Sleep-like slow oscillations induce hierarchical memory association and synaptic homeostasis in thalamo-cortical simulations

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 theoretical and computational approach demonstrating the 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. If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed. It is characterized by both a specific 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 is reflected in a hierarchical organization of post-sleep internal representations. Such effects favour higher performance in retrieval and classification tasks and create hierarchies of categories in integrated representations. The model leverages on the coincidence of top-down contextual information with bottom-up sensory flow during the training phase and on the integration of top-down predictions and bottom-up thalamo-cortical pathways during deep-sleep-like slow oscillations. Also, such mechanism hints at possible applications to artificial learning systems. Author Summary A simplified thalamo-cortical model is trained with examples drawn from the MNIST set of handwritten digits, while receiving simultaneous bottom-up sensory stimulation and selective lateral sub-threshold activation. During a training phase, spike-timing-dependent-plasticity (STDP) sculptures a pre-sleep synaptic matrix which associates training examples with well separated groups of cortical neurons and creates top-down synapses toward the thalamus. Then, the network is induced to produce deep-sleep-like slow oscillations (SO), while being disconnected from sensory and lateral stimuli and driven by its internal activity. During sleep up-states, thalamic cells are activated by top-down predictive stimuli produced by cortical neural groups and respond with a forward feedback, recruiting other cortical neurons. This induces two simultaneous and beneficial effects: a reduction in the strength of synapses inside the groups created by the training and an increase of synapses that associate examples of characters belonging to the same digit category. The fine structure of the synaptic matrix reflects the categorization of examples and a hierarchical structure within each category. During post-sleep retrieval activity the correlation between neurons reflects the richer hierarchical structure of the underlying synaptic matrix.