论文标题
使用生物学上合理的尖峰延迟代码和赢家全部抑制的有效视觉对象表示
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition
论文作者
论文摘要
深度神经网络在关键视觉挑战（例如对象识别）中超过了人类的表现，但需要大量的能量，计算和记忆。相比之下，尖峰神经网络（SNN）具有提高对象识别系统的效率和生物合理性的潜力。在这里，我们提出了一种SNN模型，该模型使用Spike-Latency编码和赢家全部抑制（WTA-I）有效地表示时尚MNIST数据集的视觉刺激。将刺激与中心旋转的接收场进行预处理，然后喂入一层尖刺神经元，其突触权重使用峰值依赖性塑性（STDP）进行更新。我们研究了代表对象的质量如何在不同的WTA-I方案下发生变化，并证明了150个尖峰神经元的网络可以有效地表示40个尖峰的对象。研究如何使用SNN中生物学上合理的学习规则来研究如何实施核心对象识别，这不仅可能进一步我们对大脑的理解，还可能导致新颖而有效的人工视觉系统。
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli from the Fashion MNIST dataset. Stimuli were preprocessed with center-surround receptive fields and then fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity (STDP). We investigate how the quality of the represented objects changes under different WTA-I schemes and demonstrate that a network of 150 spiking neurons can efficiently represent objects with as little as 40 spikes. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.
