A stochastic model for interacting neurons in the olfactory bulb

• Published in: BioSystems 2019-11, Vol.185, p.104030-104030, Article 104030
• Main Authors: Ascione, G., Carfora, M.F., Pirozzi, E.
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Algorithms; Animals; Computer Simulation; Coupled stochastic differential equations; Excitatory Postsynaptic Potentials - physiology; First spiking time probability density; Gauss-Markov processes; Markov Chains; Models, Neurological; Modified Leaky Integrate-and-Fire model; Monte Carlo Method; Nerve Net - physiology; Olfactory Bulb - cytology; Olfactory Bulb - physiology; Olfactory Receptor Neurons - physiology; Stochastic Processes
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2019.104030

We focus on interacting neurons organized in a block-layered network devoted to the information processing from the sensory system to the brain. Specifically, we consider the firing activity of olfactory sensory neurons, periglomerular, granule and mitral cells in the context of the neuronal activity of the olfactory bulb. We propose and investigate a stochastic model of a layered and modular network to describe the dynamic behavior of each prototypical neuron, taking into account both its role (excitatory/inhibitory) and its location within the network. We adopt specific Gauss-Markov processes suitable to provide reliable estimates of the firing activity of the different neurons, given their linkages. Furthermore, we study the impact of selective excitation/inhibition on the information transmission by means of simulations and numerical estimates obtained through a Volterra integral approach.