  In this work, we perform an exploratory study on synthesizing deep neural
networks using biological synaptic strength distributions, and the potential
influence of different distributions on modelling performance particularly for
the scenario associated with small data sets. Surprisingly, a CNN with
convolutional layer synaptic strengths drawn from biologically-inspired
distributions such as log-normal or correlated center-surround distributions
performed relatively well suggesting a possibility for designing deep neural
network architectures that do not require many data samples to learn, and can
sidestep current training procedures while maintaining or boosting modelling
performance.
