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- Open Access
Distinct and competing interneuron populations can generate fast and slow gamma in oscillatory models of CA1
© Keeley et al. 2015
- Published: 4 December 2015
- Local Field Potential
- Gamma Oscillation
- Mutual Inhibition
- Inhibitory Population
- Bistable Regime
Gamma oscillations are widely observed in the mammalian brain and are important markers for cognition and attention [1, 2]. In CA1 of the hippocampus of freely moving rats, power in one of two distinct oscillatory bands in the gamma regime (fast gamma and slow gamma) is predominantly present at a given moment of time . Here, we demonstrate that models of networks with competing interneuron populations with different post-synaptic effects can create distinct oscillatory regimes that mimic the observed oscillations of CA1. Our network formulation reflects the following facts: 1) The duration of post-synaptic effect of an interneuron strongly influences the frequency in biophysical models of gamma oscillations . 2) The primary CA1 inputs from CA3 and the entorhinal cortex (EC) preferentially innervate interneurons of different subtype with different post-synaptic durations [5, 6].
We show that a firing rate model with competing interneuron populations with different post-synaptic time-constants is sufficient to generate slow and fast gamma oscillations. We conclude that mutual inhibition between the modeled interneuron populations permits switching in a bistable regime between distinct fast and slow gamma states. We also find similar behavior in spike-based network models. Our models explicitly predict the following about CA1: 1) Different interneurons innervated by different upstream regions phase-lock to different gamma states. 2) One population of interneurons is silenced, and another is active during fast and slow gamma events. 3) Mutual inhibition between interneuron populations is necessary for spontaneous switching of gamma state. Using experimental electrophysiological data from awake behaving rodents, we find interneurons that satisfy conditions 1 and 2, and we show putative 'fast' and 'slow' gamma interneurons categorized by their tendency to fire and phase-lock with oscillatory events as measured by a nearby local field potential.
Previous experimental work suggests these two gamma oscillations reflect different information processing modes in the learning and memory system . Our models provide a mechanistic understanding of these modes and posit a new oscillatory role for distinct interneurons in CA1. Moreover, our models describe general oscillatory behavior in networks with distinct interneuron populations.
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