Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware [PDF]
Frontiers in Neuroscience, 2023 With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value.
Srijanie Dey, Alexander G. Dimitrov
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Reservoir based spiking models for univariate Time Series Classification [PDF]
Frontiers in Computational Neuroscience, 2023 A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficient—they run mainly on the power hungry CPUs and GPUs ...
Ramashish Gaurav +2 more
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Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [PDF]
Frontiers in Neuroinformatics, 2022 Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of ...
Srijanie Dey, Alexander Dimitrov
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Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian [PDF]
Frontiers in Neuroinformatics, 2022 Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks.
Carlo Michaelis +7 more
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Mineralogy of iron microbial mats from Loihi Seamount [PDF]
Frontiers in Microbiology, 2012 Extensive mats of Fe oxyhydroxides and associated Fe-oxidizing microbial organisms form in diverse geochemical settings – freshwater seeps to deep-sea vents – where ever opposing Fe(II)-oxygen gradients prevail.
Brandy Marie Toner +7 more
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Neurorobotic reinforcement learning for domains with parametrical uncertainty [PDF]
Frontiers in Neurorobotics, 2023 Neuromorphic hardware paired with brain-inspired learning strategies have enormous potential for robot control. Explicitly, these advantages include low energy consumption, low latency, and adaptability.
Lana Amaya, Axel von Arnim
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Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware [PDF]
Frontiers in Neuroscience, 2022 Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional ...
Simon F. Müller-Cleve +13 more
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A deterministic neuromorphic architecture with scalable time synchronization [PDF]
Nature CommunicationsCustom integrated circuits modeling biological neural networks serve as tools for studying brain computation and platforms for exploring new architectures and learning rules of artificial neural networks.
Congyang Li, Nabil Imam, Rajit Manohar
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Spiking Neural Networks for Structural Health Monitoring [PDF]
Sensors, 2022 This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field.
George Vathakkattil Joseph +1 more
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The backpropagation algorithm implemented on spiking neuromorphic hardware [PDF]
Nature CommunicationsThe capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
Alpha Renner +4 more
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