Several options available but followings are superior to others in terms of capturing the real neuron/synapse behavior with a simple and compact VLSI implementation.
For neurons: Exponential adaptive integrate and fire models @ real-time.
For synapses: STD/STP + LTP/LTD circuits @ real-time.
Nope, you can keep going with the same logic: HH is just an approximatiin of the molecular kinetics and what you really need are the FEM models in 3D with all the protein pathways etc etc.
But this leads to an entirely intractable project. The science lies not in reproducing the exact neurons bug for bug, but keeping only the necessary details, within technical possibility, for explaining some observed phenomenon. LIF and AdEx neurons seem like a good compromise for neuromorphic hardware.
everyone draws the line somewhere, this is yours. Even we take this statement to be true, you still have metabolic networks changing transmitter concentrations, dendritic arbors evolving in entirely unidentifiable ways, etc. The goal of modeling is not to say that every detail is there, but ones relevant to account for specific feature of data.
I've been wondering if dendritic arborization means that current deep learning ANNs are hopelessly far away from biological reality, or if perhaps with deep networks simple ANNs could indeed learn to compute in a similar way to complex biological neurons, just over many layers of artificial neurons.
1 https://www.sciencedirect.com/science/article/pii/S089662730...