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.

**HRank** is a filter pruning method that explores the High Rank of the feature map in each layer (HRank). The proposed HRank  is inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive. Based on HRank, the authors develop a method that is mathematically formulated to prune filters with low-rank feature maps.

HRank

HRank: Filter Pruning using High-Rank Feature Map

SynaNN

A Synaptic Neural Network and Synapse Learning

**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)

Source	A Synaptic Neural Network and Synapse Learning
Year	2000
Data Source	Papers with Code archive (CC BY-SA)