Computational Neuroscience

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

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

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

I think if you're interested, you could check out the computational neuroscience course on Coursera. It's well paced and might give you a better idea on how and to what level both the fields interact. Here's the link

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

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).

I don't know much about the biology side, but in that regard most everyone starts by working through "Principles of Neural science", Kandel. Another high quality book that angles a bit more towards data analysis & modeling is "Theoretical Neuroscience", Dayan & Abbott. This is the textbook for https://www.coursera.org/learn/computational-neuroscience which I enjoyed and found very useful. Since you are mathy, you might like "Introduction to the theory of neural computation", Hertz, Krogh, Palmer which is older but foundational. MIT, Harvard, CalTech all have free on-line lecture series. I have only glanced at these but they have good reputations. From what I hear (no recent personal experience), labs want statisticians to do data analysis, often using Matlab, Python, or maybe R. Good luck!

ps. Maybe keep your day job. Go over to r/neuroscience and see what they have to say about career & financial aspects of being a professional neuroscientist.

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!

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

MATLAB Onramp is a good place to start. It's a short interactive course. There are other onramps for topics like image and signal processing that may be relevant to you. For a full course, there's Introduction to Programming with MATLAB.

If you're more interested in neuroscience-specific tutorials, I know Quantitative Biology Workshop has some neuroscience activities. And there's Computational Neuroscience if you want a full neuroscience course.

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.

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

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/

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! :)

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.

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:

• Finish Learn Python 3 the Hard Way
• Complete the Introduction to Computer Science & Programming Using Python, by the MIT at edX.

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):

• Whirlwind Tour of Python: mostly an introduction to the Python Data Science Handbook (below).
• Python Data Science Handbook: advanced data science.
• Python in Neuroscience Tutorial: Collection of tutorials about methods of computational neuroscience using Python
• Computational Modelling in Neuroscience
• Computational Neuroscience: by Robert H. Clewley.
• Neuronal Dynamics: this one is probably my favourite so far, because it seems to explain the theory and has exercises with Python.
• Computational Neuroscience by Washington University at edX: This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. I also like this one.

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!

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.

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!

Couple of Coursera courses:

• Medical Neuroscience
• Understanding the Brain: The Neurobiology of Everyday Life (I liked this A LOT)
• Advanced Neurobiology I

Plus courses that are more computational specific:

• Synapses, Neurons and Brains
• Computational Neuroscience

And there is going to be a free Online Summer School hosted by International Youth Neuroscience Association this July. I guess they are still open for applications. https://ibb.co/ykcRjS0

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:

• Computational Neuroscience: Neuronal Dynamics of Cognition taught by EPFL on edX, I've heard it's great, likely targeted at "meta" kinds of models where you're not modeling neurons individually, but rather processes of brain function.
• Simulation Neuroscience Another EPFL course on edX, targeted at actual simulation of actual neurons themselves.
• Computational Neuroscience at Coursera No idea to its quality.
• If you're new to neuroscience as well, MIT has a ton of open courses on its Brain & Cognitive Sciences department Open CourseWare

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

"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?"

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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.

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

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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.

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

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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.

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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.

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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.

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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.