Abstract :
[en] Sleep contributes to memory through a mechanism called sleep-dependent memory consolidation. Little is known about its underlying physiological and functional mechanisms, but it involves global and long-lasting switches in neuronal population activity, called the wake-sleep pattern. At the network level, neuronal populations show transitions from active states to oscillatory states. Zooming in at the cellular level, neurons switch from tonic firing to bursting. In parallel, memory and learning rely on functional changes in neuronal connectivity through a process called synaptic plasticity.
The main goal of this project is to investigate how these transient switches in brain rhythmic activity, or brain states, affect synaptic plasticity and learning. To this end, we exploit recent advances in the mathematical modeling of cellular-induced fast and localized switches in brain states [Drion, 2018; Jacquerie, 2021]. These models make the generation of brain state switches independent from synaptic weights between neurons and thus compatible with synaptic plasticity. We investigate the interaction between switches in brain states and learning using an associative memory task, which quantifies the ability of neurons coding for concomitant events to strongly connect together.
To do so, we embed a neuron conductance-based model with a calcium-dependent plasticity rule in which internal calcium concentration governs the direction and the amplitude of the synaptic changes [Shouval, 2002; Graupner and Brunel, 2016], where internal calcium dynamics are regulated by the activation of NMDA receptors and voltage-gated calcium channels. This rule is parametrized to fit experimental data guaranteeing the correct behavior in wake state [Sjostrom, 2001].
The synaptic plasticity rule is shown to fail to reproduce sleep-dependent memory consolidation theories such as synaptic homeostasis hypothesis [Tononi,2020] or active system consolidation [Born, 2012] for any parameter values. Indeed, regardless of the connection strengths established during the day, they all converge to a fixe point during the night, which resets the circuit connectivity during sleep without showing any down-selection or reinforcement. Adding the effect of neuromodulators on calcium-dependent plasticity rule solves this problem by modifying calcium signal transduction pathways, hence the synaptic rule properties. The proposed model makes it therefore possible to track the evolution of synaptic wiring during sleep-wake switches, as well as to study sleep related mechanisms that can affect plasticity rules.
