| First Author | Johnson RE | Year | 2014 |
| Journal | Curr Biol | Volume | 24 |
| Issue | 19 | Pages | 2301-6 |
| PubMed ID | 25220059 | Mgi Jnum | J:252800 |
| Mgi Id | MGI:5926772 | Doi | 10.1016/j.cub.2014.08.018 |
| Citation | Johnson RE, et al. (2014) Retrograde plasticity and differential competition of bipolar cell dendrites and axons in the developing retina. Curr Biol 24(19):2301-6 |
| abstractText | Most neurons function in the context of pathways that process and propagate information through a series of stages, e.g., from the sensory periphery to cerebral cortex. Because activity at each stage of a neural pathway depends on connectivity at the preceding one, we hypothesized that during development, axonal output of a neuron may regulate synaptic development of its dendrites (i.e., retrograde plasticity). Within pathways, neurons often receive input from multiple partners and provide output to targets shared with other neurons (i.e., convergence). Converging axons can intermingle or occupy separate territories on target dendrites. Activity-dependent competition has been shown to bias target innervation by overlapping axons in several systems. By contrast, whether territorial axons or dendrites compete for targets and inputs, respectively, has not been tested. Here, we generate transgenic mice in which glutamate release from specific sets of retinal bipolar cells (BCs) is suppressed. We find that dendrites of silenced BCs recruit fewer inputs when their neighbors are active and that dendrites of active BCs recruit more inputs when their neighbors are silenced than either active or silenced BCs with equal neighbors. By contrast, axons of silenced BCs form fewer synapses with their targets, irrespective of the activity of their neighbors. These findings reveal that retrograde plasticity guides BC dendritic development in vivo and demonstrate that dendrites, but not territorial axons, in a convergent neural pathway engage in activity-dependent competition. We propose that at a population level, retrograde plasticity serves to maximize functional representation of inputs. |
