  A good understanding of how neurons use electrical pulses (i.e, spikes) to
encode the signal information remains elusive. Analyzing spike sequences
generated by individual neurons and by two coupled neurons (using the
stochastic FitzHugh-Nagumo model), recent theoretical studies have found that
the relative timing of the spikes can encode the signal information. Using a
symbolic method to analyze the spike sequence, preferred and infrequent spike
patterns were detected, whose probabilities vary with both, the amplitude and
the frequency of the signal. To investigate if this encoding mechanism is
plausible also for neuronal ensembles, here we analyze the activity of a group
of neurons, when they all perceive a weak periodic signal. We find that, as in
the case of one or two coupled neurons, the probabilities of the spike
patterns, now computed from the spike sequences of all the neurons, depend on
the signal's amplitude and period, and thus, the patterns' probabilities encode
the information of the signal. We also find that the resonances with the period
of the signal or with the noise level are more pronounced when a group of
neurons perceive the signal, in comparison with when only one or two coupled
neurons perceive it. Neuronal coupling is beneficial for signal encoding as a
group of neurons is able to encode a small-amplitude signal, which could not be
encoded when it is perceived by just one or two coupled neurons. Interestingly,
we find that for a group of neurons, just a few connections with one another
can significantly improve the encoding of small-amplitude signals. Our findings
indicate that information encoding in preferred and infrequent spike patterns
is a plausible mechanism that can be employed by neuronal populations to encode
weak periodic inputs, exploiting the presence of neural noise.
