書目名稱 | Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits | 編輯 | Dorian Florescu | 視頻video | http://file.papertrans.cn/825/824256/824256.mp4 | 概述 | Nominated as an outstanding Ph.D. thesis by the University of Sheffield.Reformulates, for the first time, the encoding performed by the integrate-and-fire neuron model as a problem of uniform sampling | 叢書名稱 | Springer Theses | 圖書封面 |  | 描述 | .This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed...A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron...Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations..A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of alinear filter, given the input of the filter encoded with the same neuron model.. | 出版日期 | Book 2017 | 關(guān)鍵詞 | System Identification; Spiking Neural Circuits; Time Encoding Machines; Time Decoding Machines; Integrat | 版次 | 1 | doi | https://doi.org/10.1007/978-3-319-57081-5 | isbn_softcover | 978-3-319-86072-5 | isbn_ebook | 978-3-319-57081-5Series ISSN 2190-5053 Series E-ISSN 2190-5061 | issn_series | 2190-5053 | copyright | Springer International Publishing AG 2017 |
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