A Brief Review on Spiking Neural Network - A Biological Inspiration

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 into a biological-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.

[1]  Neena Aloysius,et al.  A review on deep convolutional neural networks , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[2]  Filip Ponulak,et al.  Introduction to spiking neural networks: Information processing, learning and applications. , 2011, Acta neurobiologiae experimentalis.

[3]  Seema Shah,et al.  A Review of Machine Learning and Deep Learning Applications , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[4]  Faramarz Safi-Esfahani,et al.  A Review on Neural Turing Machine (NTM) , 2020, SN Computer Science.

[5]  Sander M. Bohte,et al.  Computing with Spiking Neuron Networks , 2012, Handbook of Natural Computing.

[6]  Tobi Delbrück,et al.  Training Deep Spiking Neural Networks Using Backpropagation , 2016, Front. Neurosci..

[7]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[8]  Amit Kumar Mondal.  A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions , 2020, ArXiv.

[9]  Jilles Vreeken,et al.  Spiking neural networks, an introduction , 2003 .

[10]  Terrence J. Sejnowski,et al.  Gradient Descent for Spiking Neural Networks , 2017, NeurIPS.

[11]  Malu Zhang,et al.  An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks , 2016, PloS one.

[12]  Bipin Rajendran,et al.  Spiking neural networks — Algorithms, hardware implementations and applications , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[13]  S. Thorpe,et al.  STDP-based spiking deep convolutional neural networks for object recognition , 2018 .

[14]  Ammar Belatreche,et al.  A review of learning in biologically plausible spiking neural networks , 2019, Neural Networks.

[15]  Nikola Kasabov,et al.  Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces , 2020, Neural Networks.

[16]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[17]  Albert Bifet,et al.  Spiking Neural Networks and Online Learning: An Overview and Perspectives , 2019, Neural Networks.

[18]  Osvaldo Simeone,et al.  A Very Brief Introduction to Machine Learning With Applications to Communication Systems , 2018, IEEE Transactions on Cognitive Communications and Networking.

[19]  M. O’Halloran,et al.  Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast , 2011 .

[20]  Lei Deng,et al.  Direct Training for Spiking Neural Networks: Faster, Larger, Better , 2018, AAAI.

[21]  Evangelos Eleftheriou,et al.  Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses , 2019, Scientific Reports.

[22]  Michael Pfeiffer,et al.  Deep Learning With Spiking Neurons: Opportunities and Challenges , 2018, Front. Neurosci..

[23]  Hesham H. Amin,et al.  Spiking Neural Networks: Learning, Applications, and Analysis , 2011 .

[24]  Osvaldo Simeone,et al.  An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications , 2019, IEEE Signal Processing Magazine.

[25]  M. Dehmer,et al.  An Introductory Review of Deep Learning for Prediction Models With Big Data , 2020, Frontiers in Artificial Intelligence.

[26]  Ayon Dey,et al.  Machine Learning Algorithms: A Review , 2022, International Journal of Science and Research (IJSR).

[27]  Deepak Khosla,et al.  Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.

[28]  Siti Aisyah Mohamed,et al.  A review on data clustering using spiking neural network (SNN) models , 2019, Indonesian Journal of Electrical Engineering and Computer Science.

[29]  Reza Khanbabaie,et al.  Practical applications of spiking neural network in information processing and learning , 2015 .

[30]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[31]  Timothée Masquelier,et al.  Deep Learning in Spiking Neural Networks , 2018, Neural Networks.

[32]  Juan Martín Carpio Valadez,et al.  Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems , 2019, Comput. Intell. Neurosci..

[33]  Nikola K. Kasabov,et al.  NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data , 2014, Neural Networks.