A Simple Connectivity Scheme for Sparse Coding in an Olfactory System

By Ron A. Jortner, S. S. Farivar, Gilles Laurent. 2007

202 citations recorded by the source corpus.

Recent studies, using unbiased sampling of neuronal activity in vivo, indicate the existence of sparse codes in the brain. These codes are characterized by highly specific, associative (i.e., dependent on combinations of features) and often invariant neuronal responses. Sparse representations present many advantages for memory storage and are, thus, of wide interest in sensory physiology. Here, we study the statistics of connectivity in an olfactory network that contributes to the generation of such codes: Kenyon cells (KCs), the intrinsic neurons of the mushroom body (a structure involved in learning and memory in insects) receive inputs from a small population of broadly tuned principal neurons; from these inputs, KCs generate exquisitely selective responses and, thus, sparse representations. We find, surprisingly, that KCs are on average each connected to about 50% of their input population. Simple analysis indicates that such connectivity indeed maximizes the difference between input vectors to KCs and helps to explain their high specificity.

Subfields: Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics, Sensory Systems

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “Each Kenyon cell is contacted by 400 projection neurons, roughly half of the population (Jortner et al., 2007).”
Functional analysis of a higher olfactory center, the lateral horn (2012, background): “This helps KCs to function as coincidence detectors (Perez-Orive et al., 2002, 2004) responding only to synchronized firing from multiple presynaptic PNs (Jortner et al., 2007).”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, background): “A similar increase in thresholds at successive levels of the processing stream recently has been described in the olfactory system of the locust, where it generates high selectivity and sparseness in Kenyon cells (Jortner et al., 2007).”
Mechanisms of pattern decorrelation by recurrent neuronal circuits (2010, background): “It was previously found that the correlation between patterns or channels can be decreased by thresholding, a fundamental nonlinearity in neuronal transfer functions imposed by the mechanism of action potential generatio”
Encoding of mixtures in a simple olfactory system. (2013, unknown): “This effective connectivity of ~17% seems to contradict previous electrophysiological results that indicate 50 ± 15% connectivity from PNs to KCs (Jortner et al., 2007).”
Induction of aversive learning through thermogenetic activation of Kenyon cell ensembles in Drosophila (2014, background): “…of Kenyon cells out of large neuronal populations, a principle of information encoding commonly referred to as “sparse coding” (Perez-Orive et al., 2002; Szyszka et al., 2005; Jortner et al., 2007; Ito et al., 2008; Murthy et al., 2008; Turner et al., 2008; Luo et al., 2010; Honegger et al., 2011).”
Neural syntax: cell assemblies, synapsembles and readers (2010, unknown): “For example, the 50,000 reader KCs in the mushroom body, in principal, can respond to 50,000 odorant combinations (Figure 3A; Jortner et al., 2007; Perez-Orive et al., 2002).”
Sparse, Decorrelated Odor Coding in the Mushroom Body Enhances Learned Odor Discrimination (2014, background): “(f) Temperature-dependent increase in correlation was greater for IA:EB mixtures than for IA:EB 4:1 versus δ-DL within APL-labeled hemispheres of APL>shits1 flies (n = 20 [13]) and greater for IA:EB mixtures in APL-labeled than APL-unlabeled hemispheres of APL>shits1 flies (n = 10 [9]).”
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).”
Optimal degrees of synaptic connectivity (2017, unknown): “Experiments suggest that Kenyon cells in the locust mushroom body are densely connected (Jortner et al., 2007), unlike in Drosophila, implying that their input synapses may potentially exhibit associative modifications.”
Olfactory information processing in Drosophila. (2009, unknown): “Projection neurons form synaptic connections with Kenyon cells in the mushroom body, where each Kenyon cell receives inputs from approximately 10 projection neurons inDrosophila or 400 projection neurons in locust [57,58].”
Normalization for Sparse Encoding of Odors by a Wide-Field Interneuron (2011, result): “Because each KC is, on average, connected to about half of its presynaptic population (the projection neurons or PNs) (16), small changes in the PN population’s output could affect the reliability of the KCs’ sparse output (SI 1), inconsistent with experimental observations (11).”
Odor Discrimination in Drosophila: From Neural Population Codes to Behavior (2013, background): “Thus, the MB recodes compact, dense ePN activity patterns into a much larger ensemble of KCs (Jortner et al., 2007).”
Oscillatory integration windows in neurons (2016, background): “In each oscillatory cycle, KCs receive excitatory input from PNs followed by inhibitory input from GABAergic neurons (Perez-Orive et al., 2002; Gupta and Stopfer, 2012).”
Olfactory representations by Drosophila mushroom body neurons. (2008, background): “This periodic integration scheme is essential because PNto-KC convergence is extremely high [ϳ50% or ϳ400 PNs per KC (Jortner et al. 2007)] relative to the number of simultaneous PN inputs required to bring a KC to threshold (Ͻ70) (Jortner et al. 2007).”