NeuroMatch Academy (NMA) Computational Neuroscience syllabus The content should primarily be accessed from our new ebook: https://compneuro.neuromatch.io/

Objectives: Introduce traditional and emerging computational neuroscience tools, their complementarity, and what they can tell us about the brain. A main focus is on modeling choices, model creation, model evaluation and understanding how they relate to biological questions.

Prerequisites: See here

Course materials Welcome Video Tutorials: videos, notebooks, and slides Projects: videos, notebooks, and slides Group projects are offered for the interactive track only and will be running during all 3 weeks of NMA!

Course outline Week 0 (Optional)

Asynchronous: Python Workshop Part 1 for students + Mandatory TA training for ALL TAS Asynchronous: Python Workshop Part 2 for students + Mandatory TA training for ALL TAS Wed, June 30th: Linear Algebra (Mandatory for all Tutorial TAs). Project TAs have separate training. Thus, July 1st:Calculus (Mandatory for all Tutorial TAs). Project TAs have separate training. Fri, July 2nd: Probability & Statistics (Mandatory for all Tutorial TAs). Project TAs have separate training. Week 1

Mon, July 5: Model Types Tue, July 6: Modeling Practice Wed, July 7: Model Fitting Thu, July 8: Generalized Linear Models Fri, July 9: Dimensionality Reduction Week 2

Mon, July 12: Deep Learning Tue, July 13: Linear Systems Wed, July 14: Biological Neuron Models Thu, July 15: Dynamic Networks Fri, July 16: Project day! Week 3

Mon, July 19: Bayesian Decisions Tue, July 20: Hidden Dynamics Wed, July 21: Optimal Control Thu, July 22: Reinforcement Learning Fri, July 23: Network Causality