Software to self-learn Neuroscience  - for undergraduate students

Software to self-learn Neuroscience

- 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) 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 contribute to such teams.

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PART I: Computational Models - Single Cells and Networks

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. Small Network Model 1 - How does a Central Pattern Generator Work?

6. Small Network Models 2,3 - How do Short-Term Memory and Winner-Take-All Networks Work?

PART II:  Illustrate Network Model - Fear/Reward learning in Rodents

1. Putting it all together with a biologically realistic network model -  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

PART III: Interesting Extensions

1.  Learning coding + translation to brain-machine interfaces

2.  Functioning of the amygdala in fear - Network Model of Amygdala   Extension to Anxiety Disorders     Analysis - Amygdala model

3. ......

4. ......

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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