# Adaptive Integrate-and-Fire Time Encoding Machine with Quantization

arXiv: https://arxiv.org/abs/2403.02992 · HTML: https://mlsanity.com/paper/2403.02992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 63 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [FRI-TEM: Time Encoding Sampling of Finite-Rate-of-Innovation Signals](https://mlsanity.com/paper/2106.05564/) (2021)
- [Reconstructing Classes of Non-bandlimited Signals from Time Encoded Information](https://mlsanity.com/paper/1905.03183/) (2019)
- [Sampling and Reconstruction of Bandlimited Signals with Multi-Channel Time Encoding](https://mlsanity.com/paper/1907.05673/) (2019)
- [Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation](https://mlsanity.com/paper/2208.10234/) (2022)
- [Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS](https://mlsanity.com/paper/2201.03006/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Non-Uniform Quantization Framework for Time-Encoding Machines](https://mlsanity.com/paper/2511.02728/) (2025)
- [Adaptive Compressed Integrate-and-Fire Time Encoding Machine](https://mlsanity.com/paper/2511.02444/) (2025)

## Abstract (arXiv)

An integrate-and-fire time-encoding machine (IF-TEM) is an effective asynchronous sampler that translates amplitude information into non-uniform time sequences. In this work, we propose a novel Adaptive IF-TEM (AIF-TEM) approach. This design dynamically adjusts the TEM's sensitivity to changes in the input signal's amplitude and frequency in real-time. We provide a comprehensive analysis of AIF-TEM's oversampling and distortion properties. By the adaptive adjustments, AIF-TEM as we show can achieve significant performance improvements in terms of sampling rate-distortion in a practical finite regime. We demonstrate empirically that in the scenarios tested AIF-TEM outperforms classical IF-TEM and traditional Nyquist (i.e., periodic) sampling methods for band-limited signals. In terms of Mean Square Error (MSE), the reduction reaches at least 12dB (fixing the oversampling rate). Additionally, we investigate the quantization process for AIF-TEM and analyze the quantization MSE bound. Empirical results show that classic quantization for AIF-TEM improves performance by at least 14 dB compared to IF-TEM. We introduce a dynamic quantization technique for AIF-TEM, which further improves performance compared to classic quantization. Empirically, this reduction reaches at least 10 dB compared to classic quantization for AIF-TEM.