A minimal model of working memory in neural systems and neuromorphic circuits
Abstract
Phenomenological spiking neuron models such as Izhikevich, adaptive quadratic integrate-and-fire (aQIF), and Adaptive Exponential (AdEx) are widely used because of their simplicity and numerical efficiency. These models reproduce diverse neuronal dynamics through a slow self-inhibitory adaptation variable. Here we introduce their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents. This minimal modification enables robust persistent spiking and working-memory dynamics without compromising computational efficiency. These properties remain in excitatory spiking neural networks. We then derive and validate a mean-field neural mass model that remains stable while retaining working-memory functionality. Additionally, we implement the single-neuron model in a minimal memristor-based neuromorphic circuit and experimentally confirm its dynamics. These results provide scalable tools for large-scale brain simulations and neuromorphic applications in robotics, brain-machine interfaces, and edge AI devices.
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BibTeX
@article{depannemaecker2026minimal,
title = {{A minimal model of working memory in neural systems and neuromorphic circuits}},
author = {Damien Depannemaecker and Adrien D’hollande and Gabriele Casagrande and Jiaming Wu and Marcelo J. Rozenberg},
journal = {Nature Communications},
year = {2026},
month = nov,
doi = {10.1038/s41467-026-75704-3},
url = {https://hal.science/hal-05853655v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - A minimal model of working memory in neural systems and neuromorphic circuits
AU - Damien Depannemaecker
AU - Adrien D’hollande
AU - Gabriele Casagrande
AU - Jiaming Wu
AU - Marcelo J. Rozenberg
PY - 2026
DA - 2026/11/05
JO - Nature Communications
DO - 10.1038/s41467-026-75704-3
UR - https://hal.science/hal-05853655v1
AB - Phenomenological spiking neuron models such as Izhikevich, adaptive quadratic integrate-and-fire (aQIF), and Adaptive Exponential (AdEx) are widely used because of their simplicity and numerical efficiency. These models reproduce diverse neuronal dynamics through a slow self-inhibitory adaptation variable. Here we introduce their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents. This minimal modification enables robust persistent spiking and working-memory dynamics without compromising computational efficiency. These properties remain in excitatory spiking neural networks. We then derive and validate a mean-field neural mass model that remains stable while retaining working-memory functionality. Additionally, we implement the single-neuron model in a minimal memristor-based neuromorphic circuit and experimentally confirm its dynamics. These results provide scalable tools for large-scale brain simulations and neuromorphic applications in robotics, brain-machine interfaces, and edge AI devices.
SN - 2041-1723
SN - 2041-1723
ER -