Connectomic traces of Hebbian plasticity in the entorhinal-hippocampal system

The key model of how we learn and memorize is Hebbian learning in the hippocampus, via long-term potentiation of synapses, allowing the storage of associations, linkage to places, and their consolidation into imprinted episodes. Learning is therefore expected to change synaptic weights. With the notion of hippocampal circuits being the primary site of learning in the mammalian brain, it has been assumed that all synapses in these circuits are constantly exposed to synaptic plasticity, and possibly in a learned state. However, a testing of these hypotheses is so far missing. In particular, the systematic mapping of synaptic weight distributions, and their relation to Hebbian preconditions has not been achieved yet in the hippocampal-entorhinal system. Here, we report such a systematic connectomic mapping of synaptic weight distributions and their relation to same-axon same-dendrite paired synaptic configurations across the hippocampal-entorhinal system. By analyzing millions of synapses and tens of thousands of paired synaptic configurations from 3D EM-based automated circuit reconstructions in hippocampal areas CA3, CA1, and layers 2 and 3 of the medial entorhinal cortex (MEC), we found systematic and unique synaptic weight distributions, with almost 50% (but not 100%) of synaptic weights in CA1 being in a Hebbian-consistent state, CA3 uniquely exhibiting small synaptic weights with indications of learned states, and MEC resembling previous data from other isocortices, with only up to 20% learned synaptic configurations. We further analyzed the sublayer-specificity of these weight distributions, finding the molecular layer of CA1 and the lower layers of CA3 being the most unique site of potential learning. Together, this data provides a first systematic synaptic weight analysis of the key neuronal system involved in memory formation in mammalian brains.