Capacity and Information Efficiency of a Brain-like Associative Net

We have determined the capacity and information efficiency of an associative net configured in a brain-like way with partial connectivity and noisy input cues. Recall theory was used to calculate the capacity when pattern recall is achieved using a winners-takeall strategy. Transforming the dendritic sum according to input activity and unit usage can greatly increase the capacity of the associative net under these conditions. For moderately sparse patterns, maximum information efficiency is achieved with very low connectivity levels (~ 10%). This corresponds to the level of connectivity commonly seen in the brain and invites speculation that the brain is connected in the most information efficient way.