Neuroscience Neuroscience is a broad scientific field with a main interest in the properties of neurons, how they respond to input, and interact with each other to form networks in the brain. The scientific field can be broadly divided into a theoretical & computational, an experimental, and data science part.

Theoretical and computational neuroscience are used interchangeably. Here, I focus on the theoretical part that aims to develop a generalized idea about the functioning of neurons and the brain using mathematical and computational methods.

• Courses

  • CompNeuro: excitability.
  • Nonlinear Dynamics & Chaos.
  • Intro Dynamical Systems & Chaos.
  • Dynamical Modeling Methods for Systems Biology.
  • Dynamics of Complex Systems.

• Read

  • Theoretical Neuroscience.
  • Neuronal Dynamics.
  • Mathematical Foundations of Neuroscience.
  • Dynamical Systems in Neuroscience.
  • Biological Neural Networks.
  • Analysis of Neural Excitability and Oscillations.

• Code

  • Neuronal Dynamics: Python Exercises.
  • Matlab code for Modeling Projects.
  • Tutorials for Mathematics of the Spike.
  • XPP-Aut.

• Conferences

  • CxConferences.
  • Bernstein Conference.

In this Computation part, I focus on the models developed that can be used in large neural networks, which can predict or explain the functioning of a brain region.

• Courses

  • Computational Neuroscience.
  • Systems Biology.

• Read

  • Modeling Spiking Neurons with Python.

• Code

  • Simulating Neural Spike Trains.
  • XPP files for LIF models.
  • NEURON.
  • BRIAN simulator.
  • A Neuron Playground.

• Conferences

  • NeuroSummerSchools.
  • Computational Neuroscience on the Web.
  • Organization for Computational Neurosciences.
  • Neuromatch.
  • CoSyne.

Experimental neuroscience can be broadly divided into theory based and data-driven research. Most of the time, experimental research is a blend of the two.

• Courses

  • Harvard: Fundamentals of Neuroscience.
  • Coursera: Medical Neuroscience.
  • EDX: Fundamentals of Neuroscience.
  • MIT: Brains, Minds and Machines.
  • MIT: Sensory Systems.
  • Coding and Vision.
  • iBiology.

• Read

  • Neuroscience Online.
  • List of Neuroscience Books.

• Practice

  • Backyard Brains.

• Conferences

  • Society for Neuroscience.

Another part of neuroscience is to make sense of experimental data and how this relates to standing paradigms. Here, I focus on the part of neuroscience that handles large data sets and mathematical tools used to analyze them. You can find general information on the Maths & Models page.

• Courses

  • Network Analysis in Systems Biology.
  • Integrated Analysis in Systems Biology.
  • Mathematical Biostatistics I.
  • Mathematical Biostatistics II.

• Read

  • Statistical Analysis of neural data.
  • A neural data science: how and why.
  • Recurrence in Biological and Artificial Neural Networks.

• Code

  • Allen Brain Atlas.
  • Matlab for Neuroscientists.

• Conferences

  • CoSyne.