AER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface

By Vincent Chan, Shih-Chii Liu, A. van Schaik. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2007

343 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/230089e5e5de57e0c1fe30294e4a38e261bfb191

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Timothée Masquelier
2. T. Serrano‐Gotarredona
3. B. Linares‐Barranco
4. Giacomo Indiveri
5. Nikola K. Kasabov
6. Maryam Gholami Doborjeh
7. Zohreh Gholami Doborjeh
8. Shih-Chii Liu

In Cognitive Neuroscience

1. Giacomo Indiveri
2. Shih-Chii Liu
3. Timothée Masquelier
4. Maryam Gholami Doborjeh
5. Zohreh Gholami Doborjeh
6. Ryad Benosman
7. Tobi Delbruck
8. B. Linares‐Barranco

In Electrical and Electronic Engineering

1. Giacomo Indiveri
2. Shih-Chii Liu
3. Michael Pfeiffer
4. Ryad Benosman
5. Tobi Delbruck
6. S. Furber
7. B. Linares‐Barranco
8. Timothée Masquelier

Citations in the corpus

• Neuromorphic computing at scale (2025, positive): “Similarly, medical devices such as silicon cochlear implants 56 and prosthetics with e-dermal layers 57 can improve sensory fidelity and sensitivity.”
• Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, methodology): “Then, continuous value time series of voxel data that measure activity at a certain brain location is encoded into a spike train using threshold-based representation (TBR) method or other methods [19], [22], [24].”
• On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “AER has also been used for auditory systems (Lazzaro et al., 1993; Cauwenberghs et al., 1998; Sarpeshkar et al., 2005; Wen and Boahen, 2006; Chan et al., 2007), competition, and winner-takes-all networks (Chicca et al., 2007; Oster et al., 2008), and even for systems distributed over wireless…”
• STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, methodology): “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.,…”
• The SpiNNaker Project (2014, methodology): “Analogous interfaces with AER sensors have been developed in collaboration with the Institute of Neuroinformatics (Zurich, Switzerland; using the DVS sensor [30] and the ‘‘silicon cochlea’’ [34]), with the Biology Group at the University of Osaka (Osaka, Japan; using a sensor inspired by the sustain”
• Neuromorphic sensory systems. (2010, background): “Neuromorphic sensory systems, Curr Current Opinion in Neurobiology 2010, 20:1–8 two ears [37] or by emulating the echolocalization mechanism of bats [38].”