  Conventional modeling approaches have found limitations in matching the
increasingly detailed neural network structures and dynamics recorded in
experiments to the diverse brain functionalities. On another approach, studies
have demonstrated to train spiking neural networks for simple functions using
supervised learning. Here, we introduce a modified SpikeProp learning
algorithm, which achieved better learning stability in different activity
states. In addition, we show biological realistic features such as lateral
connections and sparse activities can be included in the network. We
demonstrate the versatility of this framework by implementing three well-known
temporal codes for different types of cognitive tasks, which are MNIST digits
recognition, spatial coordinate transformation, and motor sequence generation.
Moreover, we find several characteristic features have evolved alongside the
task training, such as selective activity, excitatory-inhibitory balance, and
weak pair-wise correlation. The coincidence between the self-evolved and
experimentally observed features indicates their importance on the brain
functionality. Our results suggest a unified setting in which diverse cognitive
computations and mechanisms can be studied.
