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Zavitz, D.

Publications and source records attributed to Zavitz, D..

2 recordsLinked to original sources

Connectivity patterns shape sensory representation in a cerebellum-like network

Cerebellum-like structures -- such as the insect mushroom body -- are found in many brains and share a basic fan-out-fan-in network architecture. How the specific structural features of these networks give rise to their learning function remains largely unknown. To investigate this structure-function relationship, we developed a minimal computational model of the extensively studied Drosophila melanogaster mushroom body. We show how well-defined connectivity patterns between the Kenyon cells -- the constituent neurons of the mushroom body -- and their input projection neurons endow different functions, enabling the mushroom body to process olfactory information more efficiently. First, biases in the likelihoods at which individual projection neurons connect to Kenyon cells allow the mushroom body to prioritize the learning of particular, ethologically meaningful odors. Second, groups of projection neurons connecting preferentially to the same Kenyon cells facilitate the mushroom body to generalize across similar odors. Altogether, our results demonstrate how different connectivity patterns shape the representation space of a well-studied cerebellum-like network and impact its learning outcomes.

neuroscience↗

Effect of interglomerular inhibitory networks on olfactory bulb odor representations

Lateral inhibition is a fundamental feature of circuits that process sensory information. In the mammalian olfactory system, inhibitory interneurons called short axon cells comprise the first network mediating lateral inhibition between glomeruli, the functional units of early olfactory coding and processing. The connectivity of this network and its impact on odor representations is not well understood. To explore this question, we constructed a computational model of the interglomerular inhibitory network using detailed characterizations of short axon cell morphologies taken from mouse olfactory bulb. We then examined how this network transformed glomerular patterns of odorant-evoked sensory input (taken from previously-published datasets) as a function of the selectivity of interglomerular inhibition. We examined three connectivity schemes: selective (each glomerulus connects to few others with heterogeneous strength), nonselective (glomeruli connect to most others with heterogenous strength) or global (glomeruli connect to all others with equal strength). We found that both selective and nonselective interglomerular networks could mediate heterogeneous patterns of inhibition across glomeruli when driven by realistic sensory input patterns, but that global inhibitory networks were unable to produce input-output transformations that matched experimental data and were poor mediators of intensity-dependent gain control. We further found that networks whose interglomerular connectivity was tuned by sensory input profile decorrelated odor representations more effectively. These results suggest that, despite their multiglomerular innervation patterns, short axon cells are capable of mediating odorant-specific patterns of inhibition between glomeruli that could, theoretically, be tuned by experience or evolution to optimize discrimination of particular odorants. Significance StatementLateral inhibition is a key feature of circuitry in many sensory systems including vision, audition, and olfaction. We investigate how lateral inhibitory networks mediated by short axon cells in the mouse olfactory bulb might shape odor representations as a function of their interglomerular connectivity. Using a computational model of interglomerular connectivity derived from experimental data, we find that short axon cell networks, despite their broad innervation patterns, can mediate heterogeneous patterns of inhibition across glomeruli, and that the canonical model of global inhibition does not generate experimentally observed responses to stimuli. In addition, inhibitory connections tuned by input statistics yield enhanced decorrelation of similar input patterns. These results elucidate how the organization of inhibition between neural elements may affect computations.

neuroscience↗