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Our current understanding of neuroplasticity paints a picture of a complexinterconnected system of dependent processes which shape cortical structure soas to produce an efficient information processing system. Indeed, thecooperation of these processes is associated with robust, stable, adaptablenetworks with characteristic features of activity and synaptic topology.However, combining the actions of these mechanisms in models has provenexceptionally difficult and to date no model has been able to do so withoutsignificant hand-tuning. Until such a model exists that can successfullycombine these mechanisms to form a stable circuit with realistic features, ourability to study neuroplasticity in the context of (more realistic) dynamicnetworks and potentially reap whatever rewards these features and mechanismsimbue biological networks with is hindered. We introduce a model which combinesfive known plasticity mechanisms that act on the network as well as a uniquemetaplastic mechanism which acts on other plasticity mechanisms, to produce aneural circuit model which is both stable and capable of broadly reproducingmany characteristic features of cortical networks. The MANA (metaplasticartificial neural architecture) represents the first model of its kind in thatit is able to self-organize realistic, nonrandom features of cortical networks,from a null initial state (no synaptic connectivity or neuronaldifferentiation). In the same vein as models like the SORN (self-organizingrecurrent network) MANA represents further progress toward the reverseengineering of the brain at the network level.

Cortical Circuits from Scratch: A Metaplastic Architecture for the Emergence of Lognormal Firing Rates and Realistic Topology