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Journal Articles Frontiers in Computational Neuroscience Year : 2012

Brain state-dependent neuronal computation

Abstract

Neuronal firing pattern, which includes both the frequency and the timing of action potentials, is a key component of information processing in the brain. Although the relationship between neuronal output (the firing pattern) and function (during a task/behavior) is not fully understood, there is now considerable evidence that a given neuron can show very different firing patterns according to brain state. Thus, such neurons assembled into neuronal networks generate different rhythms (e.g., theta, gamma and sharp wave ripples), which sign specific brain states (e.g., learning, sleep). This implies that a given neuronal network, defined by its hard-wired physical connectivity, can support different brain state-dependent activities through the modulation of its functional connectivity. Here, we review data demonstrating that not only the firing pattern, but also the functional connections between neurons, can change dynamically. We then explore the possible mechanisms of such versatility, focusing on the intrinsic properties of neurons and the properties of the synapses they establish, and how they can be modified by neuromodulators, i.e., the different ways that neurons can use to switch from one mode of communication to the other.

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Neurobiology
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hal-03582709 , version 1 (21-02-2022)

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Pascale P. Quilichini, Christophe Bernard. Brain state-dependent neuronal computation. Frontiers in Computational Neuroscience, 2012, 6, ⟨10.3389/fncom.2012.00077⟩. ⟨hal-03582709⟩
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