Network Self-organization explains the distribution of synaptic efficacies in neocortex

The distribution of synaptic efficacies in neocortex has an approximately lognormal shape. Many weak synaptic connections coexist with few very strong connections such that only 20% of synapses contribute 50% of total synaptic strength. Furthermore, recent evidence shows that weak connections fluctuate strongly while the few strong connections are relatively stable, suggesting them as a physiological basis for long-lasting memories. It remains unclear, however, through what mechanisms these properties of cortical networks arise. Here we show that lognormal-like synaptic weight distributions and the characteristic pattern of synapse stability can be parsimoniously explained as a consequence of network selforganization. We simulated a simple self-organizing recurrent neural network model (SORN) composed of binary threshold units. The network receives no external input or noise but self-organizes its connectivity structure solely through different forms of plasticity. Across a wide range of parameters, the network produces lognormal-like synaptic weight distributions and faithfully reproduces experimental data on synapse stability as a function of synaptic efficacy. Overall, our results suggest that the fundamental structural and dynamic properties of cortical networks arise from the self-organizing forces induced by different forms of plasticity.