# Introduction to computational neuroscience

25 parts

How to gain the recommended background knowledge for success in computational neuroscience

Most who enter the field of computational neuroscience have a prior background in either mathematics, physics, computer science, or (neuro)biology. Since computational neuroscience requires a bit of knowledge from all these fields, with some basic knowledge of neurons and a familiarity with certain types of equations and mathematical concepts, we recommend two different "starting tracks" depending on the student's background before you begin the lectures listed below:

Intro to computational neuroscience for a computer sci/math background
The student should learn basic concepts and equations for how neurons generate signals, either a more through introduction via the Cellular Mechanisms of Brain Function course or a quick reminder via the Basic mathematics for computational neuroscience tutorials.

Intro to computational neuroscience for a biology background
Here the student is assumed to already have basic knowledge of neurons. We recommend some orientation in mathematics (differential equations, linear algebra, dynamical systems) and computer science. There are a number of possible online courses openly available, for instance the MIT OpenCourseware course on Differential Equations. After that, we recommend a quick orientation on how these mathematics apply to neuroscience by viewing the Basic mathematics for computational neuroscience tutorials.

## Introduction to modeling the brain

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model. Speaker: Gaute Einevoll

## Some simple models of neurons

Introduction to simple abstract models of neurons. Speaker: Geoffrey Hinton.

## Integrate and fire neuron modeling

Introduction to simple spiking neuron models. Author: Zubin Bhuyan, Tezpur University

## The action potential

The ionic basis of the action potential, including the Hodgkin Huxley model. Speaker: Carl Petersen.

## Modelling across scales of analysis

Forms of plasticity on many levels - short-term, long-term, metaplasticity, structural plasticity. With examples related to modelling of biochemical networks. Speaker: Upi Bhalla.

## Principles of intracellular modelling and computation

Introduction to modelling of chemical computation in the brain. Speaker: Upi Bhalla

## Simulating the long time scales and large molecule numbers involved in synaptic plasticity

Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level. Speaker: Kim "Avrama" Blackwell.

## Single neuron models - I

Introduction to stability analysis of neural models. Speaker: Bard Ermentrout

## Single Neuron Models - II

Introduction to stability analysis of neural models. Speaker: Bard Ermentrout

## Neural oscillations, weak coupling and networks

Oscillations and bursting. Speaker: Bard Ermentrout.

## Neural oscillations and networks II

Continued exploration of oscillations and bursting. Speaker: Bard Ermentrout.

## Oscillatory networks and the Ott Antonsen theory

Weakly coupled oscillators. Speaker: Bard Ermentrout

## Oscillatory networks II

Continuation of coupled oscillators. Speaker: Bard Ermentrout.

## Firing rate models - networks

Firing rate models. Speaker: Bard Ermentrout

## Spatiotemporal dynamics, waves, pattern formation

Pattern generation in visual system hallucinations. Speaker: Bard Ermentrout

## Models and theory

Introduction to the role of models in theoretical neuroscience. Speaker: Jakob Macke

## Biophysical models of single neurons

Different types of models, model complexity, and how to choose an appropriate model. Speaker: Astrid Prinz.

## Tightly and loosely coupled networks

Balanced E-I networks, stability and gain modulation. Speaker: Kenneth Miller.

## Dimensionality reduction of large-scale neural recordings

Methods for dimensionality reduction of data, with focus on factor analysis. Speaker: Byron Yu.

## Dynamics of rate-based and spiking balanced random networks

Rate and spiking Bayes populations. What are the mathematical techniques we can use to understand networks of neurons? Speaker: Julijana Gjorgjieva.

## Theory of network dynamics

Spiking neuron networks and linear response models. Speaker: Tatjana Tchumatchenko.

## Neural data analysis: The Bayesics

Bayesian neuron models and parameter estimation. Speaker: Jakob Macke.

## Bayesian models of perception, cognition and learning

Bayesian memory and learning, how to go from observations to latent variables. Speaker: Máté Lengyel.

## Constraints on information processing

Constraints can help us understand how the brain works. Speaker: Simon Laughlin.

## Evolution and Brain Computation

Approaching neural systems from an evolutionary perspective. Speaker: Gilles Laurent