An attempt of an undergrad to create a curated repository of computational neuroscience and brain-inspired artificial intelligence resources, which can also including Data Science, pure Math, Machine Learning, Deep Learning, and not constrictively limiting beyond those topics. Leave a star if you believe I can make it, or follow me if you don’t.
SaberToaster, 2025
Learning Resources
MOOCs & Online Courses
| Course | Provider/Author | Focus | Best For | Access |
| Computational Neuroscience | Coursera | Mathematical foundations of neural computation | CS students transitioning to neuro | Course |
| Neuroscience for Machine Learners | Neuromatch Academy | CS-friendly intro to neuroscience | ML practitioners | Course • Videos |
| Machine Learning Specialization | Coursera | Standard ML foundations | Beginners | Course |
| Deep Learning Specialization | Coursera | Neural networks and architectures | Intermediate practitioners | Course |
| Predictive Brain Lab Resources | MIT | Heavy NeuroAI focus | Advanced researchers | Resources |
| Computational Neuroscience 2020 | Michelle R. Greene, Ph.D | Self-paced neuroscience curriculum | Self-directed learners | Course |
Essential Books & Textbooks
Mathematical Foundations
| Title | Authors | Year | Focus | Best For | Access |
| Neuronal Dynamics | Wulfram Gerstner et al | 2014 | Mathematical models of neural dynamics | Physics background | Free Online |
| Theoretical Neuroscience | Dayan & Abbott | 2001 | Mathematical framework for computation | CS + Physics students | Essential textbook |
| Dynamical Systems in Neuroscience | Izhikevich | 2007 | Mathematical models of neuronal behavior | Advanced mathematical focus | Standard reference |
| Neural Engineering | Eliasmith & Anderson | 2003 | Neural representation principles (NEF) | Engineering approach | NEF methodology foundation |
AI/ML References
| Title | Authors | Year | Focus | Notes |
| Deep Learning | Goodfellow et al. | 2016 | Comprehensive DL textbook | Standard reference |
| Attention Is All You Need | Vaswani et al. | 2017 | Transformer architecture | Revolutionary paper |
Blogs & Personal Resources
| Author | Affiliation | Focus | Why Follow | URL |
| Christopher Olah | Anthropic AI co-founder | Neural network interpretability | Clear explanations of complex concepts | Blog |
| Aman Chadha | AWS GenAI Chief Research Scientist | AI research and applications | Industry perspective on cutting-edge research | Homepage |
| Charles Frye | Helen Wills Neuroscience Institute | Computational neuroscience | Academic insights bridging theory and practice | Homepage • CNS |
YouTube Channels
| Channel | Creator | Focus | Why Watch | URL |
| 3Blue1Brown | Grant Sanderson | Mathematical visualizations | Best math/ML concept explanations | Channel |
| Artem Kirsanov | Artem Kirsanov | Computational neuroscience animations | Covers most CNS concepts with clear animations | Channel |
| Deepia | Various | AI/ML visualizations | Cool visualization techniques | Channel |
| Computerphile | University team | Computer science concepts | CS fundamentals relevant to neurocomputation | Channel |
Research Literature
Mathematical & Computational Foundations
| Paper/Book | Authors | Year | Key Contribution | Impact | URL |
| What the Frog’s Eye Tells the Frog’s Brain | Lettvin et al. | 1959 | Feature detection in visual system | Classic computational neuro | |
| Computational neuroscience: a frontier | Multiple | 2020 | State of the field overview | Recent comprehensive review | PDF |
Note: Mathematical foundations table to be expanded with specific papers on neural dynamics, information theory, and computational methods.
