Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Cognitive neuroscience

Background:

• Hippocampal ripples are neural oscillations vital for memory consolidation and planning.
• Existing computational models for ripple generation lack consensus on the primary pacemaker mechanism (excitation vs. inhibition).
• Intra-ripple frequency accommodation (IFA), a frequency decay within a ripple event, is an experimentally observed feature not fully explained by current models.

Purpose of the Study:

• To elucidate the underlying mechanism of intra-ripple frequency accommodation (IFA) in hippocampal ripples.
• To investigate the role of feedback-based inhibition-first models in reproducing IFA.
• To identify critical parameters influencing IFA dynamics.

Main Methods:

• Analytical mean-field approach applied to neural network dynamics.
• Numerical simulations of leaky integrate-and-fire spiking networks.
• Development of a drift-based approximation for population rate and interneuron mean membrane potential dynamics.

Main Results:

• IFA is reproduced by a feedback-based inhibition-first model relying on delayed inhibitory synaptic coupling.
• The speed of excitatory drive change is critical for IFA emergence.
• IFA arises from a speed-dependent hysteresis effect in interneuron membrane potential dynamics during transient excitation.

Conclusions:

• The study provides a mechanistic explanation for IFA in hippocampal ripples.
• IFA is a robust feature of inhibition-first ripple models, dependent on the dynamics of excitatory drive.
• The findings predict that IFA asymmetry diminishes with slow drive changes but persists otherwise.