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bioRxiv · 10.1101/2025.08.29.673001

Voltage-dependent reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance

Rodrigues, M.·Gasco-Galvez, C.·Vinck, M.
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Abstract

Spiking recurrent neural networks (SRNNs) rival gated RNNs on various tasks, yet they still lack several hallmarks of biological neural networks. We introduce a biologically grounded SRNN that implements Dales law with voltage-dependent AMPA and GABA reversal potentials. These reversal potentials modulate synaptic gain as a function of the postsynaptic membrane potential, and we derive theoretically how they make each neurons effective dynamics and subthreshold resonance input-dependent. We trained SRNNs on the Spiking Heidelberg Digits dataset, and show that SRNN with reversal potentials cuts spike energy by up to 4x, while increasing task accuracy. This leads to high-performing Dalean SRNNs, substantially improving on Dalean networks without reversal potentials. Thus, Dales law with reversal potentials, a core feature of biological neural networks, can render SRNNs more accurate and energy-efficient.

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Rodrigues, M., Gasco-Galvez, C., Vinck, M.. 2025-09-02. Voltage-dependent reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance. https://doi.org/10.1101/2025.08.29.673001

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