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Edwards, R. E.; Cauwenberghs, Gert

doi:10.1023/a:1008373826094

Cited by 19 publications

(3 citation statements)

References 13 publications

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“…It has been fully characterized in Drakakis et al (1997), and has been used to implement Hodgkin–Huxley VLSI models of neurons (Toumazou et al, 1998). A similar log-domain circuit is shown in Figure 1A: This circuit, called the “Tau-Cell,” was first proposed in Edwards and Cauwenberghs (2000) as a BiCMOS log-domain filter; it was fully characterized in van Schaik and Jin (2003) as a sub-threshold log-domain circuit, and used in Yu and Cauwenberghs (2010b) to implement conductance-based synapses. This circuit is used also in the tau-cell neuron, described in Section 4.2.…”

Section: Silicon Neuron Circuit Blocksmentioning

confidence: 99%

Neuromorphic Silicon Neuron Circuits

Indiveri¹,

Linares-Barranco²,

Hamilton³

et al. 2011

Front. Neurosci

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1,140

835

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Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

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Section: Silicon Neuron Circuit Blocksmentioning

confidence: 99%

Neuromorphic Silicon Neuron Circuits

Indiveri¹,

Linares-Barranco²,

Hamilton³

et al. 2011

Front. Neurosci

Self Cite

1,140

835

View full text Add to dashboard Cite

show abstract

“…By construction, the types of computational primitives and signal processing operations performed by mixed-signal neuromorphic circuits in hardware networks of spiking neurons are the same ones that can be observed in animal nervous systems. The neuromorphic circuits in question can carry out different types of linear, non-linear, or adaptive filtering operations [96][97][98], they can implement different types of normalizing operations on different temporal and spatial scales [99,100], they can implement delay chains [101], oscillators [35,102], resonators [103], decision-making networks [95], and importantly, they can be designed to implement SNNs with on-chip and on-line spike-based learning properties to solve classification or regression tasks [89][90][91]104].…”

Section: Learningmentioning

confidence: 99%

Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications

Donati

Indiveri

2023

Prog. Biomed. Eng.

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Bioelectronic medicine treats chronic diseases by sensing, processing, and modulating the electronic signals produced in the nervous system of the human body, labeled 'neural signals'. While electronic circuits have been used for several years in this domain, the progress in microelectronic technology is now allowing increasingly accurate and targeted solutions for therapeutic benefits. For example, it is now becoming possible to modulate signals in specific nerve fibers, hence targeting specific diseases. However, to fully exploit this approach it is crucial to understand what aspects of the nerve signals are important, what is the effect of the stimulation, and what circuit designs can best achieve the desired result. Neuromorphic electronic circuits represent a promising design style for achieving this goal: their ultra-low power characteristics and biologically plausible time constants make them the ideal candidate for building optimal interfaces to real neural processing systems, enabling real-time closed-loop interactions with the biological tissue. In this paper, we highlight the main features of neuromorphic circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems. We present examples of neural computational primitives that can be implemented for carrying out computation on the signals sensed in these closed-loop systems and discuss the way to use their outputs for neuro-stimulation. We describe examples of applications that follow this approach, highlight open challenges that need to be addressed, and propose actions required to overcome current limitations.

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“…Spike-based models of neurons based on silicon technology have recently become popular in the academic literature, and a good overview of these models is given in [17]. The most prominent models include a "Tau-Cell neuron" model, based on the first-order low-pass filter [18], an Axon-Hillock circuit [19], and an Izhikevich neuron circuit [20], which implement a variety of spiking behaviors, such as regular spiking, spike-frequency adaptation, and bursting. Therefore, the development of neurodynamic models in circuit design is an important task of modern neuroelectronics, in particular, in the field of brain-machine interface.…”

Section: Introductionmentioning

confidence: 99%

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

Борисков

Velichko

2019

Electronics

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In this paper, we present circuit solutions based on a switch element with the S-type I–V characteristic implemented using the classic FitzHugh–Nagumo and FitzHugh–Rinzel models. Using the proposed simplified electrical circuits allows the modeling of the integrate-and-fire neuron and burst oscillation modes with the emulation of the mammalian cold receptor patterns. The circuits were studied using the experimental I–V characteristic of an NbO2 switch with a stable section of negative differential resistance (NDR) and a VO2 switch with an unstable NDR, considering the temperature dependences of the threshold characteristics. The results are relevant for modern neuroelectronics and have practical significance for the introduction of the neurodynamic models in circuit design and the brain–machine interface. The proposed systems of differential equations with the piecewise linear approximation of the S-type I–V characteristic may be of scientific interest for further analytical and numerical research and development of neural networks with artificial intelligence.

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Cited by 19 publications

References 13 publications

Neuromorphic Silicon Neuron Circuits

Neuromorphic Silicon Neuron Circuits

Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications

Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators

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