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Bibliographic Details
Main Author:	Kumar, Amit
Format:	Recurso digital
Language:	English
Published:	Zenodo 2026
Subjects:	spiking neural networks epileptiform synchrony E/I balance neuromorphic computing EBRAINS SpiNNaker sparsity intelligence PyNN phonosemantics AGI BrainScaleS nonlinear dynamics Kuramoto model thermodynamic noise Johnson-Nyquist noise Hodgkin-Huxley model conductance-based models biophysics
Online Access:	https://doi.org/10.5281/zenodo.19602055
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This paper reports the empirical discovery and formal characterization of the Epileptiform Synchrony Limit (ESL): a universal failure mode of recurrent Spiking Neural Networks (SNNs) in which scaling contextual memory (membrane time constant τ_m) without maintaining strict Excitatory/Inhibitory (E/I) balance causes irreversible collapse of sparse semantic representations into a ~500 Hz synchronous firing state. Using the PyNN/Brian2 framework on the DDIN phonosemantic architecture, I executed three systematic experiments: a τ_m sweep, an 80/20 E/I lateral inhibition architecture, and a global Winner-Take-All (WTA) inhibitory circuit. In all high-τ_m configurations, the network entered a seizure state destroying all semantic clustering, regardless of inhibitory weight magnitude. I prove formally that this behavior is not a tuning failure but a fundamental physical constraint: when continuous Poisson input drive exceeds the time-averaged inhibitory conductance, the membrane voltage integrates without bound until the threshold is saturated at every timestep. No static weight-based inhibitory circuit can prevent this collapse under continuous input. The condition for stable asynchronous firing requires dynamic E/I equilibrium, requiring either (i) Hodgkin-Huxley adaptation currents (M-current, AHP) for single-neuron homeostasis, or (ii) thermodynamic analog noise from BrainScaleS hardware as stochastic regularization. I further prove that the ESL is not pathological but constitutive: the postulate "Intelligence requires The Void (Sparsity)" holds at the biophysical level. Continuous, unspaced spiking activity mathematically precludes the formation of sparse distributed representations that carry semantic structure. These findings establish the ESL as a formal boundary condition for neuromorphic AGI design, and identify its resolution as the primary remaining prerequisite for physical EBRAINS hardware deployment.

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