Computational modeling of spike generation in serotonergic neurons of the dorsal raphe nucleu

We consider here a single-compartment model of these neurons which is capable of describing many of the known features of spike generation, particularly the slow rhythmic pacemaking activity often observed in these cells in a variety of species. Included in the model are ten kinds of voltage dependent ion channels as well as calcium-dependent potassium current. Calcium dynamics includes buffering and pumping. In sections 3-9, each component is considered in detail and parameters estimated from voltage clamp data where possible. In the next two sections simplified versions of some components are employed to explore the effects of various parameters on spiking, using a systematic approach, ending up with the following eleven components: a fast sodium current $I_{Na}$, a delayed rectifier potassium current $I_{KDR}$, a transient potassium current $I_A$, a low-threshold calcium current $I_T$, two high threshold calcium currents $I_L$ and $I_N$, small and large conductance potassium currents $I_{SK}$ and $I_{BK}$, a hyperpolarization-activated cation current $I_H$, a leak current $I_{Leak}$ and intracellular calcium ion concentration $Ca_i$. Attention is focused on the properties usually associated with these neurons, particularly long duration of action potential, pacemaker-like spiking and the ramp-like return to threshold after a spike. In some cases the membrane potential trajectories display doublets or have kinks or notches as have been reported in some experimental studies. The computed time courses of $I_A$ and $I_T$ during the interspike interval support the generally held view of a competition between them in influencing the frequency of spiking. Spontaneous spiking could be obtained with small changes in a few parameters from their values with driven spiking.Comment: The abstract has been truncate

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin

An investigation into serotonergic and environmental interventions against depression in a simulated delayed reward paradigm

The disruption of the serotonergic (5HT) system has been implicated in causing major depression and the standard view is that a lack of serotonin is to blame for the resulting symptoms. Consequently, pharmacological interventions aim to increase serotonin concentration in its target areas or stimulating excitatory 5HT receptors. A standard approach is to use serotonin reuptake inhibitors (SSRIs) which cause a higher accumulation of serotonin. Another approach is to stimulate excitatory serotonin receptors with psychedelic drugs. This article compares these two approaches by first setting up a system-level limbic system model of the relevant brain areas and then modelling a delayed reward paradigm which is known to be disrupted by a lack of 5HT. Central to our model is how serotonin changes the response characteristics of decision-making neurons where low levels of 5HT allow small signals to pass through, whereas high levels of 5HT create a barrier for smaller signals but amplifying the larger ones. We show with both standard behavioural simulations and model checking that SSRIs perform significantly better against interventions with psychedelics. However, psychedelics might work better in other paradigms where a high level of exploration is beneficial to obtain rewards

An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies