Neuromorphic sensory systems.

By Shih-Chii Liu, Tobi Delbruck. Current Opinion in Neurobiology, 2010

347 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ea089e4a4dc1e31a1199e28c4b4524adbf4a53c2

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Memory and Information Processing in Neuromorphic Systems (2015, background): “However, it has been argued that there are several areas in which neuromorphic systems offer significant advantages over conventional computers [13], [14], [27], [4], [5], such as that of sensory processing [28] or “autonomous systems”.”
Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator (2014, background): “tionally well for neuromorphic event-driven sensors, as shown in [1] and [3]–[5].”
A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS (2018, background): “Information processing is entirely event-driven, leading to sparse low-power computation [5].”
HFirst: A Temporal Approach to Object Recognition (2015, background): “Notable examples of spiking AER vision sensors include the earliest examples of spiking silicon retinae by Culurciello et al. [18] and Zaghloul et al. [19], as well as the more recent Dynamic Vision Sensor (DVS) from Delbruck [21], the sensitive DVS from Linares-Berranco [22], and the Asynchronous…”
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing (2015, background): “These platforms are ideally driven by input from neuromorphic sensors such as silicon retinas [12] or cochleas [13], which create sparse, framefree, and precisely timed streams of events, with substantially reduced latencies compared to frame-based approaches.”
Event-Based Vision: A Survey (2019, background): “More recent event camera technology has been reviewed in the electronics and neuroscience literature [10], [36], [38], [45], [46], [47].”
Visual-Inertial Navigation: A Concise Review (2019, neutral): “event cameras [178, 179] may better capture dynamic”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “interfacing circuits and networks of the type described in this paper with neuromorphic event-based sensors [164]; new promising neural-based approaches have been proposed that link neuromorphic systems to machine learning [165]–[169]; substantial progress has been made in the field of neuromorphic ”
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, background): “…to auditory/visual sensory systems (Sarpeshkar et al., 1996; van Schaik and Meddis, 1999; Zaghloul and Boahen, 2004; Costas-Santos et al., 2007; Liu and Delbruck, 2010), to reconﬁgurable spiking neural network architectures with learning and plasticity (Giulioni et al., 2008; Hsieh and Tang,…”
Finding a roadmap to achieve large neuromorphic hardware systems (2013, background): “…engineering work based on these front-end systems (Riesenhuber and Poggio, 2000; Fu et al., 2008; Schaik et al., 2009; Chakrabartty and Liu, 2010; Liu and Delbruck, 2010; Farabet et al., 2011; Sejnowski and Delbruck, 2012), our focus will be on the computation using these front-end structures in…”
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output (2014, background): “Recently, an increasing amount of effort has been put into the development of practicable vision sensors based on biological principles [24], [25].”
Event-driven contrastive divergence for spiking neuromorphic systems (2013, background): “Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment.”