Aurel Lazar

Aurel Lazar Columbia University
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Research Summary

My current research interests are in computing with neural circuits (in silico), and on reverse engineering the fruit fly (Drosophila melanogaster) brain (in vivo).

My work on computing with neural circuits is centered on Neural Computing Engines and on Massively Parallel Neural Computation. I pioneered formal theoretical methods of neural encoding and decoding (Time Encoding Machines), functional identification of dendritic stimulus processors and biophysical spike generators (Channel Identification Machines) and, spike processing. My research group implemented massively parallel neural computation algorithms in the analog domain (graded potentials) and in the spike domain on clusters of GPUs. Some of the code developed by my collaborators is available for free download.

My in vivo work on Reverse Engineering the Fruit Fly Brain primarily addresses sensory processing in the early olfactory system of the Drosophila. I led a team of two graduate students who developed a ground-breaking odor delivery system that is both precise and reproducible; time-varying odor waveforms reaching a fruit fly can be reproduced to within 1% on a millisecond time-scale. We have demonstrated that Olfactory Sensory Neurons (OSNs) encode both the concentration and concentration gradient of odorant waveforms and that, similarly, projection neurons encode both the OSN spiking rate and its gradient.

More recently (2012) I initiated the Neurokernel Project, a potentially transformative open-source software platform for the isolated and integrated emulation and validation of fruit fly brain neural circuits, their connectivity patterns, and other parts of the fly?s nervous system on clusters of GPUs. Neurokernel provides standard APIs among local processing units (modeling neuropils) and tools for building an architecture from components developed by the community of researchers at large. Means for managing model data that facilitates modifications of fly brain models or improve their accuracy in light of new experimental data is provided by Neuroarch, a database for specification and storage of canonical neural circuit models of the fly brain.

Prior to my research in computational and systems neuroscience, I spent some 20 years as PI leading a number of computer networking research groups. I covered a broad set of research topics/fields, including building major switching hardware, architecting broadband kernels for programmable networks and creating game theory models for resource allocation. I also run a networking start-up as CEO.

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