Under Construction!
Data Analysis
⁃ Pyka-Parametric-Anatomical-Modeling-2014
Parametric Anatomical Modeling is a method to translate large-scale anatomical data into spiking neural networks. PAM is implemented as a Blender addon.
LICENSE: GNU GPL v2.0
DOI: 10.5281/zenodo.3298590
PUBLICATIONS: Pyka, M., Klatt, S., Cheng, S. (2014): Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy, 8, 91. http://doi.org/10.3389/fnana.2014.00091
⁃ Pyka-Pam-Utils-2014
This is a module with some helpful functions to process the data generated by PAM
License: GNU GPL v2.0
DOI: 10.5281/zenodo.3298825
PUBLICATIONS: Pyka, M., Klatt, S., Cheng, S. (2014): Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy, 8, 91. http://doi.org/10.3389/fnana.2014.00091
Neural Networks (empty)
Cognitive Models
⁃ Dynamics of Disease States in Depression
Major depressive disorder (MDD) is a disabling condition that adversely affects a person general health, work or school life, sleeping and eating habits, and person's family. Despite intense research efforts, the response rate of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. To advance our understanding of MDD, we use computational modelling as described in our article.
The model to simulate the dynamics of disease states in depression can be found below.
License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299247
PUBLICATIONS: Demic, S. & Cheng, S. (2014): Modeling the Disease States in Depression. PLoS ONE 9(10): e110358. https://doi.org/10.1371/journal.pone.0110358
⁃ Episodic Memory Deficits in Depression
License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299871
PUBLICATIONS: Fang, J., Demic, S., & Cheng, S. (2018) The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory, PLOS ONE, 13(6), e0198406
Reinforcement Learning (empty)
High-quality figures
Our group aims to provide neuroscientific community with a collection of high-quality SVG-figures for free use in publications, presentations, websites etc. via GitHub.
All SVG-files in the repository are distributed under the terms of the Create Commons Attribution 4.0 International License.
Current available images include: