Dendritic growth and synaptic organization from activity-independent cues and local activity-dependent plasticity

Abstract

Dendritic branching and synaptic organization shape single neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a computational model in which dendrite growth and the organization of synapses arise from the interaction of local activity-dependent plasticity and activity-independent cues from potential synaptic partners. Consistent with experiments, three phases of dendritic growth -- overshoot, pruning, and stabilization -- emerge naturally in the model. The model generates biologically-realistic dendritic morphologies under normal and perturbed learning rules, and synapses cluster spatially according to the correlated activity they experience. We demonstrate that activity-dependent and -independent factors influence dendritic growth throughout development, suggesting that early developmental variability can affect mature morphology. Our model generates dendrites with approximately optimal wiring length consistent with experimental measurements. Therefore, a single mechanistic model generates realistic dendritic development and accounts for synaptic organization from correlated inputs. Our work suggests concrete mechanistic components underlying dendritic growth and synaptic formation and removal in function and dysfunction.

A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites

“…Such simplicity makes our tool adaptable and easy to generalise to different morphologies and helps to understand whether certain neuron types optimise their dendrites primarily for material or conduction costs. However, our tool does not take into account the interactions between different neurons during growth, as do other morphological models such as CX3D [95,96] and the one in the reference [97]. Here the authors [97] used an activity-driven algorithm where neuronal growth was determined by the activity of nearby potential synapses.…”

“…However, our tool does not take into account the interactions between different neurons during growth, as do other morphological models such as CX3D [95,96] and the one in the reference [97]. Here the authors [97] used an activity-driven algorithm where neuronal growth was determined by the activity of nearby potential synapses. The approach of CX3D focused on chemical gradients and mechanical forces that can generate layer-specific branching patterns.…”

Developmental dynamics of the prefrontal cortical SST and PV interneuron networks: Insights from the monkey highlight human-specific features

“…The ultimate step of cortical maturation corresponds to the consolidation of the synaptic connections that persist beyond the phase of synaptic pruning [48]. Consolidation consists of two significant events: axonal myelination [49] and the deposition of chondroitin sulfate proteoglycans (CSPG), one of the main components of perineuronal nets (PNN), which form the extracellular matrix (ECM) [50].…”