Recent studies have shown that convolutional neural networks (CNNs) are not the only feasible solution for image classification. Furthermore, weight sharing and backpropagation used in CNNs do not correspond to the mechanisms present in the primate visual system. To propose a more biologically plausible solution, we designed a locally connected spiking neural network (SNN) trained using spike-timing-dependent plasticity (STDP) and its reward-modulated variant (R-STDP) learning rules. The use of spiking neurons and local connections along with reinforcement learning (RL) led us to the nomenclature BioLCNet for our proposed architecture. Our network consists of a rate-coded input layer followed by a locally connected hidden layer and a decoding output layer. A spike population-based voting scheme is adopted for decoding in the output layer. We used the MNIST dataset to obtain image classification accuracy and to assess the robustness of our rewarding system to varying target responses.
翻译:最近的研究显示,革命性神经网络并不是图像分类的唯一可行解决办法。此外,CNN使用的重量共享和背面插图与灵长类视觉系统中的机制不符。为了提出更符合生物学的解决方案,我们设计了一个本地连接的喷射神经网络(SNN),该网络使用悬浮刺激依赖性塑料(STDP)及其奖励调制变异学习规则来进行训练。使用神经神经元和本地连接以及强化学习(RL),导致我们在拟议架构中使用BioLCNet的术语。我们的网络由一个速记输入层组成,然后有一个本地连接的隐藏层和一个解码输出层。在产出层解码时采用了基于人口的快速投票办法。我们使用MNIST数据集来获取图像分类准确性,并评估我们奖励系统的强性,以不同的目标回应。