A minimal model of working memory in neural systems and neuromorphic circuits

Authors: Damien Depannemaecker, Adrien d’Hollande, Gabriele Casagrande, Jiaming Wu, Marcelo Rozenberg

Presentation type: Poster at SNUFA 2026 online workshop (4-5 Nov 2026)

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.

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. We demonstrate the generality of the mechanism, implementing the single-neuron model in a minimal memristor-based neuromorphic electronic circuit and experimentally confirm its dynamics. Furthermore, we show that, contrary to common wisdom, these properties remain in a solely excitatory network of self-excitatory neurons. We derive and validate a mean-field neural mass model that remains stable while retaining working-memory functionality.

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