Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
This work addresses the fundamental problem of sleep's role in memory and learning for neuroscience and AI, though it is incremental as it builds on existing models and mechanisms.
The study tackled the problem of understanding how sleep benefits cognitive tasks by showing that deep-sleep-like slow oscillations in a thalamo-cortical model improve visual classification of handwritten digits, resulting in higher performance in retrieval and classification tasks through synaptic homeostasis and memory association.
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.