Computational Neuroscience              - A System-theoretic Hands-on Introduction

Code-enabled and AI augmented Self Learning

Slides & videos (with links to open-source Colab Notebooks)

LEARNING OUTCOMES - This set of modules is designed to bridge the gap between disciplines, enabling life scientists to master computational modeling while helping quantitative scientists translate engineering skills into biological contexts.  

FOR LIFE SCIENTISTS: (i) Technical Literacy: Develop proficiency in computational and active learning software modules. (ii) Model Implementation: Gain the ability to independently develop and analyze the output of computational models at single-cell and network levels. (iii) Quantitative Fundamentals: Build an enhanced understanding of neuroscience through a mathematical and simulation-based perspective.

FOR QUANTITATIVE SCIENTISTS: (i) Contextual Translation: Apply existing engineering, modeling, and software skills to specific biological and neural systems. (ii) Neural Engineering Foundations: Gain an introduction to the unique constraints and complexities of biological data and nervous system principles. (iii) Collaborative Literacy: Learn to translate complex modeling outputs into biological insights that are actionable for life science partners.

FOR ALL: (i) Effective Communication: Foster interdisciplinary dialogue to interact effectively on collaborative projects. (ii) Team Integration: Develop the confidence to contribute to and lead teams in the growing, high-impact field of neuroscience.

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Part I: Computational Models of Single Neurons

0: Glossary

A: Neurobiology Basics - A Refresher               Colab_A 

B: Biology to Model - Mapping mechanisms to Math and Code           Colab_B 

C: Passive Membrane and Dendritic Cable            Colab_C 

D: Spiking, Adaptation, and Bursting           Colab_D

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Part II: Computational Models of Neuronal Networks

E: Synapses and Transmission           Colab_E

F: Small Networks - Short Term Memory (STM) and Winner-Take-All (WTA) Networks           Colab_F 

PART III:  Model to Behavior and to Translation/BMI

G: Model to Behavior — From circuit computations to behavioral readouts           Colab_G 

H: Model to Translation -  internal states to robot behavior via BMI            Colab_H 

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Other relevant resources/publications: (continuing to be updated)

• Publications: (1) Integrating model-based approaches into neuroscience curriculum – Interdisciplinary neuroscience course in engineering, IEEE Transactions on Education 62(1) 48-56 LINK; (2) Open-source tutorials to teach neuroscience; (3) Communication in Brain Circuits and Systems: a Primer;

• Modules for 2- and 4-year College and High School Faculty (topics: neuroscience, neural engineering, Brain/body as computer/robot - sensors, robotics, ...)

• Other Neural Engineering Laboratory Publications

Google Colab notebooks – Python Basics

1. Learning python using micro:bit ($20) - Video   Slides 

2. Introduction to Jupyter Notebook, Markdown and Python

3. Tools for Exploratory Data Analysis: numpy, pandas, matplotlib