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Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation
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DOI：10.3389/fnsys.2026.1788937.png)
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Abstract
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IntroductionUnderstanding how artificial neural networks (ANNs) can capture biologically meaningful dynamics is a central challenge in systems neuroscience. In this work; we investigate whether spiking neural networks (SNNs) can function not only as machine-learning tools but also as biologically inspired computational analogs and tractable testbeds for studying pathological neural dynamics.MethodsWe implemented a spiking autoencoder composed of Leaky Integrate-and-Fire and Synaptic neuron models to create a controlled framework for analyzing how biologically related parametric changes to neuronal and synaptic dynamics influence learning and information transfer. By tuning model parameters to induce persistent overfiring-like behavior; we emulated a hyperexcitability-like regime conceptually analogous to NaV channel dysfunction in hippocampal circuits. Reconstruction performance and network activity were evaluated under both noiseless and noisy conditions.ResultsThe induced hyperexcitability-like regime degraded image reconstruction performance and disrupted stable information propagation; consistent with impaired processing in hyperexcitable neural systems. Layer-wise firing-rate analysis revealed that the altered regime was characterized by unstable activity redistribution rather than sustained global overactivation. Importantly; introducing controlled Gaussian noise into the input stream partially restored reconstruction quality and improved learning performance; suggesting that stochastic perturbations can partially compensate for instability in dysfunctional network regimes.DiscussionThese findings demonstrate that specific SNN parameter regimes can reproduce key signatures of pathological excitability while also providing a platform for investigating compensatory mechanisms. Overall; this work positions spiking autoencoders as scalable; biologically grounded frameworks for hypothesis-driven studies of neural dysfunction and candidate interventions; supporting the integration of ANN methodologies with mechanistic models in systems neuroscience.
Keywords:
spiking neural network
computational neuroscience
temporal lobe epilepsy
autoencoder
biological neural modeling
Gaussian noise
image compression
LIF neuron model
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