via edX|1st Sep|NA|NA Software Defined Networking via Coursera|1st Sep|NA|4★ (5) Algorithms, Part I via Coursera|1st Sep|6|4.4★ (37) Software Processes and Agile Practices via Coursera|1st Sep|4|4.3★ (9) Introduction to Software Product Management via Coursera|1st Sep|2|4.2★ (10) Client Needs and Software Requirements via Coursera|1st Sep|4|4.3★ (6) Reviews & Metrics for Software Improvements via Coursera|1st Sep|4|NA [NEW] Programming Mobile Services for Android Handheld Systems: Content via Coursera|1st Sep|NA|NA Programming Mobile Services for Android Handheld Systems: Concurrency via Coursera|1st Sep|NA|5★ (2) Agile Planning for Software Products via Coursera|1st Sep|4|3★ (2) Programming Languages, Part A via Coursera|5th Sep|NA|4.9★ (16) Introduction To Swift Programming via Coursera|5th Sep|5|1.2★ (5) Data Management and Visualization via Coursera|5th Sep|4|2.4★ (5) Cybersecurity and Mobility via Coursera|5th Sep|NA|NA Data Analysis Tools via Coursera|5th Sep|4|3★ (3) Managing Data Analysis via Coursera|5th Sep|1|1.8★ (6) Python Data Structures via Coursera|5th Sep|7|4.4★ (29) Using Python to Access Web Data via Coursera|5th Sep|6|4.5★ (28) Using Databases with Python via Coursera|5th Sep|5|4.5★ (17) iOS App Development Basics via Coursera|5th Sep|5|4★ (2) Testing with Agile via Coursera|5th Sep|NA|NA Cloud Computing Concepts: Part 2 via Coursera|5th Sep|5|4.8★ (4) [NEW] Single Page Web Applications with AngularJS via Coursera|5th Sep|NA|NA Introduction to Meteor.js Development via Coursera|5th Sep|4|5★ (3) Internet of Things: Setting Up Your DragonBoard™ Development Platform via Coursera|5th Sep|10|3★ (3) Algorithms: Design and Analysis, Part 1 via Coursera|5th Sep|6|4.7★ (52) Cryptography I via Coursera|5th Sep|7|4.7★ (38) Running Product Design Sprints via Coursera|5th Sep|5|NA Algorithms: Design and Analysis, Part 2 via Coursera|5th Sep|6|4.8★ (16) [NEW] Programming Languages, Part B via Coursera|5th Sep|NA|NA Dealing With Missing Data via Coursera|5th Sep|NA|NA Machine Learning via Coursera|5th Sep|11|4.8★ (204) Cryptography via Coursera|5th Sep|7|4.2★ (6) Introduction to Big Data via Coursera|5th Sep|3|2.6★ (27) Algorithmic Toolbox via Coursera|5th Sep|5|4.7★ (6) Data Visualization and Communication with Tableau via Coursera|5th Sep|5|4★ (7) Database Management Essentials via Coursera|5th Sep|7|3.8★ (4) Java Programming: Solving Problems with Software via Coursera|5th Sep|4|3.3★ (8) Front-End Web UI Frameworks and Tools via Coursera|5th Sep|4|4.3★ (6) Hadoop Platform and Application Framework via Coursera|5th Sep|5|1.9★ (19) Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure via Coursera|5th Sep|5|3.4★ (7) A developer's guide to the Internet of Things (IoT) via Coursera|5th Sep|NA|4★ (1) Big Data, Cloud Computing, & CDN Emerging Technologies via Coursera|5th Sep|3|3.3★ (4) Algorithms on Strings via Coursera|5th Sep|NA|3★ (1) Process Mining: Data science in Action via Coursera|5th Sep|6|4.3★ (12) Java Programming: Arrays, Lists, and Structured Data via Coursera|5th Sep|4|4.3★ (3) Introduction to Process Mining with ProM via FutureLearn|5th Sep|4|NA Responsive Web Design via Coursera|5th Sep|4|3.3★ (10) Multiplatform Mobile App Development with Web Technologies via Coursera|5th Sep|4|5★ (1) Mastering the Software Engineering Interview via Coursera|5th Sep|4|5★ (1) Big Data Integration and Processing via Coursera|5th Sep|NA|NA Java for Android via Coursera|6th Sep|4|NA Knowledge Management and Big Data in Business via edX|6th Sep|6|3.5★ (2) Foundations of Data Analysis - Part 1: Statistics Using R via edX|6th Sep|6|4★ (1) Programming Mobile Applications for Android Handheld Systems: Part 2 via Coursera|12th Sep|5|4.5★ (12) Approximation Algorithms Part I via Coursera|12th Sep|5|5★ (2) Front-End JavaScript Frameworks: AngularJS via Coursera|12th Sep|4|3.8★ (4) Beginning Game Programming with C# via Coursera|12th Sep|12|3.4★ (14) Programming Mobile Applications for Android Handheld Systems: Part 1 via Coursera|12th Sep|5|4.1★ (35) Software Architecture for the Internet of Things via Coursera|12th Sep|NA|NA HTML5 Part 2: Advanced Techniques for Designing HTML5 Apps via edX|13th Sep|8|3★ (1) The Nature of Code via Kadenze|14th Sep|5|5★ (14) Learning From Data (Introductory Machine Learning) via edX|18th Sep|10|4.4★ (16) Interactive Computer Graphics via Coursera|19th Sep|8|3.5★ (2) Principles of Computing (Part 1) via Coursera|19th Sep|5|4.6★ (25) [NEW] Data Analysis for Social Scientists via edX|19th Sep|NA|NA Algorithmic Thinking (Part 2) via Coursera|19th Sep|NA|4.4★ (8) Introduction to Architecting Smart IoT Devices via Coursera|19th Sep|NA|NA Internet of Things: Communication Technologies via Coursera|19th Sep|4|3★ (2) Introduction to Neurohacking In R via Coursera|19th Sep|NA|NA Principles of Computing (Part 2) via Coursera|19th Sep|NA|4.3★ (14) [NEW] Getting started with Augmented Reality via Coursera|19th Sep|NA|NA Global Warming II: Create Your Own Models in Python via Coursera|19th Sep|5|2★ (1) [NEW] Functional Programming in Haskell: Supercharge Your Coding via FutureLearn|19th Sep|NA|NA Software Security via Coursera|19th Sep|6|4.7★ (20) Algorithmic Thinking (Part 1) via Coursera|19th Sep|4|4.1★ (13) Programming Languages, Part A via Coursera|19th Sep|NA|4.9★ (16) Agile Development Using Ruby on Rails - Advanced via edX|20th Sep|8|4.6★ (5) [NEW] Algorithms via edX|20th Sep|6|NA Build Your Own iOS App via Coursera|26th Sep|NA|NA Moving to the Cloud via Coursera|26th Sep|NA|NA [NEW] Introduction to Data Science in Python via Coursera|26th Sep|NA|NA [NEW] Software Construction in Java via edX|26th Sep|NA|NA Client Needs and Software Requirements via Coursera|26th Sep|4|4.3★ (6) ADVANCED(26)

