论文标题
通过失重尖峰神经网络中的时间延迟记忆
Memory via Temporal Delays in weightless Spiking Neural Network
论文作者
论文摘要
神经科学界的一个普遍观点是，记忆是在神经元之间的连接强度中编码的。这种感知使人工神经网络模型集中在连接权重作为调节学习的关键变量上。在本文中，我们提出了可以执行简单分类任务的失重尖峰神经网络的原型。该网络中的记忆存储在神经元之间的时机中，而不是连接的强度，并使用HEBBIAN尖峰时序依赖性可塑性（STDP）进行了训练，该速度调节连接的延迟。
A common view in the neuroscience community is that memory is encoded in the connection strength between neurons. This perception led artificial neural network models to focus on connection weights as the key variables to modulate learning. In this paper, we present a prototype for weightless spiking neural networks that can perform a simple classification task. The memory in this network is stored in the timing between neurons, rather than the strength of the connection, and is trained using a Hebbian Spike Timing Dependent Plasticity (STDP), which modulates the delays of the connection.
