Intrinsic network dynamics of the hippocampal CA1 area using a data-driven full-scale model

Understanding the intrinsic dynamics of individual brain regions is essential for explaining how they transform incoming signals into coherent outputs. However, isolating their intrinsic behaviors experimentally remains a major challenge, particularly for areas, such as the hippocampus, that do not receive direct sensory inputs and therefore cannot be driven or perturbed in a controlled manner through peripheral stimulation. There is thus a lack of systematic insight into the baseline operating regime of the hippocampal circuitry and the internal mechanisms—cellular heterogeneity, recurrent dynamics, and inhibitory control—that shape its computational properties even before external inputs arrive. Mathematical and computational models play a crucial role in reducing these problems, offering a convenient way to probe the autonomous behavior of these circuits and to study conditions that cannot be examined in vivo or in vitro. To address this challenge, here we introduce a data-driven full scale spiking-neuron model of the mouse hippocampal CA1 area, designed to investigate intrinsic CA1 dynamics using connectivity, firing, and synaptic properties calibrated on electrophysiological recordings to reproduce the experimentally observed heterogeneity of cellular connectivity and excitability. Using this framework, we show how intrinsic neuronal diversity strongly influences the spatiotemporal activation patterns of the circuit and how inhibitory control plays a critical role in structuring information flow within CA1. A key finding is that excitatory neurons exhibit a preferred inter-spike interval in the theta range, which persists even in the absence of inhibition. Consistently with experimental evidence in vitro, the results suggest that the CA1 network contains intrinsic and locally recurrent dynamics that can support the emergence of theta oscillations independently from external drive. We further show that inhibitory interneurons impose scale-dependent activation across the network, highlighting their essential role in refining local dynamics and shaping input–output transformations of hippocampal circuits.