使用生物学上合理的峰值延迟代码和赢家通吃抑制的视觉对象的高效多尺度表示。

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Melani Sanchez-Garcia, Tushar Chauhan, Benoit R Cottereau, Michael Beyeler
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引用次数: 1

摘要

深度神经网络在物体识别等关键视觉挑战方面的表现超过了人类,但需要大量的能量、计算和内存。相比之下,尖峰神经网络(snn)有潜力提高目标识别系统的效率和生物合理性。在这里,我们提出了一个SNN模型,该模型使用峰值延迟编码和赢家通吃抑制(WTA-I)来有效地表示视觉刺激,并使用多尺度并行处理。模拟早期视觉皮层的神经元反应特性,用三种不同的空间频率(SF)通道对图像进行预处理,然后将图像馈送给一层尖峰神经元,这些神经元的突触权重通过尖峰时间依赖的可塑性进行更新。我们研究了在不同的SF波段和WTA-I方案下表征对象的质量变化。我们证明了一个由200个尖峰神经元组成的网络,调整为3个sf,可以有效地表示每个神经元只有15个尖峰的物体。研究如何在snn中使用生物学上合理的学习规则来实现核心对象识别,不仅可以进一步加深我们对大脑的理解,而且还可以带来新颖高效的人工视觉系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition.

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition.

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 using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. 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.

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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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