RT Journal Article SR Electronic T1 Signal Variability Reduction and Prior Expectation Generation through Wiring Plasticity JF bioRxiv FD Cold Spring Harbor Laboratory SP 024406 DO 10.1101/024406 A1 Naoki Hiratani A1 Tomoki Fukai YR 2015 UL http://biorxiv.org/content/early/2015/08/13/024406.abstract AB In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly non-random, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We show that a connection structure helps synaptic weight learning when it provides prior expectations. We further demonstrate that an adequate network structure naturally emerges from dual Hebbian learning for both synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Correlations between spine dynamics and task performance generated by the proposed rule are consistent with experimental observations.Author Summary A virtue of the brain that is missing from artificial machines is its ability to reorganize and improve the circuit structure. Neural circuits should be adequately tuned to perform information processing such as decoding of sensory signal from noisy sensory inputs, or motor command generation from stochastic premotor inputs. Activity-dependent modifications of synaptic efficiency through long-term potentiation and depression are considered to play a major role in this tuning, but rewiring through creation and elimination of synaptic connections is also active even in the cortex of adult mammalian. It is still unknown what neural circuits learn to represent through the changes in synaptic efficiency and connections, and how such learning is performed by local spine dynamics. In this study, we reveal the functional advantage of representation by synaptic connection structure over that by synaptic efficiency. Furthermore we derive a dual-Hebbian learning rule that governs the two forms of plasticity. The rule improves network communication and enables robust computation by capturing slow components of the environment with connection structure. Our work provides an important step towards understanding of synaptic wiring plasticity and resultant connection structure.