Abstract
The hippocampal theta rhythm is crucial for memory and navigation. It is paced by parvalbumin-expressing GABAergic neurons in the medial septum. These neurons segregate into two subpopulations-Teevra and Komal cells-which fire in anti-phase bursts at theta frequency, with a stable phase shift of approximately 150 degrees, and exhibit mutual inhibition. While essential, the network mechanism enabling this stable anti-phase synchronization remains unresolved, as previous models relying on intrinsic neuronal adaptation or external rhythmic inputs conflict with experimental evidence. Here, we propose a novel computational model demonstrating that short-term synaptic depression (STD) within the reciprocal inhibitory circuit between these populations is sufficient to generate and stabilize the observed anti-phase theta oscillations. The model consists of two homogeneous populations of fast-spiking (Izhikevich model) neurons, connected via depressing synapses governed by Tsodyks-Markram dynamics. Using numerical simulation and optimization, we identify parameter regimes that robustly produce oscillations with the target 150-degree phase lag. Our analysis reveals that the balance of synaptic strengths is critical for initiating collective rhythmicity, while the time constants of neurotransmitter recovery ([Formula: see text]) and facilitation ([Formula: see text]) primarily control the precise phase relationship. The model reproduces key experimental features: sharp transitions to rhythmic states, frequency in the 4-5 Hz theta band, and independent control of oscillation amplitude and frequency by external excitatory drive and synaptic weights. The rhythm is stable over a wide parameter range, with frequency increasing with external drive but decreasing with stronger synaptic coupling. We conclude that STD provides a parsimonious and biologically plausible negative feedback mechanism to stabilize anti-phase pacing in the medial septum, effectively explaining the endogenous generation of hippocampal theta rhythm without requiring intrinsic pacemaking or phasic external inputs. This work highlights the fundamental role of network dynamics and short-term plasticity in producing critical brain rhythms.