Effective And Optimal Storage of Memory Patterns With Variable Coding Levels
This paper studies the storage of memory patterns with varying coding levels (fraction of firing neurons within a pattern) in an associative memory network. It was previously shown that effective memory storage (that scales with the network’s size) requires that the synaptic modification rule used during learning explicitly depends on the coding level of the stored memory patterns. We show that the memory capacity of networks storing variably coded memory patterns is inherently bounded and does not scale with the network’s size. These results question the biological feasibility of associative memory learning that uses synaptic level information only. However, we show that applying a neuronal weight correction mechanism that uses local neuronal level information, provides effective memory capacity even when the coding levels vary considerably. Using neuronal weight correction yields near optimal memory performance even with non-optimal synaptic learning rules. These findings provide further support to the idea that neuronal level normalization processes play an important role in governing synaptic plasticity and may be crucial for learning in the nervous system. ∗To whom correspondence should be addressed. 0