  Conventionally, information is represented by spike rates in the neural
system. Here, we consider the ability of temporally modulated activities in
neuronal networks to carry information extra to spike rates. These temporal
modulations, commonly known as population spikes, are due to the presence of
synaptic depression in a neuronal network model. We discuss its relevance to an
experiment on transparent motions in macaque monkeys by Treue et al. in 2000.
They found that if the moving directions of objects are too close, the firing
rate profile will be very similar to that with one direction. As the difference
in the moving directions of objects is large enough, the neuronal system would
respond in such a way that the network enhances the resolution in the moving
directions of the objects. In this paper, we propose that this behavior can be
reproduced by neural networks with dynamical synapses when there are multiple
external inputs. We will demonstrate how resolution enhancement can be
achieved, and discuss the conditions under which temporally modulated
activities are able to enhance information processing performances in general.
