Resources | || || |||| |

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

Computational Neuroscience and Neurophysics

Machine Learning

Reinforcement Learning // Optimal Control

Complex systems

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]