Tracking long-term natural changes in hippocampal excitatory to inhibitory synapses from spikes

Although long-term synaptic plasticity is often studied using controlled electrical or optogenetic stimulation, it also occurs spontaneously during natural, ongoing brain activity. Here, using large-scale spike recordings in mice from the Allen Institute Visual Coding Neuropixels dataset, we examine to what extent fluctuations in putative synaptic efficacy can be tracked and explained by models of activity-dependent long-term plasticity. We first detect putative excitatory synaptic connections within the hippocampus based on cross-correlations between the spike trains of thousands of pairs of neurons. Most of these putative connections are between presynaptic neurons with broad spike waveforms and postsynaptic neurons with narrow spike waveforms and are consistent with synapses from excitatory to inhibitory units. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy. Previous work found that these fluctuations can be partially predicted from pre- and postsynaptic firing rates and models of short-term plasticity. Here we model naturally occurring long-term potentiation and depression using rate-based learning rules. We find that modeling the covariance of pre- and postsynaptic activity improves prediction of efficacy fluctuations. We also examine synaptic changes associated with hippocampal sharp-wave ripples, but do not find clear evidence of systematic SWR-associated changes for the putative synapses studied here.