Clustered Inputs Maximize Efficiency for Stable Place Field Encoding in Hippocampal Pyramidal Neurons

Abstract

How spatial organization of synaptic inputs along the dendritic tree of neurons influence their feature selectivity is a central question in neuroscience. Here, we mapped the three-dimensional distribution of all excitatory and inhibitory synapses across the entire dendritic arbor of individual mouse CA1 pyramidal neurons in vivo and built biophysically detailed computational models to probe their functional impact on their ability to emerge as place cells. We found that excitatory, but not inhibitory, synapses are non-uniformly distributed, forming structural clusters preferentially on terminal dendrites. These excitatory synaptic clusters generate high-quality place fields more efficiently than randomized synaptic distributions, requiring fewer active synapses to achieve equivalent somatic output. Crucially, even when firing rates are matched, clustered inputs sustain significantly higher voltage-gated calcium influx and NMDA receptor activation, key substrates for synaptic plasticity. Further analysis reveals that clustering enables domain-specific computational strategies: oblique dendrites rely on cluster location, basal dendrites on cumulative synaptic strength, and the trunk on local input dispersion. Disrupting clustering collapses this compartmentalized processing into uniform summation. Our results establish synaptic clustering as a key mechanism that maximizes computational efficiency and enables sophisticated dendritic processing underlying hippocampal spatial representation.