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Aurel A. Lazar

Publications and source records attributed to Aurel A. Lazar.

3 recordsLinked to original sources

The Connectome and the Quest for the Functional Logic of the Drosophila Early Olfactory System

In recent decades, the early olfactory system (EOS) of the fruit fly has become a leading model for studying olfactory processing and associative memory, owing in part to a well-characterized feedforward pathway that feeds the processes underlying associative memory and by examining the role played by a handful of neurons and synapses. The recent completion of dense electron-microscopy connectomes provides high quality visualizations of every cell type, neuron, and synapse along the early olfactory pathway. Yet a wiring diagram, however complete, does not by itself reveal the functional logic of a neural circuit. Reviewing the EOS connectome and synaptome datasets of the past fifteen years, we note that the feedforward pathway is embedded in dense local feedback circuits of large scale multi-input multi-output neurons. A systematic understanding of feedback loop abstractions, and their capacity to govern the input/output transformations at each neuropil stage, is the underlying foundation of the functional logic of the early olfactory circuits. In addition, we argue that a quantitative account of the functional logic requires an explicit model of the odorants present in the natural environment. Consisting of odorant objects, such a model defines the semantics and syntax of olfactory information processing, and calls for new distance measures for classifying the odorant semantics in support of associative memory operations. Furthermore, odor information processing must abide by causality, treating the circuit as a real-time, stage-by-stage cascade of giant local feedback loops.

q-bio.NC

Sparse Identification of Contrast Gain Control in the Fruit Fly Photoreceptor and Amacrine Cell Layer

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

q-bio.NC

Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

We investigate the sparse functional identification of complex cells and the decoding of visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm of both temporal and spatio-temporal stimuli is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification, and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the non-linear input model and the widely used spike-triggered covariance algorithm.

q-bio.NC