Sokolov, Y. | Search bioRxiv

bioRxiv2023

Emergent effects of synaptic connectivity on cortical sleep slow wave amplitude, density and propagation in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. Author SummarySlow Wave Sleep (SWS) is a primary brain state displayed during Non-Rapid Eye Movement (NREM) sleep. While previously thought of as homogenous waves of activity that sweep across the entire brain, modern research has suggested a more nuanced pattern of activity that can vary between local and global slow wave activity. However, understanding how these states emerge from small scale neuronal dynamics and network connectivity remains unclear. We developed a biophysically realistic model of the human brain capable of generating SWS-like behavior, and investigated the role of connectivity in the spatio-temporal dynamics of these slow waves. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network behavior - specifically, models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. These results were compared to human data, and we found that models producing mixed states provided the best match to the network behavior and functional connectivity of human subject data. These findings shed light on how the spatio-temporal properties of SWS emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

neuroscience↗

bioRxiv2019

Can sleep protect memories from catastrophic forgetting?

Continual learning remains to be an unsolved problem in artificial neural networks. Biological systems have evolved mechanisms by which they can prevent catastrophic forgetting of old knowledge during new training and allow lifelong learning. Building upon data suggesting the importance of sleep in learning and memory, here we test a hypothesis that sleep protects memories from catastrophic forgetting. We found that training in a thalamocortical network model of a \"new\" memory that interferes with previously stored \"old\" memory may result in degradation and forgetting of the old memory trace. Simulating NREM sleep immediately after new learning leads to replay, which reverses the damage and ultimately enhances both old and new memory traces. Surprisingly, we found that sleep replay goes beyond recovering old memory traces that were damaged by new learning. When a new memory competes for the neuronal/synaptic resources previously allocated to the old memory, sleep replay changes the synaptic footprint of the old memory trace to allow for the overlapping populations of neurons to store multiple memories. Different neurons become preferentially supporting different memory traces to allow successful recall. We compared synaptic weight dynamics during sleep replay with that during interleaved training - a common approach to overcome catastrophic forgetting in artificial networks - and found that interleaved training promotes synaptic competition and weakening of reciprocal synapses, effectively reducing an ensemble of neurons contributing to memory recall. This leads to suboptimal recall performance compared to that after sleep. Together, our results suggest that sleep provides a powerful mechanism to achieve continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize memory interference.

neuroscience↗