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Aurel A. Lazar (Fellow, IEEE), Professor of Electrical Engineering at Columbia University.

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

My work on computing with neural circuits is centered on Neural Computing Engines and on NeuroInformation Processing Machines. I pioneered formal theoretical methods of neural encoding and decoding (Time Encoding Machines) and functional identification of dendritic stimulus processors and biophysical spike generators (Channel Identification Machines). My research group created and implemented Spike Processing Machines and Phase Processing Machines 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.

I initiated Neurokernel, an open source platform for the emulation of the fruit fly brain, and Neuroarch, a database for querying and executing fruit fly brain circuits. Neurokernel and NeuroArch are two of the systems foundations of the Fruit Fly Brain Observatory (FFBO), a worldwide collaborative effort between experimentalists, theorists and computational neuroscientists with the goal to create an open platform for the emulation and biological validation of fruit fly brain models in health and disease. I am currently leading the development of NeuroNLP and NeuroGFX, two key FFBO applications, that enable researchers to use plain English to probe biological data that are integrated into NeuroArch and provide users highly intuitive tools to execute neural circuit models with Neurokernel.

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.