TY - JOUR T1 - Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-Random Features of Cortical Synaptic Wiring JF - bioRxiv DO - 10.1101/027268 SP - 027268 AU - Daniel Miner AU - Jochen Triesch Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/10/01/027268.abstract N2 - Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, among other properties, above-chance bidirectionality, an overrepresentation of certain triangular motifs, and a heavy-tailed distribution of synaptic efficacies. Additional evidence suggests significant distance dependency to connectivity over a scale of several hundred micrometers, and particular patterns of synaptic turnover dynamics. It is currently not understood what processes give rise to this combination of features of cortical wiring. We present a spiking network model of a cortical slice culture which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring.Author Summary The problem of how the brain wires itself up has important implications for the understanding of both brain development and cognition. We present a self-organizing neural network model incorporating just a handful of plausible mechanisms of change and development alongside topological constraints. The model gives rise to numerous non-random features which have been observed in experiments, but never before simultaneously produced by a single model. The results imply that only a few simple mechanisms and constraints are required to produce, at least to the first approximation, various characteristic features of a typical fragment of brain microcircuitry. In the absence of any of these mechanisms, simultaneous production of all desired features fails, suggesting a minimal set of necessary mechanisms for their production. ER -