Generating sparse and selective third-order responses in the olfactory system of the fly

By Sean X. Luo, R. Axel, L. F. Abbott. 2010

151 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics, Genetics

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

1. Nitin Gupta
2. Andrew C. Lin
3. Mark Stopfer
4. Kenneth D. Harris
5. Hoger Amin
6. Hermann Riecke
7. Pierre Yger
8. Gero Miesenböck

In Ecology, Evolution, Behavior and Systematics

1. Andrew C. Lin
2. Hoger Amin
3. Nitin Gupta
4. Mark Stopfer
5. S. Farris
6. Wolf Huetteroth
7. R. Silver
8. N. A. Cayco-Gajic

In Genetics

1. Andrew C. Lin
2. Nitin Gupta
3. Mark Stopfer
4. Hoger Amin
5. Gero Miesenböck
6. S. Farris
7. M. Parnas
8. Wolf Huetteroth

Citations in the corpus

• Are mushroom bodies cerebellum-like structures? (2011, background): “Similarly, Kenyon cells have been reported to generate a sparse and selective output in response to olfactory input (Perez-Orive et al., 2002; Wang et al., 2004; Szyszka et al., 2005; Jortner et al., 2007; Ito et al., 2008; Luo et al., 2010).”
• Functional analysis of a higher olfactory center, the lateral horn (2012, background): “Nevertheless, the LH, in general, is thought to mediate very specific responses to odors (Luo et al., 2010).”
• Odor Discrimination in Drosophila: From Neural Population Codes to Behavior (2013, methodology): “However, calculations and several precedents (Legenstein and Maass, 2008; Luo et al., 2010; Olsen et al., 2010) suggest that the desired separation of ePN activity vectors could be achieved through inhibition that selectively blocks low-frequency spike trains.”
• The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “The size of this improvement increased with the number of cells used, consistent with reports that large numbers of random projections can provide useful data representations [43,44].”
• Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb (2012, background): “Implemented in simulations by global lateral inhibition, it was found to reduce the correlation between the different channels (activities of the principal neurons) across a large set of odors [70,71].”
• Neuronal mechanisms underlying innate and learned olfactory processing in Drosophila. (2019, background): “This ‘input gain control’ normalizes responses by total ORN activity, 71 decreases correlations between odor response profiles of different PNs [12], and improves 72 odor discrimination by a perceptron modelling neurons post-synaptic to PNs [14].”