A biomorphic digital image sensor

By Eugenio Culurciello, Ralph Etienne-Cummings, Kwabena A. Boahen. IEEE J. Solid State Circuits, 2003

379 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/857db09fc6daf2c0c6572ee046f56b6d60def6bc

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In Cellular and Molecular Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Wide-Dynamic-Range CMOS Image Sensors—Comparative Performance Analysis (2009, background): “In these sensors, light intensity is converted into pulse frequency [29]–[32].”
Learning Sparse Neural Networks via Sensitivity-Driven Regularization (2018, background): “[15]) are two clear examples of how a sparse, properly-correlated input signal can make the learning problem easier.”
STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “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;…”
Event-Based Vision: A Survey (2019, background): “by another photodiode. The brighter the light, the faster the capacitor discharges. The ATIS intensity readout transmits two more events coding the time between crossing two threshold voltages, as in [48]. This way, only pixels that change provide their new intensity values. The brighter the illumin”
A 128$\,\times$ 128 1.5% Contrast Sensitivity 0.9% FPN 3 µs Latency 4 mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Preamplifiers (2013, background): “formation sensors [9], [10], time-to-first spike coding sensors”
Neuromorphic Silicon Neuron Circuits (2011, methodology): “This reduces power during spike production by a factor of approximately 25, accelerates the transition at threshold by a factor of approximately 100, and reduces power consumption during reset (Culurciello et al., 2003).”
CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking (2009, background): “If the components generate short-latency meaningful, nonredundant spikes, then spike-based systems can efficiently compute “ondemand” compared to conventional approaches.”
Neuromorphic sensory systems. (2010, background): “This understanding has led to recent neuromorphic sensors with performance that can exceed conventional technology, especially in applications like low power, high speed, or wide dynamic range vision [20–25,26 ], and neural prosthetics [51 ,52 ].”
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output (2014, background): “The so-called ‘‘octopus retina’’ [42] encodes and communicates individual pixel intensities in the instantaneous frequency (or interspike intervals) of AER events emitted concurrently by each pixel.”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “AER has been used fundamentally in vision (retina) sensors, for purposes such as simple light intensity to frequency transformations (Culurciello et al., 2003; Posch et al., 2010), time-to-first-spike coding (Ruedi et al., 2003; Chen and Bermak, 2007), foveated sensors (Azadmehr et al., 2005),…”