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

In order to closely mimic biology, it is important to consider the various classes of neurons that exist. For example, cortical neurons have many different classes and types that we aim to replicate. By focusing on biophysical details and utilizing multi-compartment models, we can accurately represent the topology of neurons. We work at the biological time scale to facilitate communication between different components. When creating a neural network, it is essential to incorporate synapses, axonal delay, internal plasticity, and noise. The choice of neuron model is crucial and should be based on the specific application requirements.

A comparison of neuron models can be seen in a table by Izhikevich, with biological plausibility on the vertical axis and implementation costs on the horizontal axis. The simplest model is located in the lower left, while the most complex model is in the upper right. Our team primarily implements the Hodgkin-Huxley model due to its close resemblance to biology. We have also worked with the Izhikevich, Tubul, and Kohno models in collaboration with the University of Tokyo.

Overall, selecting the appropriate neuron model is essential for accurately representing biological processes in neural networks.