Course Name|Start Date|Length (in weeks)|Rating :--|:--:|:--:|:--:|:--: Bitcoin and Cryptocurrency Technologies via Coursera|1st Sep|7|4.6★ (9) Neural Networks for Machine Learning via Coursera|1st Sep|8|4.5★ (11) [NEW] Combining and Analyzing Complex Data via Coursera|1st Sep|NA|NA [NEW] Nearest Neighbor Collaborative Filtering via Coursera|1st Sep|NA|NA [NEW] Machine Learning: Recommender Systems & Dimensionality Reduction via Coursera|1st Sep|NA|NA [NEW] System Validation: Automata and behavioural equivalences via Coursera|5th Sep|NA|NA Machine Learning for Data Analysis via Coursera|5th Sep|4|3★ (3) Advanced Linear Models for Data Science 1 : Linear Models via Coursera|5th Sep|NA|NA [NEW] Introduction to Recommender Systems: Non-Personalized and Content-Based via Coursera|5th Sep|NA|NA Introduction to Natural Language Processing via Coursera|5th Sep|NA|3.8★ (6) Big Data: Statistical Inference and Machine Learning via FutureLearn|5th Sep|2|4★ (2) Quantitative Formal Modeling and Worst-Case Performance Analysis via Coursera|5th Sep|4|4★ (2) Machine Learning: Regression via Coursera|5th Sep|6|4.7★ (13) Introduction to Recommender Systems via Coursera|5th Sep|8|3.6★ (19) [NEW] Reliable Distributed Algorithms, Part 1 via edX|5th Sep|NA|NA Text Mining and Analytics via Coursera|5th Sep|4|3.7★ (6) Machine Learning: Clustering & Retrieval via Coursera|5th Sep|NA|4.5★ (2) Approximation Algorithms Part II via Coursera|12th Sep|4|NA [NEW] Cloud Computing Applications, Part 2 via Coursera|12th Sep|NA|NA Clinical Bioinformatics: Unlocking Genomics in Healthcare via FutureLearn|19th Sep|5|NA Machine Learning: Classification via Coursera|19th Sep|7|4.8★ (6) [NEW] Advanced Apache Spark for Data Science and Data Engineering via edX|21st Sep|2|NA Computational Neuroscience via Coursera|23rd Sep|8|3.8★ (6) Modeling Discrete Optimization via Coursera|26th Sep|8|4★ (5) [NEW] Advanced Java Concurrency via Coursera|26th Sep|NA|NA Computational Neuroscience via Coursera|26th Sep|8|3.8★ (6)

