# DVA-Neurons: Design and Verification of Adaptive LIF Neurons: From Single-Neuron Dynamics to Multi-Neuron Spiking Networks

arXiv: https://arxiv.org/abs/2609.22775 · HTML: https://mlsanity.com/paper/2609.22775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 19 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition](https://mlsanity.com/paper/2302.01194/) (2023)
- [Event-Driven Learning for Spiking Neural Networks](https://mlsanity.com/paper/2403.00270/) (2024)
- [Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks](https://mlsanity.com/paper/2207.04889/) (2022)
- [Resonant Energy Recycling SRAM Architecture](https://mlsanity.com/paper/2010.01767/) (2020)
- [Compiling Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2004.03717/) (2020)
- [Power and Skew Reduction Using Resonant Energy Recycling in 14-nm FinFET Clocks](https://mlsanity.com/paper/2205.07949/) (2022)
- [EA: An Event Autoencoder for High-Speed Vision Sensing](https://mlsanity.com/paper/2507.06459/) (2025)
- [EventDrive: Event Cameras for Vision-Language Driving Intelligence](https://mlsanity.com/paper/2606.18242/) (2026)
- [An Efficient Power Management Unit With Continuous MPPT and Energy Recycling for Wireless Millimetric Biomedical Implants](https://mlsanity.com/paper/2602.02376/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computation of biological neurons. However, two challenges limit their practical deployment. First, fixed-parameter Leaky Integrate-and-Fire (LIF) neurons lack the adaptation mechanisms observed in biology, where neurons modulate their excitability based on firing history. Second, scaling from single neurons to multi-neuron networks introduces challenges in synaptic weight distribution and inter-neuron spike routing that are absent in isolated designs. This paper addresses both issues through the extension, verification, and physical implementation of adaptive LIF neurons at three architectural scales. This work contributes: a 2nd-order neuron with two-stage synaptic filtering for richer temporal dynamics; a fully-connected 6-neuron spiking network with configurable weights (100 to 5) demonstrating weight-based inter-neuron communication; and a direct verification methodology enabling per-cycle observation of all internal states. All designs were synthesized targeting Selected Area Electron Diffraction (SAED) 14 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology at 1 GHz and verified with Cocotb-based Python testbenches under pulsed current stimuli (amplitude 80, ISI=3). The results show that adaptation effectively modulates firing: 31% suppression in the 2nd-order neuron (25 vs. 36 spikes) and 31% reduction in postsynaptic firing in the network (18 vs. 26 spikes). Physically, the 2nd-order neuron costs 1.77x more area and 1.52x more power than the 1st-order baseline, while the 6-neuron network demonstrates near-linear scaling (5.7x area, 5.3x power). Seven verification bugs spanning testbench connectivity, fixed-point overflow, and Verilog expression-width semantics are documented.