Machine learning without a processor: Emergent learning in a nonlinear analog network

The capabilities of digital artificial neural networks grow rapidly with their size, however the time and energy required to train them does as well. The tradeoff is far better for Brains, where the constituent parts (neurons) update their analog connections in ignorance of the actions of other neurons, eschewing centralized processing. Recently introduced analog electronic contrastive local learning networks (CLLNs) share this important decentralized property. However their capabilities were limited because existing implementations are linear. In this dataset we include experimental demonstrations of a nonlinear CLLN, establishing a new paradigm for scalable learning. Included here are data and scripts required to generate figures 2-6 of the manuscript titled \"Machine learning without a processor: Emergent learning in a nonlinear analog network\"., Methods are described in detail the associated manuscript., , # Data for \"Machine learning without a processor: Emergent learning in a nonlinear analog network\"

[https://doi.org/10.5061/dryad.8w9ghx3vx](https://doi.org/10.5061/dryad.8w9ghx3vx)

Data from experiments using a nonlinear Contrastive Local Learning Network (CLLN).

## Description of the data and file structure

Figures are generated using numbered MATLAB scripts. Data and helper scripts are contained in separate zip folders and should be uncompressed to allow scripts to run properly: unzip and place in the following file structure, and ensure the MATLAB filepath includes both folders.

\>helper_scripts/ {all .m files: Experiment2.m, ExperimentGroup.m, logic_truth_table3.m, makeOrthonormalModes.m, makePlotPrettyNow.m, network_project_superclass2.m} 

\>data/{all .mat files}

Data stored in OscilloscopeData.mat: single array T2 with columns: Time (sec), Input Voltage (V), Output Voltage (V).

Data stored in all other mat files is within an \"experiment\" object (class in Experiment2...