Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex

By R. V. Rullen, Simon J. Thorpe. Neural Computation, 2001

558 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/85389a5719d3158a376e19e9d4149ee4ed926c6b

How researchers see this paper

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

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Towards spike-based machine intelligence with neuromorphic computing (2019, methodology): “Such static-frame-based data are then converted to spike trains using appropriate encoding techniques, such as rate coding or rank-order coding 25 (see Fig.”
Ordinal Measures for Iris Recognition (2009, background): “Rullen and Thorpe [ 24 ] suggested that temporal order coding might form a rank-based image representation (i.e., ordinal measures) in the visual cortex.”
A review of learning in biologically plausible spiking neural networks (2019, unknown): “Additionally, ROC is more sensitive to noise, because, the rank of each spike is dependent on the rank of other spikes.”
Introduction to spiking neural networks: Information processing, learning and applications. (2011, background): “Thorpe and colleagues (Thorpe 1990, VanRullen and Thorpe 2001) proposed that the order in which each ganglion cell emits its first spike codes for the visual stimulus.”
STDP-based spiking deep neural networks for object recognition (2016, background): “Among them, rank-order coding is shown to be efficient for rapid processing (even possibly in retinal ganglion cells) (Portelli et al., 2016; Van Rullen & Thorpe, 2001).”
Distributed processing and temporal codes in neuronal networks (2009, background): “The main argument for the need to flexibly define relations also at these high levels of processing derives from the evidence that perceptual objects are not only represented by individual highly complex chunking neurons but also by distributed assemblies of cells (Singer 1999; Tsunoda et al. 2001).”
Spike-based strategies for rapid processing (2001, background): “Third, it is not specific for biologically important categories like animals, because it is equally fast and accurate for a completely artifactual category, namely, means of transport (Van Rullen & Thorpe, 2001b).”
The SpiNNaker Project (2014, background): “With the first observable differences in the temporal lobe starting after 150 ms of the stimulus onset, and with several synaptic stages required to arrive at the infero-temporal cortex (IT, the visual area where object recognition takes place), neurons can emit at most one spike to encode the infor”
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity (2007, background): “First, when stimulated with a ﬂashed visual stimulus, the neurons in the various layers of the system ﬁre asynchronously, with the most strongly activated neurons ﬁring ﬁrst—a mechanism that has been shown to efﬁciently encode image information [3].”