Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware

Hendy, Hagar; Merkel, Cory

doi:10.1117/1.jei.31.1.010901

Cited by 12 publications

(5 citation statements)

References 122 publications

(143 reference statements)

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“…Therefore, it underlines the need to develop hardware (e.g. neuromorphic chips [86], FPGAs, GPUs) or software (e.g. NEURON, NEST, Brian, GeNN) capable of running large scale networks in real time.…”

Section: Bioinspired Embodiment 321 Optimize Models Through Embodimentmentioning

confidence: 99%

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

Tolu

Strohmer²,

Zahra³

2023

Neuromorph. Comput. Eng.

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Neurorobotics has emerged from the alliance between Neuroscience and Robotics. It pursues the investigation of reproducing living organism-like behaviours in robots by means of the embodiment of computational models of the central nervous system. This perspective article discusses the current trend of implementing tools for the pressing challenge of early diagnosis of neurodegenerative diseases and how neurorobotics approaches can help. Recently, advances in this field have allowed the testing of some neuroscientific hypotheses related to brain diseases, but the lack of biological plausibility of developed brain models and musculoskeletal systems has limited the understanding of the underlying brain mechanisms that lead to deficits in motor and cognitive tasks.Key aspects and methods to enhance the reproducibility of natural behaviours observed in healthy and impaired brains are proposed in this perspective. In the long term, the goal is to move beyond finding therapies and look into how researchers can use neurorobotics to reduce testing on humans as well as find root causes for disease.

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Section: Bioinspired Embodiment 321 Optimize Models Through Embodimentmentioning

confidence: 99%

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

Tolu

Strohmer²,

Zahra³

2023

Neuromorph. Comput. Eng.

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“…Furthermore, use of SNNs unlocks new possibilities in terms of available learning rules, including biologically inspired approaches such as global-local learning and spike-timing-dependent plasticity [13,44]. Finally, these networks provide a perfect match for neuromorphic hardware, such as brain-inspired event-based vision sensory systems [45].…”

Section: Applications Of Snnsmentioning

confidence: 99%

Brain-inspired nanophotonic spike computing: challenges and prospects

Romeira

Adão

Nieder

et al. 2023

Neuromorph. Comput. Eng.

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Nanophotonic spiking neural networks based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs). We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes (nanoLEDs), nanolaser diodes (nanoLDs), and nanophotodetectors (nanoPDs) to realize neuron emitter and receiver spiking nodes. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant functional AI tasks, such as image pattern recognition, edge detection, and spiking neural networks for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications.

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“…Whenever the brightness change of one pixel exceeds a preset threshold, event camera outputs an event in address event representation (AER) data format, which consists of pixel position, timestamp, and polarity of the event 2 . Therefore, an event camera outputs a large number of asynchronous events 3 called event stream when it records moving objects. Event cameras have the advantages of low power consumption, low latency, high dynamic range, and high temporal resolution, which allows them to adapt to fast motions or difficult lighting scenes such as high dynamic range or low light scenes 4 .…”

Section: Introductionmentioning

confidence: 99%

Event camera object recognition using spatiotemporal event time surface and reward-modulated spike-timing-dependent plasticity learning rule

Zhou,

Zhang

2024

J. Electron. Imag.

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Event cameras record moving objects with asynchronous event streams. It remains a challenge to make full use of the spatiotemporal information of event streams to extract high-quality features and to make event camera object recognition. We propose an event-based event camera object recognition system, which includes a denoising module and an object recognition module. The denoising module removes noise events using spatiotemporal information of the events. The object recognition module extracts primary spatiotemporal features based on the event time surface prototypes obtained by clustering, then further extracts complex diagnostic features, and makes object recognition using spiking neural networks with reward-modulated spike-timing-dependent plasticity learning rule. Experimental results demonstrate that our system has better performance than baseline methods on five popular event camera datasets, especially on datasets with rich spatiotemporal dynamics. Our denoising method improves the noise robustness of our event camera object recognition system on high-speed moving datasets that are greatly affected by noises. Moreover, our method has much better recognition ability than baseline methods when using short input event streams. Our method is very beneficial for developing event-based event camera object recognition algorithm when event streams are short or noises are serious.

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Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware

Cited by 12 publications

References 122 publications

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

Brain-inspired nanophotonic spike computing: challenges and prospects

Event camera object recognition using spatiotemporal event time surface and reward-modulated spike-timing-dependent plasticity learning rule

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