RT Journal Article SR Electronic T1 On the distribution and function of synaptic clusters JF bioRxiv FD Cold Spring Harbor Laboratory SP 029330 DO 10.1101/029330 A1 Romain Cazé A1 Amanda Foust A1 Claudia Clopath A1 Simon R. Schultz YR 2016 UL http://biorxiv.org/content/early/2016/01/31/029330.abstract AB Local non-linearities in dendrites render neuronal output dependent on the spatial distribution of synapses. Previous models predicted that a neuron is more likely to fire when synaptic activity is clustered in space, and some experimental studies have observed synapses activating in clusters. Other studies, however, have revealed that synapses can also uniformly activate across dendrites without apparent spatial bias. In order to reconcile these two sets of observations, we develop a multi-compartment model that: (i) shows how clustered synapses can form and distribute throughout available compartments, and (ii) responds, subsequent to learning, most saliently to uniformly distributed synaptic activity. We measure co-activation probability to show that cluster formation and distribution depend on correlated activity within input ensembles that impinge on multiple compartments, each operating a local unsupervised, Hebbian learning rule. Clustered synapses evolve in response to long-term, learned input ensembles, heightening a neuron’s sensitivity to scattered “novel” inputs. As a result of the proposed learning rule, clustered inputs correspond to familiar pre-learned stimuli, while scattered inputs depolarize the cell for novel, unlearned stimuli. Our model reconciles seemingly conflicting experimental evidence, and suggests how clustered and scattered synapses together could underlie single neuron discrimination between familiar and novel inputs.