CA1 DYNAMICS UNDER CA3-DRIVEN INPUT IN A LARGE-SCALE MOUSE HIPPOCAMPAL MODEL

Understanding the properties of biological neuronal networks requires detailed use of morpho-anatomical computational models of brain microcircuits.Here we present a mouse CA3-CA1 hippocampal circuit to investigate the emergence of CA1 network dynamics under realistic CA3 pattern stimulations.
The CA3–CA1 network was built as a full-scale network of point-neuron models generated by using a data-driven morpho-anatomical strategy (Gandolfi et al., SciRep,2022). Starting from the CA1 construction pipeline, we extended the same framework to generate CA3 and implemented the Schaffer collateral projections to establish the connectivity between CA3 and CA1 thus forming the CA3–CA1 microcircuit. Synaptic transmission was modeled with a custom NESTML synapse including short-term dynamics and long-term STDP (Mishra et al., Nat Commun,2016). The CA1 activity was evoked and quantified by multiple CA3 realistic input patterns spanning different temporal structures and spatial recruitment levels. In parallel, the CA3 functional properties emerging from autoassociative patterns were investigated by applying theta–gamma training and testing degraded inputs across different assembly sizes. Moreover the underlying network structure was characterized by using graph-theoretical measures.
Realistic input patterns evoked in CA3 elicited responses in CA1, revealing differences in neuronal recruitment and sparseness across stimulation regimes. In particular, CA1 responses to autoassociative retrieval in CA3 from degraded inputs were related to structural network properties of both CA3 and CA1 characterized through graph theory analysis.
This work provides a biologically grounded computational framework to investigate how autoassociative activity in CA3 shapes downstream CA1 dynamics under realistic patterned inputs in a large-scale hippocampal circuit model.