Sparse, Decorrelated Odor Coding in the Mushroom Body Enhances Learned Odor Discrimination

By Andrew C. Lin, A. Bygrave, Alix de Calignon, Tzumin Lee, Gero Miesenböck. 2014

303 citations recorded by the source corpus.

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell–APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor specificity of memories.

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

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Considerable evidence has now accumulated that the mushroom body employs ultrasparse coding, as studies using a variety of methods, including whole-cell recordings, tetrode recordings, and calcium imaging, all found that only around 5-10% of Kenyon cells were active upon odour presentation (Figure 3”
Recognition (2003, positive): “These receptor responses converge onto glomeruli, producing sparse combinatorial activity patterns that enable robust odor discrimination and efficient representation of high-dimensional sensory inputs (Buck & Axel, 1991; Lin et al., 2014; Wang et al., 2021).”
Oscillatory integration windows in neurons (2016, background): “, 2002), but recent work has shown that the inhibition originates mostly from the unique feedback neuron GGN (Papadopoulou et al., 2011; Gupta and Stopfer, 2012; Lin et al., 2014).”
High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding (2021, background): “In detail, it maps each sample to a higher-dimensional Hamming space by a sparse binary mapping matrix; then each sample is represented as a sparse binary hash code after a winner-take-all (WTA) competition [26, 44].”
Drosophila Learn Opposing Components of a Compound Food Stimulus (2014, methodology): “Two-photon imaging was performed with a custommade imaging setup as described [35].”
Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit. (2017, unknown): “KCs are also under a recurrent, inhibitory control of GABAergic anterior paired lateral (APL) neurons (Figure 1A; Lin et al., 2014; Liu and Davis, 2009; Tanaka et al., 2008).”
Attractor and integrator networks in the brain (2021, unknown): “Part c adapted from ref.(136), Springer Nature.”
The complete connectome of a learning and memory centre in an insect brain (2017, background): “2f), consistent with the proposed role of APL in maintaining sparse, decorrelated KC responses 30;34;35 .”
Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit (2015, background): “The activation of this neuron was shown to enhance odor memory [13].”
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster (2017, background): “(B) APL, a wide-field inhibitory neuron that innervates the entire MB and sparsifies KC activity (Lin et al., 2014; Liu and Davis, 2009).”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, background): “…similarities to the cerebellar input layer (Laurent, 2002), including random connectivity (Caron et al., 2013), and inhibitory interneurons that facilitate sparsification (Papadopoulou et al., 2011), enhancing pattern separation and enabling the discrimination of similar odors (Lin et al., 2014).”