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A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines [article]

Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone
2017 arXiv   pre-print
In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency  ...  Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without  ...  We also examine liquid state machines (LSMs) [11] to show how the constructs available in the STPU facilitate complex dynamical neuronal systems.  ... 
arXiv:1704.08306v1 fatcat:mvg6m7g5vnddtftd24kyawo56m

Protein Structured Reservoir computing for Spike-based Pattern Recognition [article]

Karolos-Alexandros Tsakalos, Georgios Ch. Sirakoulis, Andrew Adamatzky, Jim Smith
2020 arXiv   pre-print
We apply on a single readout layer various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning  ...  To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly  ...  ) pattern recognition training algorithm in a supervised fashion on a single output layer of spiking neurons, which is found mainly in echo state networks and not in liquid state machines we use here,  ... 
arXiv:2008.03330v1 fatcat:fjzlh7ta65gvdpyr44xz7mik3a

Liquid State Machine on SpiNNaker for Spatio-Temporal Classification Tasks

Alberto Patiño-Saucedo, Horacio Rostro-González, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
2022 Frontiers in Neuroscience  
Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures  ...  However, it has been difficult to optimize LSMs for solving complex tasks such as event-based computer vision and few implementations in large-scale neuromorphic processors have been attempted.  ...  ACKNOWLEDGMENTS We are grateful to the Advanced Processor Technologies (APT) Research Group at University of Manchester for enabling access to SpiNNaker boards and support with related software.  ... 
doi:10.3389/fnins.2022.819063 pmid:35360182 pmcid:PMC8964061 fatcat:zx74emjq2fawzghjrqib4g4tg4

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Haifeng Ling, Dimitrios A. Koutsouras, Setareh Kazemzadeh, Yoeri van de Burgt, Feng Yan, Paschalis Gkoupidenis
2020 Applied Physics Reviews  
This endows synaptic circuits with concurrent actualization of inference and learning, hence facilitating the implementation of more complex neuromorphic functions. 57 Besides, the write current (i.e  ...  NEUROMORPHIC DEVICES AND FUNCTIONS The biologically inspired neuromorphic systems are expected to be capable of dealing with complex and intelligent tasks, where neuromorphic functionalities need to be  ... 
doi:10.1063/1.5122249 fatcat:toitkrqdgbg6boldbcveiqqfvu

Teaching a neural network with non-tunable exciton-polariton nodes [article]

Andrzej Opala, Riccardo Panico, Vincenzo Ardizzone, Barbara Pietka, Jacek Szczytko, Daniele Sanvitto, Michał Matuszewski, Dario Ballarini
2021 arXiv   pre-print
While such networks promise great improvements in terms of speed and energy efficiency, their performance is limited by the difficulty to apply efficient teaching.  ...  We propose a system of non-tunable exciton-polariton nodes and an efficient teaching method that relies on the precise measurement of the nonlinear node response and the subsequent use of the backpropagation  ...  Applying a voltage to the cells changes the orientation of the liquid crystals and in turn the effective refractive index seen by the incident light.  ... 
arXiv:2107.11156v1 fatcat:lri3dys47rdntor26ohn5c4jnq

Roadmap on emerging hardware and technology for machine learning

Qiangfei Xia, Karl K Berggren, Konstantin Likharev, Dmitri B Strukov, Hao Jiang, Thomas Mikolajick, Damien Querlioz, Martin Salinga, John Erickson, Shuang Pi, Feng Xiong, Peng Lin (+31 others)
2020 Nanotechnology  
Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic  ...  A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency.  ...  Acknowledgments This is a contribution of NIST, an agency of the US government, not subject to copyright.  ... 
doi:10.1088/1361-6528/aba70f pmid:32679577 fatcat:t6me4pfxgfhdvbdqjnyjuksf2e

Integration of nanoscale memristor synapses in neuromorphic computing architectures

