Abstract :
[en] Neurons modify their connections based on experience; a property called synaptic plasticity. Synaptic plasticity rules rely on the respective activity of pre-synaptic and post-synaptic neurons to create functionally relevant connections. Simultaneously, brain information processing is shaped by fluctuations in neuronal rhythmic activities, each defining distinctive brain states. Switches in brain states during wake-sleep cycles are described at the network level, by a neuronal population shift from active to oscillatory state. At the cellular level, neurons switch from tonic to burst. Such switches are organized by neuromodulators. Altogether, sleep contributes to memory, a phenomenon called sleep-dependent memory consolidation. However, little is known about its underlying physiological processes.
Using a conductance-based model robust to neuromodulation and synaptic plasticity [Jacquerie,2021], we built a cortical network to study the evolution of synaptic weights during switches in brain states. We tested several types of synaptic plasticity rules such as triplet [Pfister,2006] and calcium-dependent models [Shouval,2002; Graupner,2016]. We reproduced experimental data acquired in wakefulness [Sjostrom,2001]. We found that a switch from tonic to burst alone, without any modification of the synaptic rule, results in a homeostatic reset. All synaptic weights converge towards the same basal value whatever the rule.
We demonstrated that neuromodulatory-mediated alteration in plasticity rules can be used to overcome this reset. For triplet models, the spike-time dependent curve is deformed as demonstrated in [Gonzalez-Ruedas,2018]. For calcium-based models, calcium thresholds and learning rates are neuromodulated. The neuromodulated-synaptic rules are shown to support the down-selection mechanism during sleep, avoiding the homeostatic reset.
