This repository will contain a non-exhaustive collection of tools to mechanistically interpret neural networks that are derived from the Episodic Memory Theory Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation.
The tools are written in Python 3.6 and require the following packages:
- numpy
- scipy
- matplotlib
to install run the following in the root directory of the repository:
pip install .
Interpretation of recurrent neural networks are enabled by converting the non-linear dynamics
to linear dynamics. This is done by linearizing the non-linear dynamics around a fixed point.
Whatever method is used to linearize the dynamics, the linear model needs to be converted to
emt_tools.models.LinearModel class.
This class is a wrapper around the linear model that provides some useful methods for interpreting the linear model without going too deep into the algorithms used for interpretation.
Further, emt_tools.utils contains many functions that directly implements the interpretation
algorithms.
For sample usage of emt_tools, check out demo notebooks in https://github.com/arjunkaruvally/variable_binding_episodic