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Modeling dopaminergic modulation of clustered gamma rhythms

Abstract : Gamma rhythm (20–100Hz) plays a key role in numerous cognitive tasks: working memory, sensory processing and in routing of information across neural circuits. Incomparison with lower frequency oscillations in the brain, gamma-rhythm associated firing of the individual neurons is sparse and the activity is locally distributed in the cortex. Such “weak” gamma rhythm results from synchronous firing of pyramidal neurons in an interplay with the local inhibitory interneurons in a "pyramidal-interneuron gamma" or PING. Experimental evidence shows that individual pyramidal neurons during such oscillations tend to fire at rates below gamma, with the population showing clear gamma oscillations and synchrony. One possible way to describe such features is that this gamma oscillation is generated within local synchronous neuronal clusters. The number of such synchronous clusters defines the overall coherence of the rhythm and its spatial structure. The number of clusters in turn depends on the properties of the synaptic coupling and the intrinsic properties of the constituent neurons. We previously showed that a slow spike frequency adaptation current in the pyramidal neurons can effectively control cluster numbers. These slow adaptation currents are modulated by endogenous brain neuromodulators such as dopamine, whose level is in turn related to cognitive task requirements. Hence we postulate that dopaminergic modulation can effectively control the clustering of weak gamma and its coherence. In this paper we study how dopaminergic modulation of the network and cell properties impacts the cluster formation process in a PING network model.
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https://hal.inria.fr/hal-02410040
Contributor : Martin Krupa <>
Submitted on : Friday, December 13, 2019 - 4:25:14 PM
Last modification on : Wednesday, January 6, 2021 - 10:34:01 AM

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Denis Zakharov, Martin Krupa, Boris Gutkin. Modeling dopaminergic modulation of clustered gamma rhythms. Communications in Nonlinear Science and Numerical Simulation, Elsevier, 2020, 82, pp.105086. ⟨10.1016/j.cnsns.2019.105086⟩. ⟨hal-02410040⟩

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