r/neuroscience • comment
3 points • saoirsedlagarza

https://www.coursera.org/learn/computational-neuroscience perhaps this will give you an insight on the "how."

r/neuro • comment
6 points • eftm

It's not necessarily getting at some of the neuroscience topics that have loosely inspired some AI, nor is it necessarily that comprehensive, but you might consider this computational neuroscience Coursera:

https://www.coursera.org/learn/computational-neuroscience

​

Part of the problem is that neuroscience is much more diverse than ML is, IMO. Many subfields have almost completely disjoint background knowledge requirements.

r/neuroscience • comment
8 points • userpb

Python is your best option yes, I would say by far (with Matlab being gradually phased out it seems and code migrated to Python, but you still find old school fanatics :). You probably do not need to be an absolute expert, advanced level will get you through almost anything, and google/colleagues will carry you over the rest.
Linear algebra and ODE are the minimum I would say yes, and statistics have always been required in science. Additionally, with the advent of machine learning, statistics are now even more critical. I would suggest to gulp down on that, there are some online courses (can not recommend a specific one right now, but there are tons), and many summer schools on this field (I went to the MLSS machine learning summer school) and I have found it to be great, can recommend, speakers were top class and available. You can usually apply for funding or travel grants.

Regarding books, here is a list that was put up in my lab. For example, I would recommend browsing:

  • Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Laurence F. Abbott (Author), Peter Dayan (Author)
  • Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition by Wulfram Gerstner (Author), Werner M. Kistler (Author), Richard Naud (Author), Liam Paninski (Author)

These are not really introductory books, but they cover quite a lot. You will get an idea of where you feel you need to read up with respect to math for example. If they are too technical, then I could suggest some online course on coursera (e.g. computational neuroscience from UWashington) or edX (e.g. those by EPFLx).

r/neuroscience • comment
2 points • neurocubed

Check out computational neuroscience on coursera. It gave me a really useful background before taking more advanced machine learning courses.

r/udub • comment
1 points • AbjectKaleidoscope4

Haven't taken it but I think it's also offered on Coursera if you want to preview the video lectures for information about how the instructors teach: https://www.coursera.org/learn/computational-neuroscience

r/ArtificialInteligence • comment
1 points • inkbleed

https://www.coursera.org/learn/computational-neuroscience this is basically the same as the one I did, by University of Washington. Amazing world class lecturers but they don't necessarily require you to know more than maybe senior high school math. There's tests after each lesson and discussion groups with other students to help you learn.

