Particle-swarm based modelling reveals two distinct classes of CRHPVN neurons.

Abstract

Electrophysiological recordings can provide detailed information of single neurons' dynamical features and shed light on their response to stimuli. Unfortunately, rapidly modelling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modelled single neuron dynamics leads to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropin releasing hormone neurons from the PVN (CRHPVN ) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified adaptive exponential integrate-and-fire model (AdEx) with particle swarm optimization (PSO). All CRHPVN neurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were found that reliably reproduced current-clamp traces for any single neuron. Despite multiple solutions, the dynamical features of the models such as the rheobase, fixed points, and bifurcations, were shown to be stable across fits. We found that CRHPVN neurons can be divided into two subtypes according to their bifurcation at the onset of firing: CRHPVN -integrators and CRHPVN -resonators. The existence of CRHPVN -resonators was then directly confirmed in a follow-up patch-clamp hyperpolarization protocol which readily induced post-inhibitory rebound spiking in 33% of patched neurons. We constructed networks of CRHPVN model neurons to investigate the network level responses of CRHPVN neurons. We found that CRHPVN -resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRHPVN neurons may be critical to maintaining a baseline firing tone in the PVN. KEY POINTS: Corticotropin-releasing hormone neurons (CRHPVN ) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response. We developed a computational modelling platform that uses particle swarm optimization to rapidly and accurately fit biophysical neuron models to patched CRHPVN neurons. A model was fitted to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting algorithms. Any neuron undergoing standard current clamp step protocols for a few minutes can be fitted by this procedure The dynamical analysis of the modelled neurons shows that CRHPVN neurons come in two specific 'flavours': CRHPVN -resonators and CRHPVN -integrators. We directly confirmed the existence of these two classes of CRHPVN neurons in subsequent experiments. Network simulations show that CRHPVN -resonators are critical to retaining the baseline firing rate of the entire network of CRHPVN neurons as these cells can fire rebound spikes and bursts in the presence of strong inhibitory synaptic input.