Giacomo Indiveri, Bernabé Linares-Barranco, Robert Legenstein, George Deligeorgis, Themistoklis Prodromakis
2013 Nanotechnology  
element and the low energy required to write distinct states.  ...  As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient  ...  Acknowledgment This work was supported by the European CHIST-ERA program, via the "Plasticity in NEUral Memristive Architectures" (PNEUMA) project.  ... 
doi:10.1088/0957-4484/24/38/384010 pmid:23999381 fatcat:6isdvp5f4vhgddskpmlx2xq5ra

Six Networks on a Universal Neuromorphic Computing Substrate

Thomas Pfeil, Andreas Grübl, Sebastian Jeltsch, Eric Müller, Paul Müller, Mihai A. Petrovici, Michael Schmuker, Daniel Brüderle, Johannes Schemmel, Karlheinz Meier
2013 Frontiers in Neuroscience  
As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality.  ...  At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials.  ...  Petrovici); antennal lobe model (Michael Schmuker); liquid state machine (Sebastian Jeltsch). All authors contributed to writing this paper. S., and Aertsen, A. (2010b).  ... 
doi:10.3389/fnins.2013.00011 pmid:23423583 pmcid:PMC3575075 fatcat:yqkre36bzfce5eettvxhu3pytm

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
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic  ...  built-in capabilities to learn or deal with complex data as our brain does.  ...  Concluding Remarks Integrating event-based vision sensing and processing with neuromorphic computation techniques is expected to yield solutions that will be able to penetrate the artificial vision market  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Spiking Neural Networks Hardware Implementations and Challenges

Maxence Bouvier, Alexandre Valentian, Thomas Mesquida, Francois Rummens, Marina Reyboz, Elisa Vianello, Edith Beigne
2019 ACM Journal on Emerging Technologies in Computing Systems  
As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms.  ...  Neuromorphic computing is henceforth a major research field for both academic and industrial actors.  ...  Finally, a specific type of spiking networks, called liquid state machines (LSMs) or reservoir networks, fully takes its inspiration from the brain.  ... 
doi:10.1145/3304103 fatcat:p3frra3osnhybj4hkor4y5cyqm

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  
This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the  ...  Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach.  ...  (SRC), through Extremely Efficient Collective Electronics (EXCEL), an SRC-NRI Nanoelectronics Research Initiative under Research Task ID 2698.002.  ... 
doi:10.1088/1741-2552/aa67a9 pmid:28573983 fatcat:y732323nkbh7rcuy42so35cdnq

Competing memristors for brain-inspired computing

Seung Ju Kim, Sang Bum Kim, Ho Won Jang
2020 iScience  
To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed.  ...  A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing.  ...  Since Turing pioneered artificial intelligence (AI), researchers have attempted to invent machines that can ''learn'' at a similar level as humans so that they can deal with the complex problems they faced  ... 
doi:10.1016/j.isci.2020.101889 pmid:33458606 pmcid:PMC7797931 fatcat:jswaukrdljh63cmycx6leplzma

Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization [article]

Wenrui Zhang, Peng Li
2021 arXiv   pre-print
In neural circuits, recurrent connectivity plays a crucial role in network function and stability.  ...  We aim to enable systemic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization.  ...  Only recently, [29] adopted a simulated annealing algorithm to learn the optimal hyperparameters of liquid state machine (LSM) models through a three-step search.  ... 
arXiv:2108.01793v1 fatcat:ynp773e54vf4pjlkw222o4ju4m

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt, Chris Van Hoof
2018 Neural Networks  
The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine  ...  computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states),  ...  Responsive Technologies aNd Engagement togetheR).  ... 
doi:10.1016/j.neunet.2017.12.015 pmid:29414535 fatcat:3y3dvm5ezvg77ij2g3ukes4vpa

Mapping Spiking Neural Networks to Neuromorphic Hardware [article]

Adarsha Balaji, Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Giacomo Indiveri, Jeffrey L. Krichmar, Nikil Dutt, Siebren Schaafsma, Francky Catthoor
2019 arXiv   pre-print
Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner.  ...  We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption.  ...  For other architectures such as the Liquid State Machine (LSM) [27] , ISI of critical neurons contribute to the accuracy.  ... 
arXiv:1909.01843v1 fatcat:w4kpthbfcvbjpiw7oue7bxcpyq
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