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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Neurosciences --- deep spiking neural networks --- SNN learning algorithms --- programming framework --- SNN benchmarks --- neuromorphics

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20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come.

Neuromorphic computing --- Brain-inspired computing --- Massively-parallel computing --- Spiking neural networks --- Neuro-robotics

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

deep spiking neural networks --- SNN learning algorithms --- programming framework --- SNN benchmarks --- neuromorphics

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Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

brain imaging --- artificial neural networks --- deep learning --- neural information processing --- backpropagation --- spiking neural networks

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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

graphene oxide --- artificial neural network --- simulation --- neural networks --- STDP --- neuromorphics --- spiking neural network --- artificial intelligence --- hierarchical temporal memory --- synaptic weight --- optimization --- transistor-like devices --- multiscale modeling --- memristor crossbar --- spike-timing-dependent plasticity --- memristor-CMOS hybrid circuit --- pavlov --- wire resistance --- AI --- neocortex --- synapse --- character recognition --- resistive switching --- electronic synapses --- defect-tolerant spatial pooling --- emulator --- compact model --- deep learning networks --- artificial synapse --- circuit design --- memristors --- neuromorphic engineering --- memristive devices --- OxRAM --- neural network hardware --- sensory and hippocampal responses --- neuromorphic hardware --- boost-factor adjustment --- RRAM --- variability --- Flash memories --- neuromorphic --- reinforcement learning --- laser --- memristor --- hardware-based deep learning ICs --- temporal pooling --- self-organization maps --- crossbar array --- pattern recognition --- strongly correlated oxides --- vertical RRAM --- autocovariance --- neuromorphic computing --- synaptic device --- cortical neurons --- time series modeling --- spiking neural networks --- neuromorphic systems --- synaptic plasticity