That said, there might be other similar coursera courses that are more suitable to your interests personally, definitely recommend checking it out, it was a huge game changer for me personally :) good luck!

r/reinforcementlearning • comment
2 points • abstractcontrol

I went through this one two years back and it was fine for what it set out to do, but it won't really give you any insights regarding machine learning if that is what you are expecting. I remember it covering rather low level details of the brain's functioning which is not that useful to know unless you are specifically interested in that.

I'd rather recommend instead the recent talks on Computational Theories of the Brain.

r/neuro • comment
1 points • purpletrip

Perhaps have a look at coursera? They offer online courses in Computational Neuroscience
https://www.coursera.org/learn/computational-neuroscience#about

r/neuroscience • comment
1 points • 3HATb

Hi,

I got a PhD in computational neuroscience and used to TA Master students on cognitive neuroscience program. In my opinion:

1) Peter Dayan book Theoretical neuroscience is pretty good one. But it is rather a text-book, it gives clear math description for every topic. Biophysics of neurons would be clear for someone with Physics background, while reinforcement learning would suggest that you have some computer science knowledge.

2) The brain from inside out by Georgy Buzaki. It is really good one, this might require some previous neuroscience knowledge and might be hard to read if you do not have a neuroscience background. But the book is story-driven and gives a lot of good references from one of the top neuroscientists in the world.

3) Also, the online course, like this one might provide you a good overview of different theories: https://www.coursera.org/learn/computational-neuroscience

Good luck with your studies! :)

r/neuroscience • comment
1 points • jndew

Good for you! I've enjoyed several free courses through Coursera. For a fascinating introduction without much prerequisite, you could try:

https://www.coursera.org/learn/synapses

For a little more meat, but requiring more math and computer experience than you might yet have gained so far:

https://www.coursera.org/learn/computational-neuroscience

Coursera offers a variety of other classes that might suite you. And there are other MOOC sites that do as well. Have fun & good luck! /jd

r/neuro • comment
1 points • flaminglasrswrd

Here's a course on Computational Neuroscience that starts today on Coursera https://www.coursera.org/learn/computational-neuroscience

And a paper discussing python for neurosci https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4396193/

r/coursera • comment
4 points • publiclass

Enter the course in "My Course" and delete "/home/welcome" from the address.

For example, you need to visit https://www.coursera.org/learn/machine-learning/

instead of https://www.coursera.org/learn/machine-learning/home/welcome

to see the sign that says "Financial aid available" near the "Go to course" button.

r/neurallace • comment
1 points • RajshekarReddy

These two are great starting courses. I’ve personally done the first one, and about 50% of the way through the second one. And like it’s already been said before, having a project to focus and apply what you’ve learnt is really important.

The first course is more on statistical analysis I.e Neural encoding, Neural decoding etc. The second course is about the signal processing side of things. I’d suggest start with the second course.

1. Computational Neuroscience, University of Washington

2. Neural signal analysis - Mike Cohen, Neuroscientist

r/neuroscience • post
6 points • Executer13
Resources to Learn Python for Computational Neurosciences

Hello,


TL;DR: I am learning Python and as soon as I am more acquainted with it I would like to use it in neurosciences. Do you recommend any resources to learn computational neuroscience? I would prefer something along "MatLab for Neuroscientists", but Python-related. I listed some resources below, not sure if any of those is good. Thank you.


I am looking for resources to learn Python, with the intent of improving my skills, and maybe, some day, find an opportunity for research.

I think I have a somewhat solid background in neurosciences (having read several books and keeping up to date with the latest papers). I don't know advanced mathematics or physics, but I'm confident I can learn. I know statistics, so that's something.

I have tried to learn Python in the past but, due to unexpected circumstances, I failed -- but I did not give up. I am now once again trying to get acquainted with the language. My plan to learn basic Python is:

Most people say that the best way to learn is by doing some small projects, so after knowing the basic of Python I was hoping I could start learning about Python applied to Neuroscience and start a small project. The problem is: I don't know how to make the transition. Here are the resources I found on computational neuroscience (mostly):

As you can see, I did my research. The problem is I don't know where to begin. So my questions are:

  1. If you are into computational neuroscience, does any of these resources seem to be appropriate for me, based on my background?
  2. Are there any other resources I should know about, and which ones?

