Abstract
This chapter proposes a new model for processing information using spike trains, by developing a direct relationship between the input and output of a linear filter, both encoded into spike time sequences by a TEM. The TEM considered in this study is the IF neuron model. The proposed representation forms the basis for a new algorithm to compute the time encoded output directly from the input spike time sequence. The approximation error introduced by the proposed implementation is characterized by deriving an error bound between the real and estimated spike times that is a function of the IF neuron parameters. The proposed direct encoding approach is much faster than the indirect approach, involving simulating the filter output and subsequent encoding. A numerical simulation study is used to illustrate the approach.
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Florescu, D. (2017). A New Method for Implementing Linear Filters in the Spike Domain. In: Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-57081-5_6
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DOI: https://doi.org/10.1007/978-3-319-57081-5_6
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