Biological evidence suggests that adaptation of synaptic delays on short to medium timescales plays an important role in learning in the brain. Inspired by biology, we explore the feasibility and power of using synaptic delays to solve challenging tasks even when the synaptic weights are not trained but kept at randomly chosen fixed values. We show that training ONLY the delays in feed-forward spiking networks using backpropagation can achieve performance comparable to the more conventional weight training. Moreover, further constraining the weights to ternary values does not significantly affect the networks' ability to solve the tasks using only the synaptic delays. We demonstrate the task performance of delay-only training on MNIST and Fashion-MNIST datasets in preliminary experiments. This demonstrates a new paradigm for training spiking neural networks and sets the stage for models that can be more efficient than the ones that use weights for computation.

翻译：生物学证据表明，短至中等时间尺度上的突触延迟适应在大脑学习中发挥重要作用。受生物学启发，我们探索了即使突触权重未经训练而保持随机固定值的情况下，利用突触延迟解决具有挑战性任务的可行性与能力。研究表明，仅通过反向传播训练前馈脉冲网络中的延迟，即可达到与传统权重训练相当的性能。进一步将权重约束为三值状态时，网络仅依赖突触延迟解决问题的能力并未受到显著影响。我们通过MNIST和Fashion-MNIST数据集的初步实验，验证了仅延迟训练的任务表现。这为脉冲神经网络的训练提供了新范式，并为构建比基于权重的计算模型更高效的模型奠定了基础。