Software to self-learn Neuroscience  - for med students, residents, faculty

Self-learning Software to Link Neuroscience to Medicine

- Computational models of neurons and neural circuits

Slides & videos (with links to open-source Virtual/Colab Labs)

Learning Outcomes: (i) Enhanced understanding of neuroscience fundamentals, including from a quantitative perspective; (ii) Enhanced knowledge in the usage of computational and active learning software modules; (iii) Ability to independently develop and analyze the output of computational models at the single cell and network levels; (iv) Confidence to interact effectively with quantitative scientists (modelers, software professionals) on collaborative projects, and to lead such teams.

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PART I:  CASE STUDY of Fear learning in Rodents  (4-HOUR MODULE)

1. Exploring Our Brain's Learning Process  LINK

(i)  Wired to Think: Exploring the Brain as an Electrical Circuit

(ii)  Learning Fear: Exploring Pavlovian Conditioning and Neural Pathways

(iii) Connecting the Circuits of Fear: Understanding Neural Pathways and Conditioning

2. Intro to Tone-Shock & Video

3. Network Model of Amygdala

4. Extension to Anxiety Disorders 

5. Optional - More on Network model of amygdala 

PART II: Computational Models - Single Cells and Networks  (6-HOUR MODULE)

1. How do we Model a Neuron?

2. What is an Action Potential?

3. What are Bursting and Adaptation?

4. How do Synapses Function?

5. Network Model - How does a Central Pattern Generator Work?

6. Network Model - How do Short-Term Memory and Winner-Take-All Networks Work?

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Note: The course materials represent an abbreviated version of an NIH-funded workshop conducted by Dr. Nair for 3 years from 2016-19

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