The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.

神经元发出尖峰的精确时间在塑造传出生物神经元的反应中起着至关重要的作用。神经活动的时间维度对于理解神经生物学中的信息处理具有重要意义，特别是对于神经形态硬件（例如基于事件的相机）的性能。尽管如此，许多人工神经模型忽视了神经活动的这一关键时间维度。在这项研究中，我们提出了一个模型，旨在使用配备异质突触延迟的尖峰神经元层来有效检测时间尖峰基序。我们的模型利用树突树上存在的不同突触延迟，使时间精确的突触输入的特定安排能够在到达基础树突树时同步。我们将这个过程形式化为时不变逻辑回归，可以使用标记数据进行训练。为了证明其实际功效，我们将该模型应用于转换为事件流的自然视频，模拟生物视网膜或基于事件的相机的输出。为了评估模型在检测视觉运动方面的鲁棒性，我们通过选择性修剪权重进行实验，并证明即使在工作量显着减少的情况下，模型仍然保持高效。总之，通过提供全面的、事件驱动的计算构建块，异构延迟的结合有可能大大提高未来尖峰神经网络算法的性能，特别是在神经形态芯片的背景下。