Thank you very much for taking the time to read this.

Have a nice day!

r/askscience • comment
2 points • pianobutter

Then I highly recommend this site. You can download neural data and analyze it yourself. MATLAB is the current lab standard, but Python is slowly gaining ground. Most guides use the former, but you should be able to use the latter as well.

Theoretical Neuroscience by Dayan and Abbott is the "main" textbook for computational neuroscience. Rajesh Rao has a Coursera course that I highly recommend as well. He wrote this highly influential paper on predictive coding in the visual cortex. Predictive coding is probably the best framework we currently have for understanding how the brain works. This paper should get you excited about the topic.

r/compmathneuro • comment
1 points • pianobutter

MATLAB is still the most common software, while there is an active effort to move people toward Python. Rao's course on Computational Neuroscience covers both. Abbott and Dayan's Theoretical Neuroscience is a good companion (though it is a bit dated). Personally, I would recommend Python. Google's Colab is a great resource! I can't recommend it highly enough.

Fundamentals of Computational Neuroscience by Thomas Trappenberg is a nice and soft entry with coding examples and exercises. Considering the two books you've dealt with already, you should be able to rush through it in no time.

You're going to need linear algebra and calculus. Also, you'll need statistics. Bayesian inference is very popular these days, so you might want to look into that as well.

As for neurobiology itself, Kandel's Principles of Neural Science is the standard textbook. It's the Bible of the field for a reason. It really is an exellent reference work. Of course, you shouldn't read the whole thing. Read the chapters that you feel are relevant. The early ones are the most important ones, in my opinion.

r/MLQuestions • post
1 points • BroscientistsHateHim
Temporal aspects of computational neuroscience vs Machine Learning

I have started the computational neuroscience course here and found it interesting for giving a bit of biological context to neural nets and machine learning.

One thing that has been bothering me though is that it suggests there is a very fundamental difference between current state of biology understanding and current state of ML and deep learning. Lacking any better knowledge about the research in the area, I might call it the 'temporal' or 'phase' aspect. In the standard ML models the output of an individual artificial neuron is a real number that represents the firing rate in the biological context. If we add a second artificial neuron which has an output firing rate, and both neurons are inputs to the next layer, modeling the firing rate with a single number that is time invariant seems to pretty dramatically dumb down the model because it ignores the phase of the two firing rates which is extremely important in the biological behavior. In biology we know the simultaneous appearance of input spikes is much more likely to trigger a spike in the downstream neuron, and that behavior is critical for many circuits - for example in coincidence circuits. It also plays a major role in how the biological learning algorithm takes place by effectively increasing the weights of neurons that are phase locked. In other words, from the biological perspective we know that the firing rate of the input neurons is not the only important part but also that the individual spikes can occur at the same time across multiple input neurons, and when that occurs the activation of the output neuron is far more likely. And we lose all of that nuance when we model the firing rate with a fixed number.

Of course I understand that making the output of an artificial neuron some complex function of time would necessarily make ANNs much more computationally expensive, but I find it surprising that this topic isn't discussed much at all.

On to my questions

  • Is there a rationalization for why it is OK to ignore this aspect in our ML models?
  • Is there a domain within ML or a specific library/type of neural net that tries to account for this?

r/neuroscience • comment
1 points • Spaceandbrains

https://www.coursera.org/learn/computational-neuroscience , https://www.coursera.org/learn/neural-networks-deep-learning , other courses on edx, coursera, ecornell are quite good? human brain project or other online resources could be useful to download mri datasets, then you could use spm and conn? https://www.fil.ion.ucl.ac.uk/spm/ https://web.conn-toolbox.org/ Hope it's useful!

r/neuroscience • comment
1 points • jndew

I really enjoyed the U.W Coursera class https://www.coursera.org/learn/computational-neuroscience which uses Dayan & Abott as the textbook. I think Dayan&Abott is, or at least has been, the standard. I wouldn't have been able to get through the book without the lectures. Matlab was used when I took it a few years ago, but I guess they've redesigned the class for Python? Work through this class and they'll have you doing some meaningful computer simulation and data-analysis projects.

