Algorithms for obtaining parsimonious higher order neurons

Name: Algorithms for obtaining parsimonious higher order neurons
Author: Sezener, C. E., Öztop, Erhan

İsim	Algorithms for obtaining parsimonious higher order neurons
Yazar	Sezener, C. E., Öztop, Erhan
Basım Tarihi:	2017
Basım Yeri	- Springer International Publishing
Konu	Learning systems, Neurons, Central nervous systems, Deterministic algorithms, Heaviside step function, Hybrid algorithms, Meta-heuristic search, Numerical comparison, Temporal dynamics, Time complexity
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-3-319-68599-1
Kayıt Numarası	d88f661c-2a2c-4189-b56f-fba9f406e1b8
Lokasyon	Computer Science
Tarih	2017
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Most neurons in the central nervous system exhibit all-or-none firing behavior. This makes Boolean Functions (BFs) tractable candidates for representing computations performed by neurons, especially at finer time scales, even though BFs may fail to capture some of the richness of neuronal computations such as temporal dynamics. One biologically plausible way to realize BFs is to compute a weighted sum of products of inputs and pass it through a heaviside step function. This representation is called a Higher Order Neuron (HON). A HON can trivially represent any n-variable BF with 2n product terms. There have been several algorithms proposed for obtaining representations with fewer product terms. In this work, we propose improvements over previous algorithms for obtaining parsimonious HON representations and present numerical comparisons. In particular, we improve the algorithm proposed by Sezener and Oztop [1] and cut down its time complexity drastically, and develop a novel hybrid algorithm by combining metaheuristic search and the deterministic algorithm of Oztop.
DOI	10.1007/978-3-319-68600-4_18
Cilt	10613

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Algorithms for obtaining parsimonious higher order neurons

Yazar Sezener, C. E., Öztop, Erhan

Basım Tarihi 2017

Basım Yeri - Springer International Publishing

Konu Learning systems, Neurons, Central nervous systems, Deterministic algorithms, Heaviside step function, Hybrid algorithms, Meta-heuristic search, Numerical comparison, Temporal dynamics, Time complexity

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-3-319-68599-1

Kayıt Numarası d88f661c-2a2c-4189-b56f-fba9f406e1b8

Lokasyon Computer Science

Tarih 2017

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Most neurons in the central nervous system exhibit all-or-none firing behavior. This makes Boolean Functions (BFs) tractable candidates for representing computations performed by neurons, especially at finer time scales, even though BFs may fail to capture some of the richness of neuronal computations such as temporal dynamics. One biologically plausible way to realize BFs is to compute a weighted sum of products of inputs and pass it through a heaviside step function. This representation is called a Higher Order Neuron (HON). A HON can trivially represent any n-variable BF with 2n product terms. There have been several algorithms proposed for obtaining representations with fewer product terms. In this work, we propose improvements over previous algorithms for obtaining parsimonious HON representations and present numerical comparisons. In particular, we improve the algorithm proposed by Sezener and Oztop [1] and cut down its time complexity drastically, and develop a novel hybrid algorithm by combining metaheuristic search and the deterministic algorithm of Oztop.

DOI 10.1007/978-3-319-68600-4_18

Cilt 10613