A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons.
A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons.
复制标题
具有集成和激发神经元的时间编码信号的新颖重建框架。
DOI：
10.1162/neco_a_00764
复制
发表时间：
2015
影响因子：
2.9
通讯作者：
Florescu D
中科院分区：
文献类型：
--
作者：
Florescu D
整合和激发神经元是时间编码机器，它将模拟信号的幅度转换为非均匀的、严格递增的尖峰时间序列。在一定条件下，利用时间解码机可以从非均匀尖峰时间序列中重构出编码信号。利用非均匀采样理论，研究了有限带宽空间和一般平移不变空间的时间编码和时间解码方法。这封信提出了一个新的框架，研究中频时间编码和解码的中频时间编码问题重新制定为一个统一的采样问题。这个框架形成了两个新的算法的基础上重建信号的尖峰时间序列。我们证明，所提出的重建算法更快，因此更适合于实时处理，同时提供了类似的精度水平，相比标准的重建算法。
Integrate-and-fire neurons are time encoding machines that convert the amplitude of an analog signal into a nonuniform, strictly increasing sequence of spike times. Under certain conditions, the encoded signals can be reconstructed from the nonuniform spike time sequences using a time decoding machine. Time encoding and time decoding methods have been studied using the nonuniform sampling theory for band-limited spaces, as well as for generic shift-invariant spaces. This letter proposes a new framework for studying IF time encoding and decoding by reformulating the IF time encoding problem as a uniform sampling problem. This framework forms the basis for two new algorithms for reconstructing signals from spike time sequences. We demonstrate that the proposed reconstruction algorithms are faster, and thus better suited for real-time processing, while providing a similar level of accuracy, compared to the standard reconstruction algorithm.