A guided roadmap for learning computational neuroscience, including recommended topics, resources and labs.

Getting Started

LLM experiences

In the era of LLMs, using them correctly and prompting them effectively can significantly boost efficiency. My strongest impression, however, is that they tend to hallucinate, make things up, and often agree with you uncritically. It’s essential to read through and understand everything they generate. You’re welcome to share your own experiences with me via email.

• Searching: I find the DeepThought mode in the GPT family especially helpful for literature search, as it provides references to actual papers and helps reduce hallucinations.
• Coding: In my view, Cursor is very useful since it can search across the entire project repo, test and launch functions, write documentation, and comment code. However, hallucinations still occur, and the tool doesn’t always accomplish what it claims. Always make sure you understand the code it produces, or ask the agent to provide a step-by-step walkthrough of the project code.
• Writing: As a non-native English speaker, I often rely on GPTs to polish my writing (as I am right now). That said, their wording tends to be general rather than precise. For academic writing, precision and conciseness are critical, so I always double-check and refine the output.

Prerequisites

• Mathematics: Linear algebra, calculus, probability theory, and statistics
• Programming: Python or MATLAB proficiency
• Neuroscience: Basic understanding of neurons, synapses, neural circuits and neural anatomy.

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Core Topics

1. Mathematical Foundations

• Dynamical Systems: Phase planes, stability analysis, bifurcations
• Probability & Statistics: Bayesian inference, information theory
• Linear Algebra: Matrix operations, eigenvalues, principal components

2. Neural Modeling

• Single Neuron Models: Hodgkin-Huxley, integrate-and-fire, LIF
• Network Models: Rate models, spiking networks, connectivity patterns
• Plasticity: Hebbian learning, STDP, homeostatic mechanisms

3. Data Analysis Methods

• Time Series Analysis: Spectral analysis, cross-correlation, coherence
• Dimensionality Reduction: PCA, ICA, factor analysis, manifold learning
• Machine Learning: Classification, regression, clustering for neural data

4. Advanced Topics

• Neural Coding: Rate coding, temporal coding, population coding
• Learning & Memory: Reinforcement learning, memory consolidation
• Brain Networks: Graph theory, small-world networks, modularity

Recommended Resources

Essential Textbooks

• “Theoretical Neuroscience” by Dayan & Abbott - The gold standard
• “Neuronal Dynamics” by Gerstner, Kistler, Naud & Paninski
• “Principles of Neural Design” by Sterling & Laughlin

Online Courses

• Coursera: Computational Neuroscience (University of Washington)
• edX: Introduction to Computational Thinking and Data Science (MIT)
• YouTube: 3Blue1Brown series on neural networks

Programming Tools

• Python: NumPy, SciPy, matplotlib, scikit-learn, Brian2
• MATLAB: Signal Processing Toolbox, Statistics Toolbox
• Specialized: NEURON, NEST, PyTorch for deep learning

Research Labs & Communities

Leading Labs

• Theoretical: Larry Abbott (Columbia), Haim Sompolinsky (Hebrew University)
• Data-Driven: Liam Paninski (Columbia), Jakob Macke (Tübingen)
• Circuits: David Tank (Princeton), Karel Svoboda (Janelia)

Conferences

• Cosyne: Computational and Systems Neuroscience
• NeurIPS: Neural Information Processing Systems
• CNS: Organization for Computational Neuroscience

Online Communities

• Reddit: r/ComputationalNeuro, r/MachineLearning
• Twitter: Follow researchers and labs for latest developments
• Stack Overflow: Programming questions and solutions

Learning Path Suggestions

Beginner (6-12 months)

1. Review mathematical prerequisites
2. Complete Dayan & Abbott chapters 1-5
3. Implement basic neuron models in Python/MATLAB
4. Attend local neuroscience seminars

Intermediate (1-2 years)

1. Study network dynamics and connectivity
2. Learn advanced data analysis techniques
3. Contribute to open-source neuroscience tools
4. Present work at conferences

Advanced (2+ years)

1. Develop novel theoretical frameworks
2. Collaborate with experimental labs
3. Publish research in peer-reviewed journals
4. Mentor junior researchers

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This roadmap is a living document. Suggestions and contributions are welcome!

Contact: Feel free to reach out if you have questions about computational neuroscience or need guidance on specific topics.

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