for pre-print, see: https://www.biorxiv.org/content/10.1101/2023.11.27.568876v1
use DynANN.ipynb to run code and reproduce figures in manuscript
contains all scripts to implement and train a neural network on the AET problem
aet_net: code to implement network structure for 1-layer network (a 2-layer network did a better job at segregating competing inputs, so this is not explored in manuscript)
aet_net_2lay: code to implement network structure for 2-layer network
aet_stim: generate visual inputs and minibatches
aet_dyn: Euler integration as functions (however, these are explicitly coded in current notebook)
aet_optuna_bear: exploratory script, finding tunable parameters with optuna (optimized for HPC)
aet_figures: script to create all figures in publication