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Spatio-temporal Spike Pattern Classification in Neuromorphic Systems [chapter]

Sadique Sheik, Michael Pfeiffer, Fabio Stefanini, Giacomo Indiveri
2013 Lecture Notes in Computer Science  
In this paper we evaluate the computational approaches that have been proposed for classifying spatio-temporal sequences of spike-trains, derive the main principles and the key components that are required  ...  Such systems typically comprise event-based sensors and multi-neuron chips that encode, transmit, and process signals using spikes.  ...  Explicit delay circuits can be implemented in multi-compartmental neuron models to carry out spatio-temporal processing.  ... 
doi:10.1007/978-3-642-39802-5_23 fatcat:p2q6das46reefacqzdj7qp6ay4

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  
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.  ...  Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its  ...  In both of these studies, CPG computational models emulated on neuromorphic hardware were coupled with CPGs from isolated spinal cord kept alive ex vivo. Lamprey spinal cord.  ... 
doi:10.1088/1741-2552/aa67a9 pmid:28573983 fatcat:y732323nkbh7rcuy42so35cdnq

Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review

Cheng Wang, Amogh Agrawal, Eunseon Yu, Kaushik Roy
2021 Frontiers in Neuroscience  
neuromorphic functionalities.  ...  In light of the non-destructive nature and the relatively simple physical process of multi-domain switching, we envision that ferroic-based multi-state devices provide an alternative pathway toward energy  ...  FIGURE 8 | 8 (A) Crossbar implementation of multi-level FeFET cells for neuromorphic computing.  ... 
doi:10.3389/fnins.2021.661667 pmid:33994935 pmcid:PMC8115403 fatcat:53iad2znyffszeu2oes2fow4wm

Regenerative memory in time-delayed neuromorphic photonic systems [article]

B. Romeira, R. Avó, José M. L. Figueiredo, S. Barland, J. Javaloyes
2015 arXiv   pre-print
FitzHugh-Nagumo model with delayed feedback.  ...  We investigate a regenerative memory based upon a time-delayed neuromorphic photonic oscillator and discuss the link with temporal localized structures.  ...  neuron with a delayed coupling representing an autaptic connection as seen in Fig. 1a) .  ... 
arXiv:1503.07781v1 fatcat:ppepvdvm25ettdfcmpmfoysx2q

Neuromorphic photonic networks using silicon photonic weight banks

Alexander N. Tait, Thomas Ferreira de Lima, Ellen Zhou, Allie X. Wu, Mitchell A. Nahmias, Bhavin J. Shastri, Paul R. Prucnal
2017 Scientific Reports  
At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.  ...  We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks.  ...  Reservoirs can thus derive complexity from physical processes that are difficult to model or reproduce, such as coupled amplifiers 66 , coupled nonlinear MRRs 67 , time-delayed dynamics in fibers 64  ... 
doi:10.1038/s41598-017-07754-z pmid:28784997 pmcid:PMC5547135 fatcat:5g5gfnspcfespculrsdwbizdfi

Photonic neuromorphic technologies in optical communications

Apostolos Argyris
2022 Nanophotonics  
Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications.  ...  In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications.  ...  Photonic neuromorphic computing in optical communications It was only the last decade when the first designs and implementations in photonic or optoelectronic reservoir computing were proposed.  ... 
doi:10.1515/nanoph-2021-0578 fatcat:dykdgrpzabbl5hocs6xdzvwgzu

Closed-loop control of a modular neuromorphic biohybrid [article]

Hanna Keren, Johannes Partzsch, Shimon Marom, Christian Mayr
2018 arXiv   pre-print
Using a closed-loop control to monitor the activity of the coupled hybrid, we show that both modules are congruently modified, in the macroscopic as well as the microscopic activity levels.  ...  Overall, this strategy provides an experimental access to the controllability of neural activity irregularities, when embedded in a modular organization.  ...  So far, the neuromorphic approach has been successful in implementations of sensory functions (e.g. visual processing [20] ) and computational functions that rely on building blocks of brain processing  ... 
arXiv:1802.07905v1 fatcat:m7luirtks5gkhj444s7jpga6e4

Regenerative memory in time-delayed neuromorphic photonic resonators

B. Romeira, R. Avó, José M. L. Figueiredo, S. Barland, J. Javaloyes
2016 Scientific Reports  
We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection.  ...  We link our experimental implementation, based upon a nanoscale nonlinear resonant tunneling diode driving a laser, to the paradigm of neuronal activity, the FitzHugh-Nagumo model with delayed feedback  ...  Acknowledgements We would like to thank Gary Ternent, University of Glasgow, UK, for the fabrication of the RTD devices employed in this work, Charles Ironside, Curtin University, Perth, Western Australia  ... 
doi:10.1038/srep19510 pmid:26781583 pmcid:PMC4726037 fatcat:agqpizndk5bsddbxwfpsagf4aq

