A Five-Decade Dynamic-Range Ambient-Light-Independent Calibrated Signed-Spatial-Contrast AER Retina With 0.1-ms Latency and Optional Time-to-First-Spike Mode

Leñero‐Bardallo, Juan A.; Serrano-Gotarredona, Teresa; Linares-Barranco, B.

doi:10.1109/tcsi.2010.2046971

Cited by 42 publications

(26 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a large collection of spike-driven vision sensors have been reported, such as sensors for luminance (Culurciello et al, 2003; Chen et al, 2011), temporal contrast (Barbaro et al, 2002; Mallik et al, 2005; Chan et al, 2007a; Lichtsteiner et al, 2008; Leñero-Bardallo et al, 2011; Posch et al, 2011; Serrano-Gotarredona and Linares-Barranco, 2013), motion (Kramer, 1996; Sarpeshkar et al, 1996; Ozalevli and Higgins, 2005), and spatial contrast (Ruedi et al, 2003; Zaghloul and Boahen, 2004; Costas-Santos et al, 2007; Massari et al, 2008; Leñero-Bardallo et al, 2010). Spike-driven principles have also been used for auditory systems (Sarpeshkar et al, 2005; Wen and Boahen, 2006, 2009; Chan et al, 2007b), competition and Winner-Take-All networks (Indiveri, 2000; Chicca et al, 2007; Oster et al, 2008), learning (Mill et al, 2011), classification (Mitra et al, 2009), fall detection (Fu et al, 2008), and systems distributed over wireless sensor networks (Teixeira et al, 2005; Massari et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

STDP and STDP variations with memristors for spiking neuromorphic learning systems

Serrano-Gotarredona¹,

Masquelier²,

Prodromakis³

et al. 2013

Front. Neurosci.

408

262

View full text Add to dashboard Cite

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.

show abstract

Section: Introductionmentioning

confidence: 99%

STDP and STDP variations with memristors for spiking neuromorphic learning systems

Serrano-Gotarredona¹,

Masquelier²,

Prodromakis³

et al. 2013

Front. Neurosci.

408

262

View full text Add to dashboard Cite

show abstract

“…If we compare their performance with the human retina, we can state that they perform worse in the majority of uncontrolled situations and environments: their dynamic range is much lower, their sensitivity to light is also lower, and their power consumption is much higher. For these reasons, some authors considered the idea of developing bio-inspired vision sensors (also called retinas, (Mahowald 1994, Leñero-Bardallo et al 2010, Leñero-Bardallo et al 2011. These sensors try to mimic the interactions of the cells of the human retina.…”

Section: Bio-inspired Event-based Vision Sensorsmentioning

confidence: 99%

Fire detection with a frame-less vision sensor working in the NIR band

Leñero‐Bardallo¹,

Fernández-Berni²,

Rodríguez-Vázquez³

et al. 2014

Advances in Forest Fire Research

View full text Add to dashboard Cite

This paper draws the attention of the community about the capabilities of an emerging generation of bio-inspired vision sensors to be used in fire detection systems. Their principle of operation will be described. Moreover experimental results showing the performance of an eventbased vision sensor will be provided. The sensor was intended to monitor flames activity without using optic filters. In this article, we will also extend this preliminary work and explore how its outputs can be processed to detect fire in the environment. IntroductionInfrared cameras can easily detect fire and hot spots. Unfortunately, they are expensive, difficult to handle, and fragile (Briz et al. 2003). For this reason, CMOS cameras are still preferred for some applications where it is not strictly necessary operating in the whole infrared band, (Cheon et al. 2009, Naoult et al. 2007, Bendiscio et al. 1998, Fernandez-Berni et al. 2012. Silicon can detect Near Infrared Radiation (NIR) within the band [700 1,100] nm. This property can be exploited to monitor flame and fire activity.Flames have a very characteristic oscillatory behavior. They flicker with certain frequency components that depend on their nature. The study of these frequency components is interesting for industrial processes and applications. Traditionally CMOS cameras with NIR filters have been employed (Yan et al. 2006). As an alternative to this traditional approach, recently we proposed an bio-inspired event-based system to monitor flame activity without using optic filters (Leñero-Bardallo et al. 2013). The idea behind this method was to compare the photocurrents of stacked photodiodes at different depths. The top one was more sensitive to shorter wavelengths and the bottom one was more sensitive to longer wavelengths, having a very good sensitivity within the NIR band. Just comparing the difference between their photocurrents, we were able to determine if the incident radiation was within the NIR band without optic filters. Moreover, we could process with a real-time algorithm the temporal variations of the NIR levels in the visual scene provoked by flames and compute their frequency components values. This paper studies how the prior work to monitor flame activity could be extended to detect fire in the environment and emit an alarm in case. We will show that the previously implemented algorithm outputs can be processed to detect fire with a low computational load. Up

show abstract

“…In humans, object recognition can be performed as quickly as in about 150 ms [39], giving time to each neuron in the ventral stream hierarchy to fire just one spike [39], revealing a highly efficient timing-domain signal encoding in the brain. Based on these observations, neuromorphic researchers worldwide have developed in the last 10 years a collection of ED sensor [40][41][42][43][44][45][46][47][48] and processor [49][50][51][52] chips. For example, Fig.…”

Section: Spiking Neural Network For Event-driven Sensing and Processingmentioning

confidence: 99%

Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systems

Serrano-Gotarredona

Linares-Barranco

2013

Memristors and Memristive Systems

Self Cite

View full text Add to dashboard Cite

A Five-Decade Dynamic-Range Ambient-Light-Independent Calibrated Signed-Spatial-Contrast AER Retina With 0.1-ms Latency and Optional Time-to-First-Spike Mode

Cited by 42 publications

References 25 publications

STDP and STDP variations with memristors for spiking neuromorphic learning systems

STDP and STDP variations with memristors for spiking neuromorphic learning systems

Fire detection with a frame-less vision sensor working in the NIR band

Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systems

Contact Info

Product

Resources

About