Membrane Potential-Driven Adaptive Threshold Plasticity for SNNs: A Bio-Inspired Mechanism Combining Inverse Depolarization Rate and Proportional Membrane Potential Dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While spiking neural networks (SNNs) have demonstrated remarkable efficiency in neuromorphic computing by emulating biological neuronal dynamics, their learning capabilities remain constrained by predominant focus on synaptic plasticity. This limitation overlooks critical neurobiological evidence showing that intrinsic neuronal plasticity, particularly dynamic threshold adaptation, plays an essential role in balancing neural responsiveness and signal fidelity. Inspired by two neurophysiological principles governing threshold regulation: 1) the inverse correlation between spiking thresholds and preceding depolarization rates, and 2) the proportional relationship between thresholds and average membrane potentials, we propose a Membrane Potential-Driven Adaptive Threshold Plasticity (MPD-ATP) framework. This biologically grounded mechanism establishes a dual-pathway control system where instantaneous depolarization rates and sustained membrane potential states jointly modulate neuronal thresholds through an adaptive scaling factor. The instantaneous depolarization rate dynamically lowers thresholds during strong input bursts, while the sustained average membrane potential adjusts the baseline threshold to stabilize firing during sparse input. This complementary regulation improves precision and robustness. Extensive evaluations on static (CIFAR-10/100) and neuromorphic (CIFAR10-DVS, DVSGesture) benchmarks demonstrate that MPD-ATP-enhanced networks achieve superior classification accuracy with enhanced noise robustness. Systematic ablation studies reveal that the coordinated interaction between depolarization-sensitive and membrane potential-proportional threshold adjustments is critical for preventing signal saturation in high-activity networks while mitigating under-activation in sparse-input scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it