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Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces [article]

David G. Clark, Jesse A. Livezey, Edward F. Chang, Kristofer E. Bouchard
2018 arXiv   pre-print
Here, we develop methods for precise linear computations in spiking neural networks and use these methods to map the evolution of a linear dynamical system (LDS) onto an existing neuromorphic chip: IBM's  ...  Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware.  ...  Acknowledgments We would like to thank Ben Dichter for collecting the ECoG data as well as Rebecca Carney for her contributions to this project.  ... 
arXiv:1805.08889v2 fatcat:m2np7y6yozb7nkzxotlmiqzj6i

Programmable neuromorphic circuits for spike-based neural dynamics

Mostafa Rahimi Azghadi, Saber Moradi, Giacomo Indiveri
2013 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS)  
Hardware implementations of spiking neural networks offer promising solutions for a wide set of tasks, ranging from autonomous robotics to brain machine interfaces.  ...  We propose a set of programmable hybrid analog/digital neuromorphic circuits than can be used to build compact low-power neural processing systems.  ...  Abstract-Hardware implementations of spiking neural networks offer promising solutions for a wide set of tasks, ranging from autonomous robotics to brain machine interfaces.  ... 
doi:10.1109/newcas.2013.6573600 dblp:conf/newcas/AzghadiMI13 fatcat:yh6dxsid2vfufe5kii445olehq

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

Fabio Boi, Timoleon Moraitis, Vito De Feo, Francesco Diotalevi, Chiara Bartolozzi, Giacomo Indiveri, Alessandro Vato
2016 Frontiers in Neuroscience  
DISCUSSION In this paper, we showed the applicability of neuromorphic hardware in a brain-machine interface system, in the first demonstration of this kind.  ...  Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world.  ...  SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnins. 2016.00563/full#supplementary-material Conflict of Interest  ... 
doi:10.3389/fnins.2016.00563 pmid:28018162 pmcid:PMC5145890 fatcat:bdggh3hrhfecnj33uh5644kmbu

Low-Power Neuromorphic Hardware for Signal Processing Applications [article]

Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa,, Evangelos Eleftheriou
2019 arXiv   pre-print
Inspired by the time-encoding mechanism used by the brain, third generation spiking neural networks (SNNs) are being studied for building a new class of information processing engines.  ...  Most state-of-the-art machine learning solutions are based on memory-less models of neurons.  ...  In summary, we believe that there will be two stages of innovations for the field of low-power brain-inspired computing platforms.  ... 
arXiv:1901.03690v3 fatcat:34eavryprvdaxcvuteujwizeia

Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking [article]

Philippe Reiter, Geet Rose Jose, Spyridon Bizmpikis, Ionela-Ancuţa Cîrjilă
2020 arXiv   pre-print
This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices.  ...  Neuromorphic technologies based on Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements by modeling  ...  For explaining the biological analogies for SNNs, discussing the current research streams in the neuromorphic domain, and inspiring the Nengo CNN-to-SNN conversion experiment detailed in this paper, we  ... 
arXiv:2007.05606v1 fatcat:mw7nczubnzao3l73kyibxyvjpy

Neuromorphic Electronic Systems for Reservoir Computing [article]

Fatemeh Hadaeghi
2020 arXiv   pre-print
This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems.  ...  Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been  ...  RC on Mixed Digital/Analog Neuromorphic Systems The majority of analog neuromorphic systems designed to solve machine learning/signal processing problems tend to rely on digital components, for instance  ... 
arXiv:1908.09572v2 fatcat:cimkbnvyrjc3lhixlyufgmqy3i

Data and Power Efficient Intelligence with Neuromorphic Learning Machines

Emre O. Neftci
2018 iScience  
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on  ...  neuromorphic hardware for proactive learning of real-world data.  ...  Their low-power operation and potential scalability make them ideal candidates to overcome the memoryrelated challenges of neuromorphic hardware.  ... 
doi:10.1016/j.isci.2018.06.010 pmid:30240646 pmcid:PMC6123858 fatcat:zo4dvtgo75c7pn6n7tal24fkly

