# Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?

## TL;DR

This paper evaluates the performance of spiking neural networks with spike-timing dependent plasticity on image classification tasks, comparing them to auto-encoders and analyzing their limitations for visual recognition.

## Contribution

It extends the evaluation of SNNs to color images and identifies key bottlenecks hindering their effectiveness in image recognition tasks.

## Key findings

- SNNs perform comparably to auto-encoders on recognition datasets.
- Limitations in on-center/off-center coding affect color image processing.
- Current inhibition mechanisms are ineffective for SNN-based recognition.

## Abstract

Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition.