Nonlinear dynamics and machine learning of recurrent spiking neural networks

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

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Image Completion using Spiking Neural Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a5294.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4449-4452

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Spiking Neural Networks ◽

Machine Learning Techniques ◽

Free Form ◽

Third Generation ◽

Image Completion ◽

Learning Techniques ◽

Original Picture

In this paper, we are showing how spiking neural networks are applied in image repainting, and its results are outstanding compared with other machine learning techniques. Spiking Neural Networks uses the shape of patterns and shifting distortion on images and positions to retrieve the original picture. Thus, Spiking Neural Networks is one of the advanced generations and third generation of machine learning techniques, and is an extension to the concept of Neural Networks and Convolutional Neural Networks. Spiking Neural Networks (SNN) is biologically plausible, computationally more powerful, and is considerably faster. The proposed algorithm is tested on different sized digital images over which free form masks are applied. The performance of the algorithm is examined to find the PSNR, QF and SSIM. The model has an effective and fast to complete the image by filling the gaps (holes).

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A Brief Review on Spiking Neural Network - A Biological Inspiration

10.20944/preprints202104.0202.v1 ◽

2021 ◽

Author(s):

Taki Hasan Rafi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Mathematical Structure ◽

Spiking Neural Networks ◽

Spiking Neural Network ◽

Current Flows ◽

Learning Machine ◽

High Level

Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

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BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

Frontiers in Neuroinformatics ◽

10.3389/fninf.2018.00089 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 25

Author(s):

Hananel Hazan ◽

Daniel J. Saunders ◽

Hassaan Khan ◽

Devdhar Patel ◽

Darpan T. Sanghavi ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Spiking Neural Networks

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Nonlinear Dynamics, Quasi-Periodic Summation, Self-Oscillating Processes, and Information Coding in Selective Spiking Neural Networks

Bulletin of the Russian Academy of Sciences Physics ◽

10.3103/s1062873818110163 ◽

2018 ◽

Vol 82 (11) ◽

pp. 1425-1430

Author(s):

M. E. Mazurov

Keyword(s):

Nonlinear Dynamics ◽

Neural Networks ◽

Spiking Neural Networks ◽

Information Coding ◽

Oscillating Processes

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Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2019-0009 ◽

2019 ◽

Vol 9 (4) ◽

pp. 283-291 ◽

Cited By ~ 5

Author(s):

Sou Nobukawa ◽

Haruhiko Nishimura ◽

Teruya Yamanishi

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Supervised Learning ◽

Pattern Classification ◽

Spike Timing ◽

Spiking Neural Networks ◽

Learning Problems ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Self Organized

Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.

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MACHINE LEARNING FOR SOFTWARE

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.04.061 ◽

2019 ◽

pp. 251-255

Author(s):

Shafagat Mahmudova

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Soft Computing ◽

Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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