Learning and sleep in a thalamo-cortical multi-area model

Wakefulness and sleep are brain-states that are essential for cognitive performances. During wakefulness, our perceptual system is continuously subjected to sensory inputs from different sources and modalities. The involved brain areas process the input in a framework set by previous knowledge (acquired through individual and evolutionary experience) with a crucial role played by the exchange of signals with other brain areas. The ability of the brain to integrate and segregate this information by building a coherent and complete representation of the environment is impressive. Despite there is plenty of empirical evidence suggesting that the nervous system uses a statistically optimal approach in combining external information, little is known about how the brain implements these strategies. Moreover, recent studies have shown that sleep plays a central role in storing and reorganizing information gained while awake and in the optimization of the energetic post-sleeping rates. 

Starting from these results and a recent simplified single area thalamo-cortical model, our work focuses on two main issues. First, we aim to simulate the ability of the awake brain to combine different kinds of information, throughout multisensory perception, with contextual information starting from the case of the integration of the two visual hemicampi that in the brain are processed by areas placed in two different hemispheres. Second, we study the beneficial effects of a deep-sleep-like biologically plausible slow oscillation activity on the classification accuracy.

In summary, in this work, we create a simplified thalamo-cortical multi-area simulation model trained to learn, sleep and perform a classification task on handwritten digits.