Abstract:
: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.
journal_name
Neural Computjournal_title
Neural computationauthors
Florescu D,Coca Ddoi
10.1162/NECO_a_00764subject
Has Abstractpub_date
2015-09-01 00:00:00pages
1872-98issue
9eissn
0899-7667issn
1530-888Xjournal_volume
27pub_type
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