**Minimum Description Length** provides a criterion for the selection of models, regardless of their complexity, without the restrictive assumption that the data form a sample from a 'true' distribution.

Extracted from [scholarpedia](http://scholarpedia.org/article/Minimum_description_length)

**Source**:

Paper: [J. Rissanen (1978) Modeling by the shortest data description. Automatica 14, 465-471](https://doi.org/10.1016/0005-1098(78)90005-5)

Book: [P. D. Grünwald (2007) The Minimum Description Length Principle, MIT Press, June 2007, 570 pages](https://ieeexplore.ieee.org/servlet/opac?bknumber=6267274)

A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear and log-concave synapse function of excitatory and inhibitory probabilities of channels.

SynaNN

A Synaptic Neural Network and Synapse Learning

**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.

Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com