利用整合放电神经元对混合带限信号进行编码和解码
Encoding and Decoding Mixed Bandlimited Signals using Spiking Integrate-and-Fire Neurons
摘要：
传统的采样集中于通过记录已知时间点的信号幅度来对带限信号进行编码和解码。或者，可以使用生物启发的方案来进行采样。其中之一是集成和发射时间编码机（IF-TEM）。它们的行为就像简化版的尖峰神经元，并使用尖峰时间而不是幅度来编码它们的输入。此外，当这些神经元中的多个联合处理一组混合信号时，它们形成前馈尖峰神经网络中的一层。在本文中，我们调查的编码和解码潜力，这样的层。我们提出了一个设置来采样一组带限信号，通过混合它们并使用不同的IF-TEM采样结果。我们提供的条件，从样本中的无噪声的情况下，完美的恢复的信号集，并建议一种算法来执行重建。
英文摘要：
Conventional sampling focuses on encoding and decoding bandlimited signals by recording signal amplitudes at known time points. Alternately, sampling can be approached using biologically-inspired schemes. Among these are integrate-and-fire time encoding machines (IF-TEMs). They behave like simplified versions of spiking neurons and encode their input using spike times rather than amplitudes. Moreover, when multiple of these neurons jointly process a set of mixed signals, they form one layer in a feedforward spiking neural network. In this paper, we investigate the encoding and decoding potential of such a layer. We propose a setup to sample a set of bandlimited signals, by mixing them and sampling the result using different IF-TEMs. We provide conditions for perfect recovery of the set of signals from the samples in the noiseless case, and suggest an algorithm to perform the reconstruction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
