Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA - presented by Pr Timothée Levi

Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA

Pr Timothée Levi

Pr Timothée Levi
Slide at 21:40
Emulation on BioemuM for ALS studies Timothee (+) Real-time (-) No synapses (-) Number of neurons BioemuM Kobayashi 2021 Maki 2018 Architecture SoC FPGA Model Neurons 3072 Comp/neuron (av) Synapses 780404 ~2500 Computation time (ratio) 9000 (1s in 2.5h) 828 (10s in 2.3h) Target SOM K26 Tesla V100 1 core First multicompartment HH implementation on FPGA in litterature Synapses could be added while maintaining real-time Porting on larger and more recent boards would increase performances
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References
  • 1.
    A. Maki et al. (2019) FPGA-based CNN Processor with Filter-Wise-Optimized Bit Precision.
  • 2.
    T. Kobayashi et al. (2021) Testing an Explicit Method for Multi-compartment Neuron Model Simulation on a GPU. Cognitive Computation
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Summary (AI generated)

In conclusion, this system operates in real time and is the first multi-compartment HH implementation on FPGA in the literature, which is a significant achievement. Our goal was not to use more neurons compared to larger implementations on GPU, but to maintain real-time performance. This is crucial for conducting long-term simulations to study the behavior and dynamics of neurological disorders over time, which is not feasible with GPU implementations that take hours to simulate just one second.

Being able to simulate in real time allows us to communicate with different types of neurons - healthy, metal, and disorder ones - and even apply stimulation to modify their dynamics. This capability is the next step in our research. It should be noted that while we initially stated there were no synapses, there are actually synapses implemented in the system, although not all connectivity has been fully established yet.

In terms of activity monitoring, the system can provide spiking activity reports directly on the onboard file for monitoring. Additionally, membrane potentials can be obtained from the output, allowing for analysis of shape and frequency.