NeuroAI Integration Papers
| Paper | Authors | Year | Key Contribution | Research Area | Notes |
| The Genomic Bottleneck | Zador | 2019 | Constraints on innate vs learned computation | Evolutionary computation | Influential perspective paper |
| Brain-Inspired AI | Hassabis et al. | 2017 | Neuroscience-AI bidirectional influence | AI methodology | DeepMind perspective |
| Surrogate Gradient Learning in SNN | Neftci et al. | 2019 | Training spiking neural networks | SNN algorithms | Key training methodology |
| Deep Learning with Spiking Neurons | Pfeiffer & Pfeil | 2018 | Neuromorphic deep learning approaches | Hardware-software co-design | Practical implementation focus |
| Towards Spike-Based Machine Intelligence | Roy et al. | 2019 | Comprehensive SNN survey | SNN overview | State-of-the-art review |
Specialized Topics
Spiking Neural Networks
Papers on temporal dynamics, energy efficiency, and biological realism
Hopfield Networks
Classic associative memory models and modern variants
Attention Mechanisms
Biological inspiration and computational implementations
These sections to be populated with specific paper recommendations
Research Institutions & Labs
North America
| Institution | Key Researchers | Research Focus | Location | URL |
| MIT McGovern Institute | Multiple PIs | Computational neuroscience | Cambridge, MA | |
| Stanford Wu Tsai | Multiple PIs | Neural computation and AI | Stanford, CA | |
| Allen Institute | Christof Koch | Large-scale brain mapping | Seattle, WA | |
| HHMI Janelia | Multiple PIs | Neural circuits and computation | Ashburn, VA | |
| Cold Spring Harbor Laboratory | Multiple PIs | NeuroAI internships + postdocs | NYC, NY | NeuroAI Program |
Europe
| Institution | Key Researchers | Research Focus | Location | URL |
| EPFL (Computational Neuroscience) | Wulfram Gerstner | Mathematical neuroscience | Switzerland | LCN Lab |
| EPFL (NeuroAI) | Martin Schrimpf | Brain-AI alignment | Switzerland | Schrimpf Lab |
| ETH Zurich INI | Multiple PIs | Neuromorphic engineering | Switzerland | |
| DeepMind Neuroscience | Multiple researchers | Brain-inspired AI | London, UK | |
| IT:U | Jie Mei | Neuromodulatory mechanisms | Austria | Computational Neuroscience |
Asia
| Institution | Key Researchers | Research Focus | Location | URL |
| KWANGWOON University | Young-Seok Choi | Neuroengineering and AI | Korea | NeuroAI Lab |
| Shanghai Jiao Tong University | Ru-Yuan Zhang | Cognitive computational neuroscience | Shanghai, China | Zhang Lab |
Journals & Conferences
High-Impact Journals
| Journal | Type | Focus | Impact Factor | Submission Focus |
| Nature Neuroscience | Journal | High-impact neuroscience research | >20 | Breakthrough discoveries |
| Neuron | Journal | Cellular and systems neuroscience | >15 | Mechanistic insights |
| Neural Computation | Journal | Computational theory | ~3 | Mathematical models |
Open-Access Venues
| Venue | Type | Focus | Impact Factor | Why Submit Here |
| PLoS Computational Biology | Journal | Computational biology | ~4 | Open access, broad reach |
| Frontiers in Computational Neuroscience | Journal | Computational approaches | ~3 | Fast review, open access |
Key Conferences
| Conference | Full Name | Focus | Venue Type | Notes |
| NeurIPS | Neural Information Processing Systems | ML/AI with neuroscience connections | Annual | Premier ML conference |
| COSYNE | Computational and Systems Neuroscience | Computational neuroscience | Annual | Theory-focused |
| ICLR | International Conference on Learning Representations | Representation learning | Annual | Rising importance in AI |
| CNS | Cognitive Neuroscience Society | Cognitive neuroscience | Annual | Experimental focus |
| ICMNAI | International Conference on Mathematics of Neuroscience and AI | Mathematics intersection | Annual | Website |
Neural Simulation Platforms
| Tool | Category | Description | Language | Best For | URL |
| Brian2 | SNN Simulation | Clock-driven neural network simulator | Python | Research, education | |
| NEURON | Detailed Modeling | Biophysically detailed neuron models | Python/C++ | Detailed biophysical models | |
| STEPS | Spatial Modeling | Spatial stochastic simulation | Python/C++ | Subcellular processes | |
Analysis & Visualization Tools
| Tool | Category | Description | Language | Best For | URL |
| DeepLabCut | Behavioral Analysis | Markerless pose estimation | Python | Animal behavior tracking | |
| PyTorch | Deep Learning | Research-focused ML framework | Python | NeuroAI model development | |
| TensorFlow | Deep Learning | Production-focused ML framework | Python | Deployment and large-scale apps | |
This resource hub is community-maintained. Feel free to contribute through PRs or suggestions for additional resources, corrections, or organizational improvements.