Information coding in vasopressin neurons--the role of asynchronous bistable burst firing

Abstract

The task of the vasopressin system is homeostasis, a type of process which is fundamental to the brain's regulation of the body, exists in many different systems, and is vital to health and survival. Many illnesses are related to the dysfunction of homeostatic systems, including high blood pressure, obesity and diabetes. Beyond the vasopressin system's own importance, in regulating osmotic pressure, it presents an accessible model where we can learn how the features of homeostatic systems generally relate to their function, and potentially develop treatments. The vasopressin system is an important model system in neuroscience because it presents an accessible system in which to investigate the function and importance of, for example, dendritic release and burst firing, both of which are found in many systems of the brain. We have only recently begun to understand the contribution of dendritic release to neuronal function and information processing. Burst firing has most commonly been associated with rhythm generation; in this system it clearly plays a different role, still to be understood fully.

Fig. 1

Vasopressin cells project to the posterior pituitary. In response to osmotic input, cells fire in a distinct phasic bursting pattern. We can closely match this behaviour with a relatively simple single cell model.

Fig. 2

Vasopressin is synthesised in the cell body and transported to the release sites through a sequence of pools, with transport and release activity driven by spike triggered Ca²⁺ entry, and also possibly internal stores. The lower panels show in vitro data from Bicknell (1988). Stimulus-secretion coupling is non-linearly dependent on both spike rate (lower left) and burst duration (lower right). Per spike secretion is optimal at ~13 Hz, initially showing facilitation, before being limited by the releasable pool. The secretion rate also declines during prolonged bursts as the reserve pool is depleted.

Fig. 3

The ‘Forrester flywheel’ summarizes common problems in supply chains (Towill, 1996). In business, stock levels incur space and wastage costs so must be kept low; but if stocks run out, delays in restocking mean lost sales. In response to fluctuations in demand (synaptic input) the business can alter manufacture (synthesis) and moderate supply (secretion) by moderating price levels (stimulus-secretion coupling). Management needs to link manufacturing (synthesis) to sales (secretion); to link stock (stores) levels to a given variability of demand (expected variability of physiological challenge) for given delays in the system; and to ration supply by raising prices. The business must minimise the risks of losses associated with either running out of stock (hypernatraemia) or overstocking.

Fig. 4

The vasopressin cell population receives synaptic input from osmoreceptor neurons, and intercommunicates via dendritic release. Each cell body has its own release terminal where vesicles are released into the bloodstream. The lower panel shows varied plausible network topologies, making use of dendritic signals; a common population signal, overlapping local signal pools (similar to the oxytocin network of Rossoni et al., 2008), and the two combined.

Fig. 5

Three major post spike potentials, the HAP, the AHP and the DAP shape the cells’ electrical activity. The hazard, with model fitted to cell, shows how excitability changes post-spike, with shape determined by these post spike potentials (Sabatier et al., 2004). The large magnitude but fast decaying HAP generates the initial refractory period. The DAP generates the following peak in excitability which gradually falls to a plateau.

Fig. 6

Testing parameter sensitivity (post-spike excitability parameters). We tested each parameter with 10% changes starting with the fit in Table 1. Here we show the spiking pattern and hazard functions for the changes which showed the greatest variation based on the chi square fit measure comparing the model generated hazard function to the in vivo data. Each run uses the same randomly generated synaptic input. The control data uses the fitted Table 1 parameters.

Fig. 7

Testing parameter sensitivity (bursting mechanism parameters) as in Fig. 6. Changes in parameter D_cap, which corresponds to the maximal conductance of the slow DAP, show the greatest variation in the hazard function and spiking pattern, with a larger value causing a higher magnitude post-spike depolarisation and resulting longer burst duration.