Year: 2020
Type: preprint
Abstract: This is a living document that will be updated when appropriate. MIIND [1, 2] is a population-level neural simulator. It is based on population density techniques, just like DIPDE [3]. Contrary to DIPDE, MIIND is agnostic to the underlying neuron model used in its populations so any 1, 2 or 3 dimensional model can be set up with minimal effort. The resulting populations can then be grouped into large networks, e.g. the Potjans-Diesmann model [4]. The MIIND website http://miind.sf.net contains training materials, and helps to set up MIIND, either by using virtual machines, a DOCKER image, or directly from source code.
Source: arXiv (Cornell University)
Authors: Osborne, Hugh, Lai, Yi Ming, de Kamps, Marc
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3010049442