Single neurons detect spatiotemporal activity transitions through STP and EI imbalance

Sensory input and internal context converge onto the hippocampus as spatio-temporal activity patterns. Transitions in these input patterns are frequently salient. We demonstrate that short-term potentiation (STP) mediates escape from EI balance to implement mismatch detection in spatiotemporally patterned activity sequences. We characterized STP in the mouse hippocampus CA3-CA1 network using optogenetic patterned stimuli in CA3 while recording from CA1 pyramidal neurons. STP modulates EI summation across patterns, first amplifying, then reducing responses. We parameterized a multiscale model of network projections onto hundreds of E and I boutons on a CA1 neuron, each including stochastic signaling to mediate STP. The model detected mismatches in trains of input patterns, which we experimentally confirmed. Mismatch selectivity depends on stimulus overlap, network weights, and connectivity. It is robust over a wide range of model parameters and assumptions about input spike timing jitter, postsynaptic spiking and stochasticity. Finally, we predict that optimal mismatch selectivity can be tuned over low to high gamma frequencies by modulating network parameters, and show that there is strong mismatch detection for gamma-frequency bursts between theta cycles, consistent with theta-tuned snapshots of novel input.