Development of the memory dynamics in AuGMEnT network to enable learning on hierarchical tasks, like 12ax. We introduced a differentiable leaky control system that allows the network to forget non-relevant cues. Thus, we implemented a variant of the AuGMEnT network, named hybrid AuGMEnT, which employs an hybrid memory with both leaky and non-lea…
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Visualization Codes

Hybrid AuGMEnT

This is the code for the article: Multi-timescale memory dynamics extend task repertoire in a reinforcement learning network with attention-gated memory
Marco Martinolli, Wulfram Gerstner, Aditya Gilra
Front. Comput. Neurosci. 2018 | doi: 10.3389/fncom.2018.00050
preprint at:
arXiv:1712.10062 [q-bio.NC]

We extend the learning capability of the AuGMEnT network (Rombouts, Bohte, Roelfsema, 2015) by introducing multi-timescale leaky dynamics in the working memory.

Instructions for the simulations

To run the augment simulations on task XXX type:


where is the main file.

The code will automatically construct the dataset importing the building functions from TASKS/ In addition, the network will be instantiated as an object of class AuGMEnT defined in file

In the main file you can define the network properties and set the model parameters. Most important parameters: beta (learning rate), alpha (decay rate), leak (leaky coefficient).

N.B. The leak parameter can be either be a scalara or a list. In case it is a scalar, all M units in the memory decay with that parameter (e.g. leak=1.0 for standard AuGMEnT, leak=0.7 for leaky control). Otherwise, the memory is divided in equal parts (as many as the length of the list) with each subpart with different decay (e.g. leak=[0.7,1.0] for hybrid AuGMEnT).

Other standard simulation parameters are: N_sim (number of simulations), N_trial (number of maximum trials), stop (boolean wheter to stop or not after convergence), verb (boolean to enable verbose output or not),...

'further_analysis' contains additional analysis on performance of hybrid augment on 12AX-like tasks. See the inner for further details.

'DATA' folder collects all the data of convergence times, error trends, converged matrices and pre-defined training dataset.

'IMAGES' folder collects the most relevant images of performance analysis of AuGMEnT network and its variants on 12ax, saccade-antisaccade and sequence prediction tasks. 'Visualization Codes' contains the codes for most of the plots.


  • Rombouts, Jaldert O., Sander M. Bohte, and Pieter R. Roelfsema (2015). “How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks”. In: PLOS Computational Biology 11.3, pp. 1–34. DOI: 10.1371/journal.pcbi.1004060. URL: