This video specifically talked about the real difference in biological neurons and its artificial counterpart. I found the part is on how to represent information and training "artificial biological neuron networks" interesting. Notes (terms: neuron := biological neuron):

• neurons accumulates the incoming charge (stateful), with charge leakage
• three ways to encode information: frequency, timing, and parallelism
• backprop breaks down on discrete signals
• the only known mechanism for neuron weight update is "neurons that fires together wires together". but it's not very useful to developing a way to train it.
• it's likely neurons interpret its input as binary.
• the firing rate limit of neurons is only around 4 ms/spike (very slow)
• directional hearing requires distinguishing 1/2 ms delay of signals. how is it possible given the maximum firing rate? answer: create a group of neurons each detecting different delay and ordering in input signal.
• neuron components:
  • loopback neuron can store a bit of signal that can be set and reset. (like an SR latch)
  • mpsc (read-write) buffer
  • mechanism that repetitively reading refreshes the memory (like DRAM)
• power efficiency. (12W for the brain) calculated result: neocortex only fires every two seconds (Wow!)