  Information needs to be appropriately encoded to be reliably transmitted over
physical media. Similarly, neurons have their own codes to convey information
in the brain. Even though it is well-known that neurons exchange information
using a pool of several protocols of spatio-temporal encodings, the suitability
of each code and their performance as a function of network parameters and
external stimuli is still one of the great mysteries in neuroscience. This
paper sheds light on this by modeling small-size networks of chemically and
electrically coupled Hindmarsh-Rose spiking neurons. We focus on a class of
temporal and firing-rate codes that result from neurons' membrane-potentials
and phases, and quantify numerically their performance estimating the Mutual
Information Rate, aka the rate of information exchange. Our results suggest
that the firing-rate and interspike-intervals codes are more robust to additive
Gaussian white noise. In a network of four interconnected neurons and in the
absence of such noise, pairs of neurons that have the largest rate of
information exchange using the interspike-intervals and firing-rate codes are
not adjacent in the network, whereas spike-timings and phase codes (temporal)
promote large rate of information exchange for adjacent neurons. If that result
would have been possible to extend to larger neural networks, it would suggest
that small microcircuits would preferably exchange information using temporal
codes (spike-timings and phase codes), whereas on the macroscopic scale, where
there would be typically pairs of neurons not directly connected due to the
brain's sparsity, firing-rate and interspike-intervals codes would be the most
efficient codes.
