A collection of resources for Computational Neuroscience, Machine Learning and Complex Systems, I find useful or interesting.

### Computational Neuroscience and Neurophysics

- Miller’s An Introductory Course in Computational Neuroscience textbook
- Izhikevich’s Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting textbook
- Gestner’s Neuronal Dynamics: From single neurons to networks and models of cognition textbook, lectures and courses[A/B]
- Dayan and Abbott’s Theoretical neuroscience textbook and exercises
- Stone’s Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency textbook
- Cover and Thomas Elements of Information Theory textbook
- EPFL Blue Brain’s Massive Open Online courses

### Machine Learning

- MacKays Information Theory, Inference and Learning Algorithms textbook and his lectures
- Mohris Foundations of Machine Learning textbook and his courses
- Bishops Pattern Recognition and Machine Learning textbook and Implementations in Python
- Murphy’s Machine Learning: a Probabilistic Perspective textbook
- Grünwalds Minimum Description Length Principle textbook and his course
- Shalev-Shwartz and Ben-David’s Understanding Machine Learning: From Theory to Algorithms textbook and lectures
- Kappen’s Advanced machine learning course (Radboud)
- Buhmann’s Advanced machine learning course (ETH)
- Hennig’s Probabilistic Machine Learning course (Tübingen)

### Reinforcement Learning // Optimal Control

- Sutton and Barto’s Reinforcement Learning: An Introduction textbook
- Levines’s Deep Reinforcement Learning course (UC Berkeley)
- Deepmind’s Introduction to reinforcement learning lectures
- Deepmind’s Advanced Deep Learning & Reinforcement Learning lectures
- Bertsekas Reinforcement Learning and Optimal Control textbook and his lectures

### Complex systems

- Strogatz Nonlinear Dynamics and Chaos textbook and his lectures
- Thurner’s Introduction to the Theory of Complex Systems textbook
- Sayama’s Introduction to the modelling and analysis of Complex Systems textbook

# What I read – Papers

- Tycho Tax, Pedro Mediano, and Murray Shanahan. “The Partial Information Decomposition of Generative NeuralNetwork Models”.
*Entropy*19 (Sept. 2017), p. 474. [25.04.2020] - H. Yu et al. “The Coordinated Mapping of Visual Space and Response Features in Visual Cortex”.
*Neuron*47(2005), pp. 267–280. [09.04.2020] - T. Kohonen. “Self-organized formation of topologically correct feature maps”.
*Biological Cybernetics*43.1 (Jan.1982), pp. 59–69. [09.04.2020] - Yasaman Bahri et al. “Statistical Mechanics of Deep Learning”.
*Annual Review of Condensed Matter Physics11*(Mar. 2020). [01.04.2020] - J. Kwisthout and I. van Rooij. “Predictive coding and the Bayesian brain: Intractability hurdles that are yet to be overcome”.
*Proceedings of the 35th Annual Meeting of the Cognitive Science Society*, CogSci 2013, Berlin, Germany,July 31 - August 3, 2013. [01.04.2020] - B. Olshausen and D. Field. “Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images”.
*Nature*381 (1996), pp. 607–609. [20.03.2020] - Terence D. Sanger. “Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network”.
*Neural Networks*(1989), pp. 459–473. [20.03.2020]