  Computational modeling is becoming a widely used methodology in modern
neuroscience. However, as the complexity of the phenomena under study
increases, the analysis of the results emerging from the simulations
concomitantly becomes more challenging. In particular, the configuration and
validation of brain circuits involving learning often require the processing of
large amounts of action potentials and their comparison to the stimulation
being presented to the input of the system. In this study we present a
systematic work-flow for the configuration of spiking-neuronal-network-based
learning systems including evolutionary algorithms for information transmission
optimization, advanced visualization tools for the validation of the best
suitable configuration and customized scripts for final quantitative evaluation
of the learning capabilities. By integrating both grouped action potential
information and stimulation-related events, the proposed visualization
framework provides qualitatively assessment of the evolution of the learning
process in the simulation under study. The proposed work-flow has been used to
study how receptive fields emerge in a network of inhibitory interneurons with
excitatory and inhibitory spike-timing dependent plasticity when it is exposed
to repetitive and partially overlapped stimulation patterns. According to our
results, the output population reliably detected the presence of the
stimulation patterns, even when the fan-in ratio of the interneurons was
considerably restricted.