I'm working on https://mitpress.mit.edu/books/introductory-course-computational-neuroscience which I'm really enjoying because it is modeling/programming based, and comes with source-code. It claims to be low-math intro, maybe by MIT standards... This is a 2018 book, so I think it is up-to-date. I wish there were a MOOK class using this book. Does anyone know one?

A little calculus, a bit more diff.eq, linear algebra (Eigenvectors/values comes up a lot!), a bit off Shannon information theory, definitely some statistics and maybe probability too, basic physics of electricity, at least a tiny bit of chemistry, and you're all set. Not an easy subject but very challenging and rewarding. Good luck!

r/computationalscience • comment
1 points • asoplata

Shameless plug: I've tried to compile a list of open computational neuroscience resources for people who want to dive into it. If you're just getting started or are curious, there's actually quite a few open/free courses (also listed in that link) that are supposed to be excellent:

r/neuroscience • comment
1 points • Chand_laBing

Editing my earlier list of links into titles. I'll try to remove the duplicates and more coherently categorise it:

Self Learning

  • Asking Professors for reading lists

  • Khan academy

  • PubMed

  • Wikipedia

Developing an interest in neuroscience

  • Behave by Robert Sapolsky

  • The Man Who Mistook His Wife for a Hat by Oliver Sacks

  • Tale of the Dueling Neurosurgeons by Sam Kean

  • Mapping the Mind by Rita Carter

  • The Human Brain Coloring Book (Coloring Concepts)

  • Decartes’ Error by António Damásio

  • Reductionism in Art and Brain Science, from Eric Kandel

  • Foundations of Behavioral Neuroscience

  • Harvard and MIT have open courses

  • Patient H.M. By Luke Dittrich

  • An Anthropologist on Mars by Oliver Sacks

  • Reaching Down the Rabbit Hole

  • The Brain by David Eagleman

  • The Tell Tale Brain

  • Neuromania: On the Limits of Brain Science by Carlo Umiltà and Paolo Legrenzi

  • How to Create a Mind by kurzwel

  • Kluge

  • When Breath Becomes Air by Paul Kalanithi

  • Student's guide to cognitive neuroscience

  • Aging with Grace

  • Flipnosis

  • The Dummies Guide to Neuroscience

  • https://www.albertafamilywellness.org/ (online course)

  • Neurofitness - Rahul Jandial, MD, PhD

  • Human by Michael Gazzaniga

  • Steven Pinker's How The Mind Works

  • Phantoms in the Brain by V.S. Ramachandran

  • Incognito: the secret lives of the brain by David Eagleman.

  • Brain on Fire.

  • My Stroke of Insight

  • Joe Rogan has podcasts with Robert Salopsky, Matthew Walker, William Bon Hippel, etc.

Must read books/textbooks

  • Kandel

  • The Selfish Gene

  • Michael Gazzaniga

  • Anything by Oliver Sacks or V. S. Ramachandran

  • Behave by: Robert Sapolsky

  • Neurologic by: Sternberg

  • Buzsaki The Brain From Inside Out

  • Explorations of cognitive neuropsychology by Alan J Parkin

  • The Man Who Mistook His Wife for a Hat by Oliver Sacks

  • The Brain That Changes Itself, Norman Doidge

  • Human navigation: Human Spatial Navigation by Ekstrom et al.

  • The Eye and the Brain by Richard Gregory

  • Radical Embodied Cognitive Science by Anthony Chemero

  • The Master and his Emissary: The Divided Brain

  • the Making of the Western World by Iain McGilchrist

  • Hille

  • Tale of the Dueling Neurosurgeons, by Sam Kean

  • Incognito by David Eagleman.

