Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks
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| Format: | Preprint |
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2026
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| _version_ | 1866915960478236672 |
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| author | Xu, Yinhao Gottwald, Georg A. Kuncic, Zdenka |
| author_facet | Xu, Yinhao Gottwald, Georg A. Kuncic, Zdenka |
| contents | Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a heterogeneous network topology. In this work, we demonstrate that such networks naturally generate neuronal population spiking dynamics similar to those observed in biological neuronal systems. This study investigates the intrinsic and emergent dynamics of memristive networks mathematically and numerically for both DC and AC input signals. Nonlinear spike-like features are maximized when the frequency of the input driving signal matches the network's intrinsic dynamical timescale, where nonlinear resonance is observed. Furthermore, the optimal frequency for computation is found to be the maximal frequency before the onset of resonance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_18015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks Xu, Yinhao Gottwald, Georg A. Kuncic, Zdenka Disordered Systems and Neural Networks Emerging Technologies Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a heterogeneous network topology. In this work, we demonstrate that such networks naturally generate neuronal population spiking dynamics similar to those observed in biological neuronal systems. This study investigates the intrinsic and emergent dynamics of memristive networks mathematically and numerically for both DC and AC input signals. Nonlinear spike-like features are maximized when the frequency of the input driving signal matches the network's intrinsic dynamical timescale, where nonlinear resonance is observed. Furthermore, the optimal frequency for computation is found to be the maximal frequency before the onset of resonance. |
| title | Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks |
| topic | Disordered Systems and Neural Networks Emerging Technologies |
| url | https://arxiv.org/abs/2604.18015 |