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Network properties of a computational model of the dorsal raphe nucleus

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wong-Lin, KongFatt, Joshi, Alok, Prasad, Girijesh and McGinnity, Martin (2012) Network properties of a computational model of the dorsal raphe nucleus. Neural Networks, 32 . pp. 15-25. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S089360801200038X

DOI: 10.1016/j.neunet.2012.02.009

Abstract

Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.

Item Type:Journal article
Keywords:Spiking neuronal network model Serotonin neurons Inhibitory fast-spiking non-serotonergic neurons Reward-based memory-guided decision task Theta rhythm
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21301
Deposited By:Dr Kongfatt Wong-Lin
Deposited On:07 Mar 2012 09:11
Last Modified:07 Apr 2014 10:09

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