Particle-Swarm Based Modelling Reveals Two Distinct Classes of CRHPVN Neurons

Abstract

AbstractElectrophysiological recordings can provide detailed information of single neurons’ dynamical features and shed light into their response to stimuli. Unfortunately, rapidly modeling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modeled single neuron dynamics lead to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropinreleasing 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 current levels, fixed points, and bifurcations, were shown to be stable across fits. We found that CRHPVN neurons can be divided into two sub-types according to their bifurcation at the onset of firing: saddles (integrators) and sub-critical Hopf (resonators). We constructed networks of these fit 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 PointsCorticotropin-releasing hormone neurons (CRHPVN) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response.We developed a rapid computational modeling platform that uses Particle-Swarm Optimization to rapidly and accurately fit biophysical neuron models.A model was fit to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting procedures. Any neuron undergoing standard current clamping for a few minutes can be fit by this procedureThe dynamical analysis of the modeled neurons shows thatCRHPVN comes in two specific ‘flavours’: CRHPVN-resonators and CRHPVN-integrators.Network simulations show thatCRHPVN-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.