Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight 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 nonrandom, 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 demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance.
Department of Complexity Science and Engineering, The University of TokyoKashiwa, Japan; Laboratory for Neural Circuit Theory, RIKEN Brain Science InstituteWako, Japan.
Naoki
N
Hiratani
Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute Wako, Japan.
Tomoki
T
Fukai
D000818:Animals; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D055495:Neurogenesis / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D013569:Synapses / Q000502:physiology*
computational model; connectomics; neural decoding; synaptic plasticity; synaptogenesis