A unifying framework for synaptic organization on cortical dendrites

Abstract

Dendritic synaptic inputs are organized into functional clusters with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development, and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret vs. receptive field overlap in mouse visual cortex based on the cortical magnification of visual space. Including a back-propagating action potential explains branch clustering heterogeneity in the ferret, and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, our framework suggests that sub-cellular precision in connectivity can already be established in development, and unifies different aspects of synaptic organization across species and scales.Neurons in the developing brain are already precisely connected before sensory organs mature. It is widely accepted that spontaneous activity refines circuit connectivity to mature levels at the scale of single neurons and networks. Recent studies have revealed that spontaneous activity can also establish fine-scale organization of individual synapses within the dendritic arborizations of single neurons (1,2) . One striking example of this organization is functional synaptic clustering: synapses onto dendrites of pyramidal neurons that receive correlated input or encode a common sensory feature are spatially grouped. Synaptic clustering has been observed across brain regions, developmental ages and diverse species from rodent to primate (1-10) and has multiple functional benefits; it compartmentalizes the dendrites of single neurons, enables supra-linear integration of inputs and shapes memory

Cortical response selectivity derives from strength in numbers of synapses

“…This would give rise to soma-biased input populations 9 , 13 and could be achieved by modulating synaptogenesis and synaptic pruning 23 ; increasing the probability of stabilizing inputs co-tuned with the somatic output. While developmental models have made similar predictions 24 , this process has not been observed in vivo . How the strength of individual synapses relates to visual processing remains unclear, but this may be constrained by other operational features distinguishing presynaptic afferents.…”

Impact of functional synapse clusters on neuronal response selectivity

“…Notably, this mechanism did not generate clusters composed of functionally homogenous inputs, as global plasticity stabilises any input irrespective of its location within the dendritic tree that show correlated activity with a functional cluster. The interspersion of inputs with a wide range of functional properties on individual branches is an important characteristic of both experimental data 5,42,54 and computational models 55 and may thus be a signature of the regulation of structural synaptic plasticity on various spatial scales. Importantly, to generate functional clusters by global plasticity alone, dendritic nonlinearities have to be sufficiently strong with a low threshold to amplify the effect of small, randomly occurring clusters and to control the plasticity process, whereas clustering via local plasticity mechanisms does not necessarily require dendritic nonlinearities 21 .…”

Cortical neuron response selectivity derives from strength in numbers of synapses

“…This could be achieved by modulating synaptogenesis and synaptic pruning 23 ; increasing the probability of stabilizing inputs co-tuned with the somatic output. While developmental models of single neurons have made similar predictions 24 , this process has yet to be observed in vivo. Unitary synaptic strength, instead, may simply depend on operational properties of presynaptic afferents.…”