Spike-based strategies for rapid processing

By Simon J. Thorpe, Arnaud Delorme, R. V. Rullen. Neural Networks, 2001

786 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ea136a4f31dbf9bfbce7b31e363690a160a5132e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

STDP-based spiking deep neural networks for object recognition (2016, background): “It encodes the strength of these contrasts in the latencies of its output spikes (the higher the contrast, the shorter the latency).”
The tempotron: a neuron that learns spike timing–based decisions (2006, background): “Afferents in each input class, " and , are divided into N/2 synchronous pairs: for example, (1, 3) and (2, 10) in " patterns and (1, 6) and (2, 7) in patterns.”
Supervised Learning Based on Temporal Coding in Spiking Neural Networks (2016, background): “Given the typical firing rate of cortical neurons and delays across synaptic stages, this indicates rapid visual processing is mostly a feedforward process where neurons get to spike at most once [16].”
The fundamental plan of the retina (2001, background): “very first spikes of retinal ganglion cells after the appearance of a new image (Thorpe et al., 2001).”
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): “For example, phasic spiking captures the stimulation onset [14] and could be useful for the synchrony-based code in which neurons are allowed to emit a single spike [47].”
Eye smarter than scientists believed: Neural computations in circuits of the retina (2010, positive): “One possibility is that some of this information is already transmitted with the very first spikes of retinal ganglion cells after the appearance of a new image (Thorpe et al., 2001).”
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing (2014, background): “This distributed, asynchronous model processes information using both space and time [4]–[6], and is amenable to distributed, unsupervised adaptation [7], [8].”
Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity (2012, critical): “Although it has been postulated that spike times may provide a more rapid coding mechanism (Cury and Uchida, 2010; Gollisch and Meister, 2008; Thorpe et al., 2001), we found that decoders using spike count actually performed faster than those based on spike latency or peak timing (Figure 4F),…”
Animal detection in natural scenes: critical features revisited. (2010, methodology): “The experiment used a Go/No-Go gap paradigm, similar to that originally used by Thorpe et al. (1996) to elicit very fast responses: Observers were instructed to push and”
Gamma-band synchronization in the neocortex: novel analysis methods and their application to sensory and motivational systems (2013, background): “An important consequence of the delay between balanced excitation and inhibition is that it can strongly reduce the temporal window of integration (König et al., 1996; Pouille & Scanziani, 2001; Thorpe et al., 2001; Fries et al., 2007).”
Doing without Concepts (2009, background): “…a non-proprietary memory store—our longterm memory (figure 1.1).7 5 Clearly, not all these properties have to be possessed by the higher cognitive competences: linguistic understanding is not under intentional control and categorization can be extremely fast (Thorpe, Delorme, and VanRullen 2001).”
Ultra-rapid object detection with saccadic eye movements: visual processing speed revisited. (2006, background): “However, these measures includes both the time of visual processing and response execution (Fabre-Thorpe, Delorme, Marlot, & Thorpe, 2001; Thorpe et al., 1996).”
A review of learning in biologically plausible spiking neural networks (2019, neutral): “(Delorme, Perrinet, & Thorpe, 2001; Kheradpisheh et al., 2018).”
Learning in spiking neural networks (2013, background): “It is also important to note that the original SpikeNET implementation used supervised learning by manually adjusted the synaptic weighting between layers to produce impressive results [ TDV01 ].”