Fig. 7: Computational modeling supported the existence of two synapse states and a role of ECM in stabilizing newborn synapses.

a, Model of synapse dynamics in our system, based on four parameters: birth rate of new synapses (b_n), decay rates of new synapses (d_n), probability of transition from new to stable synapse (c_n→s) and decay rate of stable (d_s) synapses. b, Fitting of synapse lifetime data in Fig. 1m from Chat-PSD95^FingR fish, based on the two-state model described in a. Densities of synapses for each category of lifetime are shown from raw data (gray) and as predicted by the two-state model (purple). The numbers in parentheses on the x axis indicate the first and last time (in hours) that synapses were detected over 72 h. Note that while experimental analyses in Figs. 1 and 3 tracked only synapses that were present at 0 h (‘stable’) or born between 0 and 6 h (‘new’), the computational model examined all synapses observed over 72 h of imaging to generate this predicted distribution. c, Synapse dynamics calculated from the two-state model in mmp14^+/+ and mmp14b^−/− fish, showing calculations of the four parameters described in a. Units for synapse birth rate (b_n) reflect synapses born per µm per hour, shortened to 1 µm⁻¹ h⁻¹. Birth rate is independent of the number of synapses in the system. Units for rates of synapse decay or stabilization (d_n, c_n→s and d_s) reflect the rates as a proportion of existing synapses, that is, changed synapses per µm per hour divided by total synapses per µm, shortened to 1 h⁻¹. Error bars show ±s.d. ****P < 0.0001, Mann–Whitney U-test. See Supplementary Data 1 for exact P values. d, Graphical data summary. e, Graphical abstract of ECM impact on synapse numbers and dynamics. Illustrations in a, d and e created using BioRender.com.