Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms

Abstract

The coupling of high frequency oscillations (HFOs; >100 Hz) and theta oscillations (3–12 Hz) in the CA1 region of rats increases during REM sleep, indicating that it may play a role in memory processing. However, it is unclear whether the CA1 region itself is capable of providing major contributions to the generation of HFOs, or if they are strictly driven through input projections. Parvalbumin-positive (PV+) interneurons may play an essential role in these oscillations due to their extensive connections with neighboring pyramidal cells, and their characteristic fast-spiking. Thus, we created mathematical network models to investigate the conditions under which networks of CA1 fast-spiking PV+ interneurons are capable of producing high frequency population rhythms. We used whole-cell patch clamp recordings of fast-spiking, PV+ cells in the CA1 region of an intact hippocampal preparation in vitro to derive cellular properties, from which we constrained an Izhikevich-type model. Novel, biologically constrained network models were constructed with these individual cell models, and we investigated networks across a range of experimentally determined excitatory inputs and inhibitory synaptic strengths. For each network, we determined network frequency and coherence. Network simulations produce coherent firing at high frequencies (>90 Hz) for parameter ranges in which PV-PV inhibitory synaptic conductances are necessarily small and external excitatory inputs are relatively large. Interestingly, our networks produce sharp transitions between random and coherent firing, and this sharpness is lost when connectivity is increased beyond biological estimates. Our work suggests that CA1 networks may be designed with mechanisms for quickly gating in and out of high frequency coherent population rhythms, which may be essential in the generation of nested theta/high frequency rhythms.

Modeling oscillatory dynamics in brain microcircuits as a way to help uncover neurological disease mechanisms: A proposal

“…To support our proposal described above, in this section we use our previously developed network models (Ferguson et al, 2013) using the experimental context of local circuitry in a whole hippocampus preparation. Linkages, including cellular and microcircuit levels are present and high frequency population rhythms were examined.…”

“…Based on the whole hippocampus experimental preparation, we have developed inhibitory network models with direct linkage at three levels with experiment (Ferguson et al, 2013). This is schematized in Figure 4 where green arrows depicting a linkage are shown.…”

Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

“…While these equations were initially developed to model the excitatory cortical pyramidal neuron, the properties of this neuron when g K s = 0 mS/cm 2 closely mirror those of fast-spiking Type I inhibitory interneurons, such as the parvalbumin positive (PV+) interneurons (Ferguson et al 2013), as well as interneurons containing an M-current blocked by ACh. When the inhibitory interneurons are modeled as Type II with g K s = 1.5 mS/cm 2 , their properties mirror those of interneurons like the oriens-lacunosum moleculare (OLM) and somatostatin-expressing (SOM) cells, which exhibit an active M-current when ACh concentrations are low (Saraga et al 2003;Lawrence et al 2006;Cutsuridis and Hasselmo 2012;Cutsuridis et al 2010;Perrenoud et al 2013;Markram et al 2004).…”

“…Strong intra-connectivity within the inhibitory cell population also plays a role in the PING mechanism by ensuring that the inhibitory population fires a single synchronous burst following excitatory input. While this intra-connectivity has an abundance of biological motivation (Ferguson et al 2013;Karson et al 2009;Tateno and Robinson 2007;Perrenoud et al 2013;Markram et al 2004;Mody and Pearce 2004), strong intra-connectivity is not strictly necessary for PING-like rhythms to arise (Rich et al 2017), making inter-connectivity between excitatory and inhibitory neurons the paramount aspect of network connectivity underlying PING.…”

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

“…Standardized models currently on OSB OSB currently hosts standardized curated models from multiple regions of the brain including neocortex 8,9,21,[33][34][35][36][37][38] , cerebellum 10,[39][40][41] , hippocampus 7,[42][43][44] and olfactory bulb 45 . These include single cell models from the Allen Institute Cell Types Database 19 and the Blue Brain Project 8 , which have been converted to NeuroML, to enable visualization and simulation on OSB.…”