Assistant / engineer position in neuromorphic engineering

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France

DETAILS OF THE OFFER

Working place: Institute of Neurodegenerative Diseases (IMN)

Missions:

The goal of the project is to expand our current efforts towards performing large-scale simulations of conductance-based neuronal models on FPGAs, with an application to neurostimulation of the  hippocampal formation.

At the Institute of Neurodegenerative Diseases (IMN), the teams of Drs. Wagner and Aussel have developed a computational approach to study the effect of neurostimulation on the hippocampus, a brain structure particularly implicated in memory and Alzheimer’s disease. Specifically, they developed a computer model to represent hippocampal neurons in a biologically realistic way, and to simulate the effect of electrical stimulation on their activity (https://elifesciences.org/articles/87356). However, this approach is currently limited because it requires very large computing capacities, and the most widespread software does not allow simulations to be carried out in a reasonable time. At the Material-to-System Integration (IMS) Laboratory, Dr. Levi has recently developed a real-time neuromorphic system for the simulation of biomimetic neurons. The combined expertise of the IMN and IMS are complementary and provide an innovative solution for the simulation and understanding of neuronal mechanisms.

In this context, the RT-HippoNeuroStim project aims at translating the hippocampal model previously developed at the IMN onto the new neuromorphic computing architecture developed at the IMS. This architecture is based on Field Programmable Gate Arrays (FPGA) and shows much faster computation speed than current software emulation. We will leverage this platform to simulate the activity of the hippocampus in real time, which will greatly accelerate research on hippocampal neurostimulation.

Qualifications:

Enthusiastic and curious candidates with a certain autonomy and willing to develop skills at the interface between embedded system and neuroscience are encouraged to apply. The candidate will have knowledge in programming (Python, C/C++), embedded system (C/C++, basic Linux), FPGA design (VHDL, HLS), and computational neuroscience. Hence, multidisciplinary work capability is highly recommended.

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