Signal Variability Reduction and Prior Expectation Generation through Wiring Plasticity

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.