CNS*2026 Halifax

Introduction



Neural oscillations in the hippocampus at theta and gamma bands are believed to support temporal coding in memory and learning [2]. We hypothesize that intrinsic theta resonance in hippocampal CA1 pyramidal neurons facilitate frequency-selective enhancement and phase locking required for precise phase coding. Through computational (biophysically plausible) network models and Simulation Based Inference (SBI) [1], we examine how intrinsic and synaptic dynamics  influence the transition between firing patterns which shape oscillatory synchronization. Our results link biophysical parameters to phase locked firing in the theta and gamma frequency ranges to provide mechanistic insight into underlying temporal coding in hippocampal circuits. 



Methods



We model a simplified hippocampal network of neurons consisting of one excitatory population and two inhibitory populations of neurons. The inhibitory populations consist of slow spiking and fast spiking interneurons. We include key ionic currents (e.g. H-, M-, persistent Na+) and synaptic connections to capture spiking mode transitions and resonant dynamics. To estimate the underlying biophysical parameters we apply SBI, a Bayesian approach used to infer parameter distributions from voltage data using neural density estimators. This method allows us to capture the underlying mechanisms governing the formation of neuronal oscillations, degeneracies, and temporal coding.


Results



Our simulations show that intrinsic theta resonant currents are sufficient for enhancing spike timing and frequency selective phase locking in CA1 neurons. Phase locking analysis reveals a pronounced peak in phase locking value (PLV) near theta resonance, which is diminished with the removal of resonant currents. In addition our results demonstrate that intrinsic theta resonance is able to recruit fast spiking inhibitory neurons in order to modulate phase locking in the gamma ranges. Finally, our results show that by tuning resonant currents the phase at which neurons lock to can be modulated.



Discussion



Synaptic connections are capable of setting temporal windows where excitatory neurons can fire which on its own can produce weak phase locking. However, precise phase coding requires millisecond-scale spike timing that synaptic connections on their own cannot account for due to variability and noise. Intrinsic resonance is therefore required to stabilize spike timing, amplify responses at preferred frequencies, and promote robustness in frequency and phase specific domains. By tuning intrinsic resonant currents we are able to show robust phase locking in both theta and gamma ranges. Therefore, network connection sets firing windows, while resonance determines spike strength and phase by adaptively recruiting network activity. 



References

References 

1. Boelts, J., et al (2022). Flexible and efficient simulation-based inference for models of decision-making. eLife, 11, e77220. https://doi.org/10.7554/eLife.77220
   
2. Lowet, E., et al. (2023). Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation. Cell Reports, 42(8), 112906. https://doi.org/10.1016/j.celrep.2023.112906
   
3. Rotstein, H. G., et al (2005). Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network. Journal of Neurophysiology, 94(2), 1509–1518. https://doi.org/10.1152/jn.00957.2004
   

Acknowledgement
Acknowledgements: NSF IOS-2002863 (HGR)