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

Frédéric D Broccard, Siddharth Joshi, Jun Wang, Gert Cauwenberghs
2017 Journal of Neural Engineering  
Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance.  ...  Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large  ...  Analog neuromorphic hardware systems are compact, have low power consumption and operate in real time independent of the model size and complexity.  ... 
doi:10.1088/1741-2552/aa67a9 pmid:28573983 fatcat:y732323nkbh7rcuy42so35cdnq

Guest Editorial Learning in Neuromorphic Systems and Cyborg Intelligence

Zhaohui Wu, Ryad Benosman, Huajin Tang, Shih-Chii Liu
2017 IEEE Transactions on Neural Networks and Learning Systems  
Compared with Attention Gated Reinforcement Learning (AGREL), its higher accuracy and more stable performance indicate its powerful ability for more sophisticated Brain Machine Interfaces (BMI) applications  ...  Cyborg intelligence aims to compensate for the weaknesses of both systems by combining the computational power of machines with the perceptive and cognitive abilities of biological systems.  ...  Benosman was awarded with the National Best French Scientific Paper by the Journal La Recherche for his work on neuromorphic retinas and their applications to retina stimulation and prosthetics in 2013  ... 
doi:10.1109/tnnls.2017.2650599 fatcat:ww34h4veofg6zky6w76mtlqkke

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain.  ...  Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically  ...  This new class of extremely low-power and lowlatency artificial intelligence systems could, In a world where power-hungry deep learning techniques are becoming a commodity, and at the same time, environmental  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Recent trends in neuromorphic engineering

Sumit Soman, jayadeva, Manan Suri
2016 Big Data Analytics  
There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits.  ...  Our survey indicates that neuromorphic engineering holds a promising future, particularly with growing data volumes, and the imminent need for intelligent, versatile computing.  ...  ., is a neuromorphic hardware co-processor for spiking neural networks on 180nm CMOS technology.  ... 
doi:10.1186/s41044-016-0013-1 fatcat:oyjitbviy5cdpesqwo6fp5tpgu

Adaptive Extreme Edge Computing for Wearable Devices

Erika Covi, Elisa Donati, Xiangpeng Liang, David Kappel, Hadi Heidari, Melika Payvand, Wei Wang
2021 Frontiers in Neuroscience  
To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected.  ...  Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy.  ...  Stefan Slesazeck for useful discussion on ferroelectric and memristive devices.  ... 
doi:10.3389/fnins.2021.611300 pmid:34045939 pmcid:PMC8144334 fatcat:5by77im5crcslgt7zj3wulzd5e

Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics

Travis DeWolf, Pawel Jaworski, Chris Eliasmith
2020 Frontiers in Neurorobotics  
In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware  ...  We identify four primary challenges in building robust, embedded neurorobotic systems, including: (1) developing infrastructure for interfacing with the environment and sensors; (2) processing task specific  ...  We would also like to thank Intel for access to the Nahuku board and Kapoho Bay used in the examples, and Mike Davies for reviewing an early draft of the paper.  ... 
doi:10.3389/fnbot.2020.568359 pmid:33162886 pmcid:PMC7581863 fatcat:xqzpfowst5d6recuzna4y7hozi

A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm

Paul Merolla, John Arthur, Filipp Akopyan, Nabil Imam, Rajit Manohar, Dharmendra S. Modha
2011 2011 IEEE Custom Integrated Circuits Conference (CICC)  
active power consumption of 45pJ/spike.  ...  The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption  ...  To remain one-to-one, the different orderings must not influence the spiking dynamics.  ... 
doi:10.1109/cicc.2011.6055294 dblp:conf/cicc/MerollaAAIMM11 fatcat:vmkaolj4qvgq3dpznuybqdsejy

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Lidong Wang, Cheryl Ann Alexander
2019 Neuroscience International  
graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces  ...  Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are introduced in this paper that specifically include brain-inspired computing, brain association  ...  Acknowledgement The authors would like to thank Technology and Healthcare Solutions, Mississippi, USA and the Institute for IT innovation and Smart Health, Mississippi, USA for support.  ... 
doi:10.3844/amjnsp.2019.13.21 fatcat:dtoa5moa2bhwdbiaxe6belwlf4
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