How is the biological accuracy of ANNs typically measured? I am referring to the computational neuroscience side of neural network research that focuses on biological accuracy.  I've read references to improving biological realism (using say spiking neurons or binding neurons of various types) by emulating known properties of real neurons (action potentials and what not).  However, I haven't been able to find any detail on how the effectiveness of such attempts is measured empirically.

For example, cultured neuronal networks (such as a neurochip) could be used as a benchmark: Create an ANN and a BNN with the same number and arrangement of neurons, give them the same inputs, and compare the results...  Has anything like that been attempted?  How is the biological accuracy of ANNs typically measured?
One way the biological plausibility of an artificial neural network could be assessed is to look at how much a neural network abstracts away from the behavior of real neurons.

For instance, it is common in psychology and machine learning to use a sigmoidal activation function to determine the output of a node. If biological plausibility is a concern, one might prefer to use the Hodgkin-Huxley model. (See also: other neuronal models).

To many practitioners, biological plausibility is not a primary concern. In most ANNs, each node is not really believed to be implemented by a single neuron in the brain, and thus using a more realistic activation function does not necessarily make the network more biologically realistic.

Personally, I don't think looking at the behavioral accuracy of a biological neural network (compared  to an artificial neural network) says anything about the network's biological plausibility. Using more abstractions might produce more similar behavior due to less overfitting. Another concern is that the ANN could produce the same behavior as a BNN even if the actual computations involved are quite different.