Real-Time Multi-Chip Neural Network for Cognitive Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Real-Time Multi-Chip Neural Network for Cognitive Systems write by Amir Zjajo. This book was released on 2022-09-01. Real-Time Multi-Chip Neural Network for Cognitive Systems available in PDF, EPUB and Kindle. Simulation of brain neurons in real-time using biophysically-meaningful models is a pre-requisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. In spiking neural networks (SNNs), propagated information is not just encoded by the firing rate of each neuron in the network, as in artificial neural networks (ANNs), but, in addition, by amplitude, spike-train patterns, and the transfer rate. The high level of realism of SNNs and more significant computational and analytic capabilities in comparison with ANNs, however, limit the size of the realized networks. Consequently, the main challenge in building complex and biophysically-accurate SNNs is largely posed by the high computational and data transfer demands.Real-Time Multi-Chip Neural Network for Cognitive Systems presents novel real-time, reconfigurable, multi-chip SNN system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. The system use double floating-point arithmetic for the most biologically accurate cell behavior simulation, and is flexible enough to offer an easy implementation of various neuron network topologies, cell communication schemes, as well as models and kinds of cells. The system offers a high run-time configurability, which reduces the need for resynthesizing the system. In addition, the simulator features configurable on- and off-chip communication latencies as well as neuron calculation latencies. All parts of the system are generated automatically based on the neuron interconnection scheme in use. The simulator allows exploration of different system configurations, e.g. the interconnection scheme between the neurons, the intracellular concentration of different chemical compounds (ions), which affect how action potentials are initiated and propagate.
Neuromorphic Cognitive Systems
Neuromorphic Cognitive Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuromorphic Cognitive Systems write by Qiang Yu. This book was released on 2017-05-03. Neuromorphic Cognitive Systems available in PDF, EPUB and Kindle. This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail.
Innovations and Advances in Cognitive Systems
Innovations and Advances in Cognitive Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Innovations and Advances in Cognitive Systems write by S. D. Prabu Ragavendiran. This book was released on . Innovations and Advances in Cognitive Systems available in PDF, EPUB and Kindle.
Neuromorphic Intelligence
Neuromorphic Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuromorphic Intelligence write by Shuangming Yang. This book was released on . Neuromorphic Intelligence available in PDF, EPUB and Kindle.
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Memristors for Neuromorphic Circuits and Artificial Intelligence Applications write by Jordi Suñé. This book was released on 2020-04-09. Memristors for Neuromorphic Circuits and Artificial Intelligence Applications available in PDF, EPUB and Kindle. 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.
