Neuromorphic cochlea implants

Lande, Tor Sverre; Marienborg, J.-T.; Berg, Y.

doi:10.1109/iscas.2000.858773

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Furthermore, Lande et al proposed a biologically inspired neuromorphic cochlear implant that incorporates spike-based signal processing [184]. This implant utilizes a single-chip micropower CMOS and digital control through neuromorphic coding and redundancy, allowing for scalability to a large number of channels.…”

Section: Hearingmentioning

confidence: 99%

Neuromorphic applications in medicine

et al. 2023

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In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

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Section: Hearingmentioning

confidence: 99%

Neuromorphic applications in medicine

et al. 2023

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“…A neuromorphic analog imager is presented in [68] where image processing is carried out in the analog domain mimicking processes observed in rabbit's retinas. Additional types of neuromorphic sensor systems have been considered, e.g., neuromorphic cochleas [69,70].…”

Section: Architecture-level Realizationsmentioning

confidence: 99%

Neuromorphic microelectronics from devices to hardware systems and applications

Schmid

2016

NOLTA

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Neuromorphic systems aiming at mimicking some characteristics of the nervous systems of living humans or animals have been developed since the late 1980s', taking benefit of intrinsic properties and increasing performances of the successive silicon fabrication technologies. A regain of interest has been observed in the middle of the 2010s', which manifests itself from the emergence of large-scale projects integrating various computational and hardware perspectives, by the increased interest and involvement of industry and the growth of the volume of scientific publications. This paper reviews research directions and methods of neuromorphic microelectronics hardware, the developed hardware and its performance, and discusses current issues and potential future developments.

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“…One interesting example of modelling neural systems is Tobi Delbruck's "Physiologist's Friend" chip [136], a model of a visual cortical neuron with retinal sensors which can model the receptive field of a visual cortical neuron well enough to be used instead of a live animal for training psychology or physiology students. In addition, spiking silicon neurons are one of the underlying technologies that may permit effective sensory implants [137], both auditory [138], and visual [139]. These prosthetic application may prove to be an important growth area for this type of technology where small size and ultra-low power consumption are critical.…”

Section: Applications Of Hardware Spiking Neuronsmentioning

confidence: 99%

Implementing Neural Models in Silicon

Smith

Handbook of Nature-Inspired and Innovative Computing

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Neural models are used in both computational neuroscience and in pattern recognition. The aim of the first is understanding of real neural systems, and of the second is gaining better, possibly brain-like performance for systems being built. In both cases, the highly parallel nature of the neural system contrasts with the sequential nature of computer systems, resulting in slow and complex simulation software. More direct implementation in hardware (whether digital or analogue) holds out the promise of faster emulation both because hardware implementation is inherently faster than software, and because the operation is much more parallel. There are costs to this: modifying the system (for example to test out variants of the system) is much harder when a full application specific integrated circuit has been built. Fast emulation can permit direct incorporation of a neural model into a system, permitting realtime input and output. Appropriate selection of implementation technology can help to make interfacing the system to external devices simpler. We review the technologies involved, and discuss some example systems. Why implement neural models in silicon?There are two primary reasons for implementing neural models: one is to attempt to gain better, and possibly brain-like performance for some system, and the other is to study how some particular neural model performs. Current computer systems do not approach brain-like system performance in many areas (sensing, motor control, and pattern recognition, for example, to say nothing of the higher level capabilities of mammalian brains). There has been considerable research into how the neural system attains its capabilities. Implementing neural systems in silicon can permit direct applications of this research by permitting neural models to run rapidly enough to be applied directly to data. It is true that increases in workstation performance have allowed some software implementations of neural models to run in real time, but the highly parallel nature of neural systems, coupled with increasing interest in the application of more sophisticated (and computationally more expensive) neural models has caused interest in more direct implementation to be maintained. Interest in applying neural models to sensory and sensory-motor systems has made attaining real-time performance a critical factor. Real sensory systems are highly parallel, with multiple parallel channels of information, so that even though each channel might be implementable in real-time in software, implementing multiple channels implies hardware implementation.The study of how particular models of neural systems perform is one aspect of computational neuroscience. Such studies are usually carried out in software, as this allows easy alteration of and experimentation with systems. However, models of the highly parallel architecture of neural systems run slowly on standard 1

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Neuromorphic cochlea implants

Cited by 8 publications

References 2 publications

Neuromorphic applications in medicine

Neuromorphic applications in medicine

Neuromorphic microelectronics from devices to hardware systems and applications

Implementing Neural Models in Silicon

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