Efficient visual object representation 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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
AllObjectObject RecognitionSimilar Papers 제목 키워드 기반
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 t…
AllObjectObject RecognitionBiologically Plausible Learning of Text Representation with Spiking Neural Networks
This study proposes a novel biologically plausible mechanism for generating low-dimensional spike-based text representation. First, we demonstrate how to transform documents into series of spikes spike trains which are s…
Document ClassificationGeneral Classificationtext-classificationText ClassificationBiologically Plausible Learning via Bidirectional Spike-Based Distillation
Developing biologically plausible learning algorithms that can achieve performance comparable to error backpropagation remains a longstanding challenge. Existing approaches often compromise biological plausibility by ent…
Image GenerationConvolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we…
Object RecognitionRepresentation Learning using Event-based STDP
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes a…
QuantizationRepresentation Learning