SpikingSIM: A Bio-inspired Spiking Simulator

A simulator of spiking cameras that converts images/videos to spiking data.

$\bullet$ Introduction

The motivation of this work is to simulate the working principle of spiking cameras to convert existing image/video data to spiking data.

As shown in the Figure 1 bellow, we formulate the relation: grayscale (image) -> scene brightness intensity -> spike firing rate (spike). The source and distribution of noises are also considered to simulate more realistic spiking data.

In the following example, we first reconstruct intensity map (a) from raw spikes (c) and use it to simulate spikes (d). The image (b) is reconstructed from simulated spikes (d). The visualization shows that the synthetic data (d) generated by SpikingSIM are quite similar with the raw data (c). (It might take a little time for the images to load.)

We also convert an image (from Caltech-101 dataset) into synthetic spikes and visualize the results as bellow.

$\bullet$ Usage

Code is in ./Source/sim.py.

1. Input: Images (load_image, $G$) | Simulation Time (sim_time, $T$)

2. Output: Spike Array (syn_spike, $H\times W\times T$)

3. Noise: Noise intensity can be adjusted by Gaussian (Inherent_Noise, $\mathcal{N}$) and Poisson (Diffuse_Noise, $\mathcal{P}$) models

4. Simulation: Simulate the spike generation process of spiking cameras (Simulation)

   * Note: We release a simplified version for fast simulation (Simulation_Simplified)

$\bullet$ Citation

We hope this work could promote your research. Thanks.

Zhao J, Zhang S, Ma L, Yu Z and Huang T. 2022. SpikingSIM: A Bio-inspired Spiking Simulator // 2022 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2022.