  • This is Your Brain on Parasites

  • Principles of Neural Science

  • MIT OCW Brains Minds and Machines course

  • Gazzaniga's Cognitive Neuroscience: Biology of the Mind

  • An Introductory Course in Computational Neuroscience(Paul Miller)

  • Tutorial on Neural Systems Modeling (Thomas J. Anastasio)

  • Introduction To The Theory Of Neural Computation(John A. Hertz , Anders S. Krogh , Richard G. Palmer)

  • https://www.youtube.com/playlist?list=PLuOBGfGzMdYj9SjIh81fm4IQMw4_ZdLlC (lecture series)

  • The Neuroethology of Predation and Escape

  • Learning & Memory by Gluck, Mercado, & Myers

  • The Source by Dr Tara Swart

  • Superhuman mind

  • Thinking, fast and slow

  • The Rhythms of the Brain

  • Behave’ by Robert Sapolsky

  • Beyond the zonules of Zinn - David Bainbridge

  • The Neurobiology of the Gods - Erik D. Goodwyn

  • The Brain that Changes Itself by Norman Doidge

  • Neuroscience: Exploring the Brain

  • We Are Our Brains by Dick Swaab

  • Who's in Charge?', by Michael Gazzaniga

  • Gazzaniga, Antonio Damasio, Vilayanur Ramachandran, Daniel Levitin, Marc Wittman

  • After Phrenology by Michael Anderson

  • The Mind and the Brain by Jeffery Swartz

  • The Accidental Mind by David Linden

  • The Robot's Rebellion by Keith E. Stanovich

  • Andy Clark's "Surfing Uncertainty

  • Jakob Hohwy’s The predictive mind?

  • Musicophelia by Oliver Sacks

  • Nigg -- What is ADHD

  • Neuroanatomy ; Draw It to Know It

  • Netter -- {Anatomy}

  • Freeman -- How the brain makes up its mind

  • Grandin -- Thinking in Pictures

  • Feynman -- Surely You Must be joking Mr. Feynman!

  • Herculano-Houzel -- The Human Advantage

  • The Language Instinct

  • Lehrer -- Proust Was a Neuroscientist

  • Hawkins -- On Intelligence

  • Buzsaki works -- Recommend (some of the review articles are book length too.)

  • Restak -- Mozarks Brain and the Fighter Pilot

  • Buonomano -- Brain Bugs

  • Thinking Fast and Slow by Daniel Kahnemann

  • The Biology of Desire: Why Addiction is Not a Disease, by Marc Lewis

  • Future of the Mind by Michio Kaku

Spatial Memory

  • Dudchenko: Why people get lost

Computational Neuro

  • "book on couraera.org" from University of Washington

  • "Theoretical Neuroscience" by Dayan and Abott

  • Trappenberg

  • Gerstner's Neuronal Dynamics

  • Behave by Robert Sapolsky

  • New Mind Readers by Russ Poldrack

  • The Blackwell Companion to Consciousness (p. 12). Wiley. Kindle Edition.

  • Pfaff, Donald. How Brain Arousal Mechanisms Work

  • Montgomery, John. Evolution of the Cerebellar Sense of Self (p. 2).

  • Hohwy, Jakob. The Predictive Mind (p. 1).

  • Mapping cloud 9 by Steven Kotler

  • Student's guide to cognitive neuroscience

Best writing

  • Eric J Nestler

  • Rachel Wilson

  • Koob

  • Sacred Knowledge" by Dr. Bill Richards

  • David A. Ross

  • Len Koziol.

Getting perspective for PhD book suggestions

  • Weekly One-page-perspectives in Science

  • BBC "In Our Time" for perspective

Videos

Eagleman's series (on BBC I think) on the brain called "The brain"

Learning about neuroscience

  • Behave by Robert Sapolsky.

  • Neurophilosophy by Patricia Churchill

  • Descarte's Error

Succinct books

  • Principles of Neurobiology by Liqun Luo

ERPs and MEPs

  • Steven Lucks book " An Introduction to the Event-Related Potential Technique

Function/mechanism of neurons

  • Neuronal Dynamics by Gerstner et al

  • MIT comp neuro series https://mitpress.mit.edu/books/series/computational-neuroscience-series

  • Bullmore or Sporns.