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers [article]

Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, Gangotree Chakma
2017 arXiv   pre-print
In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC)  ...  layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.  ...  And for the programmable delay chain, we collected energy data for each spike existing in the delay chain.  ... 
arXiv:1703.05364v2 fatcat:s7ltbnkktnd7jovpjgpkfdozkm

Neuromorphic sensory systems

Shih-Chii Liu, Tobi Delbruck
2010 Current Opinion in Neurobiology  
Designers in neuromorphic engineering aim to construct electronic systems with the same efficient style of computation.  ...  By emulating the neuronal organization and function of nervous systems in electronic devices, neuromorphic engineers hope to harness the brain's efficient and powerful style of physical computation for  ...  Baker; and the NSF Telluride Neuromorphic Cognitive Engineering Workshop.  ... 
doi:10.1016/j.conb.2010.03.007 pmid:20493680 fatcat:zgtlwtysj5e25pp5ejrsctd4tu

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Hanna Keren, Johannes Partzsch, Shimon Marom, Christian G. Mayr
2019 Frontiers in Neuroscience  
Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.  ...  This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time.  ...  DISCUSSION We present a setup for coupling a biological neural network with a neuromorphic hardware network.  ... 
doi:10.3389/fnins.2019.00432 pmid:31133779 pmcid:PMC6517490 fatcat:klgtpn4gtjgh7imlan2xtkxwja

Critical neuromorphic computing based on explosive synchronization

Jaesung Choi, Pilwon Kim
2019 Chaos  
This work provides a systematic way to encode computing in a large size coupled oscillators, which may be useful in designing neuromorphic devices.  ...  In this work, we present a neuromorphic computing algorithm based on oscillator synchronization in a critical regime.  ...  The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.1063/1.5086902 fatcat:ix57czorbzbcrbj2ymugycypaq

Photonic spike processing: ultrafast laser neurons and an integrated photonic network [article]

Bhavin J. Shastri, Alexander N. Tait, Mitchell A. Nahmias, Paul R. Prucnal
2014 arXiv   pre-print
Whereas neuromorphic engineering exploits the biophysics of neuronal computation algorithms to provide a wide range of computing and signal processing applications, photonics offer an alternative approach  ...  The marriage of two vibrant fields---photonics and neuromorphic processing---is fundamentally enabled by the strong analogies within the underlying physics between the dynamics of biological neurons and  ...  The LIF model with a synaptic variable, coupled with tunable routing in a passive SOI network on a scalable platform, could open computational domains that demand unprecedented temporal precision, power  ... 
arXiv:1407.2917v1 fatcat:ybesvgvrizbi7jbvy642diteja

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

I.-A. Lungu, A. Riehle, M. P. Nawrot, M. Schmuker
2017 IBM Journal of Research and Development  
The network runs on neuromorphic hardware and performs its computations in a purely spike-based fashion. It incorporates an insect-brain-inspired, three-layer architecture with 176 neurons.  ...  Our results provide a proof of concept for the first-time use of a neuromorphic device for decoding movement intentions.  ...  Riehle is Research Director at the CNRS and studies higher cortical processes involved in movement preparation and execution and visuomotor integration by using massively parallel multi-electrode recording  ... 
doi:10.1147/jrd.2017.2656063 fatcat:v5hqeqgg75hz5ebfzy34eynszy

Emulating Dendritic Computing Paradigms on Analog Neuromorphic Hardware

Jakob Kaiser, Sebastian Billaudelle, Eric Muller, Christian Tetzlaff, Johannes Schemmel, Sebastian Schmitt
2021 Neuroscience  
BrainScaleS-2 is an accelerated and highly configurable neuromorphic system with physical models of neurons and synapses.  ...  In this paper, three multi-compartment neuron morphologies are chosen to demonstrate passive propagation of postsynaptic potentials, spatio-temporal coincidence detection of synaptic inputs in a dendritic  ...  is similar to multi-layered networks of point neurons and illustrates the computational power of multi-compartment neurons.  ... 
doi:10.1016/j.neuroscience.2021.08.013 pmid:34428499 fatcat:n2ipcdco2jgovbc2omxip2cage
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