Synaptic plasticity in the hippocampus is fundamental to learning and memory, yet few studies examine how pattern learning occurs across multiple synapses. Such cross-synapse learning is fundamental to emergent properties of pattern discrimination and generalisation, which depend on assumptions about independence of plasticity and linearity of summation. We used sparse optogenetic spatiotemporal pattern stimulation in the CA3 coupled with postsynaptic depolarization to elicit plasticity on CA1 pyramidal neurons, and found that 'trained' patterns were selectively strengthened, but only in a subset of postsynaptic cells. Increased resting membrane potential and background mini-EPSP rates were predictive of learner cells. Summation following plasticity became more linear in learners compared to non-learners, consistent with the observed elevated post-stimulus hyperpolarization on non-learner cells. Thus our exploration of biologically plausible sparse activity supports pattern-selective learning, but in a heterogeneous manner modulated by both cell-intrinsic and network features.

Dendrites are essential determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we use a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)-expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus reveal that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV basket cell assemblies when cells are driven by spatially and temporally heterogeneous synaptic inputs. These results highlight the role of inhibitory neuron dendrites in synchronized network oscillations.