  • Theoretical Neuroscience by Dayan and Abbott

  • Recent research by Scarpetta and de Candia

  • work of Xiao-Jing Wang

  • Deep Learning by Goodfellow

For physicists

  • Dynamical Systems in Neuroscience" by Eugene İzhikevich

  • Non-linear dynamics and chaos" by Steven Strogatz

  • Biophysics of computation" by Christof Koch

  • Modeling Brain Function" by Daniel J. Amit

  • Bear "Neuroscience, Exploring the Brain"

  • Peter Dayan book Theoretical neuroscience

  • https://www.coursera.org/learn/computational-neuroscience (online course)

  • Marvin Minsky’s papers

  • Wulfram Gerstner: https://neuronaldynamics.epfl.ch/

  • Peter Dayan: theoretical neuroscience

Psychedelics

  • Michael Pollan's books and podcast appearances

  • Uppers, Downers, All Arounders: Physical and Mental Effects of Psychoactive Drugs, 7th Edition 7th Edition

  • https://maps.org/training

  • Carhart-Harris, R. L., and D. J. Nutt. "Serotonin and brain function: a tale of two receptors." Journal of Psychopharmacology 31, no. 9 (2017): 1091-1120.

  • Medical Toxicology of Drug Abuse: Synthesized Chemicals and Psychoactive Plants Donald G. Barceloux

  • Primate Neuroethology, Platt and Ghazanfar

/u/morganfreemonk's List of neuro books

https://docs.google.com/document/d/1C7eIXMyU64kI3b95VsTtMQcBbsz3B9O1emZZ1yNJDT0/edit

r/TooAfraidToAsk • comment
1 points • ICrackedANut

"So are you saying thats its a mix of nature/nuture or is it 100% environment? "So are you saying thats its a mix of nature/nuture or is it 100% environment? Because if black peeps were raised well and stuff so they had the same iq as everyone else. Because they have higher bone density wouldn't that make them the superior race?"

​

Your thinking maybe right but due to lack of evidence we can provide, we can't say for sure that "You are black is why you are dumb". Currently, it is believed by some neuroscientist that environment is the cause. BUT you can change it by becoming a neuroscientist.

​

"Also i'd to love learn about some neuroscience :)"

​

First and foremost, most people who fail to study ANY subject is because of lack of self-discipline and not knowing how to learn properly.

​

Note: The courses below are all free. Click "Audit the course" after clicking enroll in Coursera.

​

Step #1

Learning How to Learn is a course by University of California San Diego. It focuses on how to better learn and avoid procrastination.

​

Step #2

Now you need to learn the relevant math and stuff.

  1. Statistic and probability (You will learn that there is no such thing as 100% confidence in statistics.)
  2. Linear Algebra
  3. Graph Theory
  4. Digital Signal Processing
  5. Take sometime to memorize brain parts.
  6. Depends on which side you want to study (Don't worry about this now as your course will tell you.)

Step #3

Now you are ready to jump into neuroscience!

  1. Medical Neuroscience
  2. Synapses, Neurons and Brains
  3. Fundamental Neuroscience for Neuroimaging
  4. Computational Neuroscience
  5. You can also learn Neural Network (A.I.) now if you have learnt the math above.

Step #5

Now it is time for you to become a scientist!

  1. Scientific Methods and Research
  2. Now you can ask yourself what question public have that you as a scientist want to answer. You can do your research from here. Write a scientific journals and then perhaps write a book on it.

​

Remember, it takes discipline to learn something. You are not only are you gonna be learning neuroscience with above steps, you will be learning self-discipline too. Most people who fail is because they lack self-discipline. When you gain self-discipline, you basically win in life.

​

PS: Don't try to rush. Instead, sit down and study relaxly. You want to understand every topic well so get yourself a notebook and a pen. Enjoy your journey.

PPS: Get yourself a weekly blog. That way you will have motivation to study the path above. It will also be useful for future employment and university admission and of course, big scholarships.