  We consider the information transmission problem in neurons and its possible
implications for learning in neural networks. Our approach is based on recent
developments in statistical physics and complexity science. We also develop a
method to select statistically significant neural responses from the background
activity and consider its wider applications. This would support temporal
coding theory as a model for neural coding.
