This is an evolving list. If you have suggestions for resources to include, please email email@example.com.
Online Seminar Series:
World Wide Neuro: a new initiative to bring seminars and talks online.
The Learning Salon: a weekly forum in which we explore bridges and contentions in biological and artificial learning.
Neurodata Without Borders: tutorials to get you started. Available for both Matlab and Python
DataJoint: a collection of tutorials to explore DataJoint.
DeepLabCut: self-paced modules.
SLEAP: an open-source deep learning package for multi-animal pose estimation and tracking.
Neuromatch Academy: An online school for computational neuroscience started by the team who created CoSMo summer school, CCN SS, Simons IBRO and the neuromatch conference. This worldwide academy will train neuroscientists in computational tools, make connections to real world neuroscience problems, and promote networking with researchers.
Materials from Past Courses
Methods in Computational Neuroscience: a course hosted by the Marine Biological Laboratory that introduces computational and mathematical techniques in neuroscience. Lectures for the last three years are available under the lectures tab of the course page. Course materials are available for 2018 and 2019.
University of Washington Course on Computational Neuroscience from Coursera: an introduction to basic computational methods for understanding what nervous systems do and for determining how they function.
Neuronal Dynamics Course and Textbook: includes links to the book, lectures, Python exercises and teaching materials.
NYU’s Mathematical Tools for Neural and Cognitive Science: A graduate lecture course covering fundamental mathematical methods for visualization, analysis, and modeling of neural and cognitive data and systems. Includes links to video lectures and exercises.
Cajal Course in Computational Neuroscience (via INCF) : teaches the central ideas, methods, and practice of modern computational neuroscience through a combination of lectures and hands-on project work.
Computational Neuroscience: the Basics (via INCF): Introduction to modeling the brain.
Computational Neuroscience: Neuronal Dynamics of Cognition (EPFL via edX): This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
Data Science and Data Skills for Neuroscientists (SFN): leading experts teach basic data skills that all neuroscientists should know and detail advanced data science methods that can be used in different circumstances.
Neurohackademy: a course on neuroimaging and data science, hosted by the University of Washington. Online materials include videos from last year’s talks and tutorials. (Links in the schedule lead to the videos.) Materials are also accessible here. The lecture on cloud computing may be of interest to those locked out of labs and unable to access their large desktop computers and servers.
UCSD Course on Neural Signal Processing: includes lab exercises in Jupyter notebooks
Coding and Vision 101 Lecture Series (Allen Institute): A 12-part series, produced by the Allen Institute for Brain Science as an educational resource for the community.
Talks from Past Conferences/Workshops:
Talks from Cosyne: Official channel for the Computational and Systems Neuroscience Conference
Neuromatch Talks: Youtube videos from past neuromatch conferences.
Dimensionality Reduction and Population Dynamics in Neural Data: The aim of this conference was to gather a number of key players in the effort for developing methods for dimensionality reduction in neural data and studying the population dynamics of networks of neurons from this angle.
Representation, Coding and Computation in Neural Circuits: The aim of this workshop was to shed light, at the level of cortical circuits, on issues of representation and coding such as sparsity and high-dimensionality, spikes and coding capacity in the presence of noise.
Computational Theories of the Brain: This workshop was about general computational principles for networks of neurons that help us understand experimental data, about principles that enable us to reproduce aspects of the brain’s astounding computational capability in models and neuromorphic hardware, and about the connections between computational neuroscience and machine learning.
The Brain and Computation: This workshop focused on the problem of inferring structure from neuroscience data.
Training Space – a free and open online site for training folks in neuroinformatics and computational neuroscience
List of computational neuroscience resources by Dan Goodman
For a list of upcoming conferences and other events, see the SCGB and related events page.