Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE)

Pitti, Alexandre; Quoy, Mathias; Lavandier, Catherine; Boucenna, Sofiane

doi:10.1016/j.neunet.2019.09.023

Cited by 13 publications

(8 citation statements)

References 120 publications

(209 reference statements)

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“…This relative code can be seen as a harsh analog to digital conversion in which the exact item values in the sequence are removed. However, in the case where

, it can represent a computational advantage to represent only the ordinal structure within the data ( 47 , 48 ) ( Fig. 3 D ).…”

Section: Modelmentioning

confidence: 99%

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Digital computing through randomness and order in neural networks

Pitti

Weidmann

Quoy

2022

Proc. Natl. Acad. Sci. U.S.A.

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We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarseness of the relative codes, we show that these principles are sufficient for coding and decoding sequences with error-free reconstruction. In particular, the number of neurons needed grows linearly with the size of the input repertoire growing exponentially. We illustrate our model by reconstructing sequences with repertoires on the order of a billion items. From this, we derive the Shannon equations for the capacity limit to learn and transfer information in the neural population, which is then generalized to any type of neural network. Following the maximum entropy principle of efficient coding, we show that random connections serve to decorrelate redundant information in incoming signals, creating more compact codes for neurons and therefore, conveying a larger amount of information. Henceforth, despite the unreliability of the relative codes, few neurons become necessary to discriminate the original signal without error. Finally, we discuss the significance of this digital computation model regarding neurobiological findings in the brain and more generally with artificial intelligence algorithms, with a view toward a neural information theory and the design of digital neural networks.

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“…This relative code can be seen as a harsh analog to digital conversion in which the exact item values in the sequence are removed. However, in the case where

, it can represent a computational advantage to represent only the ordinal structure within the data ( 47 , 48 ) ( Fig. 3 D ).…”

Section: Modelmentioning

confidence: 99%

“…We added in ref. 47 a more sophisticated hill-climbing algorithm corresponding to simulated annealing to efficiently drive the exploration process.…”

Section: Modelmentioning

confidence: 99%

Digital computing through randomness and order in neural networks

Pitti

Weidmann

Quoy

2022

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…When GridSim conducts simulation experiments, the calculation of resource scheduling time is carried out through mathematical calculation. Using this method can simplify the model of scheduling simulation, reduce the consumption of simulation experiments, and make efforts to provide targeted results [17].…”

Section: Gridsim Simulation Toolmentioning

confidence: 99%

Energy Optimization Based on Grid Resource Scheduling Algorithm

2021

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Grid technology is an emerging technology developed in the mode of computer network computing. This technology has many characteristics such as distribution, sharing, and polymorphism. In the grid environment, due to the high-performance computing of the grid, the task scheduling process becomes efficient, but it also has the problem of complex grid resource management and scheduling strategies, resulting in huge energy consumption. In order to solve the problem of energy consumption, an energy optimization model based on time constraints and energy constraints is proposed in this paper, grid resource scheduling is carried out through heuristic scheduling algorithm, and energy optimization simulation experiments are carried out under the condition of changing the number of resources and tasks. The results show that, The resource execution time corresponding to a single grid task is short, and the energy consumption value is also small. In the simulation experiment of multiple grid tasks, as the number of grid tasks increases, the task execution time increases, and the adjustment factor is 0.5 , that is, when the ratio of the time consumption factor and the energy consumption factor in the resource scheduling optimization cost function is the same, the fluctuation of the energy consumption rate is relatively stable.

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“…The index weight determination in the SPA and VFS coupling model is particularly important, which is worthy of further discussion. In this paper, an improved set pair analysis-variable fuzzy set coupling evaluation model (SPA-VFS) is established by coupling rank method [20] with entropy weight method [21]. In modern intelligent evaluation methods, the BPNN evaluation method has the problems of slow convergence and easy to fall into local optimum [22].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm

2021

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The research on the sustainability evaluation of innovation and entrepreneurship education for clean energy majors in colleges and universities can not only cultivate more and better innovative and entrepreneurial talents for the development of sustainable energy but also provide a reference for the sustainable development of innovation and entrepreneurship education for other majors. To achieve systematic and comprehensive scientific evaluation, this paper proposes an evaluation model based on SPA-VFS and Chaos bat algorithm to optimize GRNN. Firstly, the sustainability evaluation index system of innovation and entrepreneurship education for clean energy major in colleges and universities is constructed from the four aspects of the environment, investment, process, and results, and the meaning of each evaluation index is explained; Then, combined with variable fuzzy set evaluation theory (VFS) and set pair analysis theory (SPA), the classical evaluation model based on SPA-VFS is constructed, and the entropy weight method and rank method are coupled to obtain the index weight. The basic bat algorithm is improved by using Tent chaotic mapping, and the chaotic bat algorithm (CBA) is proposed. The generalized regression neural network (GRNN) model is optimized by CBA, and the intelligent evaluation model based on CBA-GRNN is obtained to realize fast real-time calculation; finally, a numerical example is used to verify the scientificity and accuracy of the model proposed in this paper. This study is conducive to a comprehensive evaluation of the sustainability of innovation and entrepreneurship education for clean energy major in colleges and universities, and is conducive to the healthy and sustainable development of innovation and entrepreneurship education for clean energy major in colleges and universities, so as to provide more innovative and entrepreneurial talents for the clean energy industry.

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Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE)

Cited by 13 publications

References 120 publications

Digital computing through randomness and order in neural networks

Digital computing through randomness and order in neural networks

Energy Optimization Based on Grid Resource Scheduling Algorithm

Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm

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