Barcelona Computational, Cognitive and Systems Neuroscience (BARCCSYN) 2021

Speech is a very complex auditory stimulation. When trying to decode it to learn the Language conveyed in it a learner needs to compute the statistic relations between the specific sounds in this input. However it is also necessary at one point of learning to ignore these specific sounds in order to be able to create more abstract knowledge about classes of words (i.e. nouns, verbs, etc.) and their relations to be able to generalise and be fluent in a Language. In the talk I will review research providing evidence of how different aspects of attention from automatic capture to the ability highlight or ignore relevant information are essential in these different stages of learning. In this endeavour I will propose that the existence of concurrent cues in the statistic relations between elements are essential to be able to guide attention to generate abstract knowledge. I will also illustrate how different brain networks need to work in coordination at different stages for this learning to occur.

The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. We showed that the coupling between each brain region and the rest of the cortex provides an efficient statistic to classify the two brain states. Based on this and other statistics, we estimated maximum entropy models to derive collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, which were all maximized in the awake state. The differences in these properties were consistent with a phase transition from critical dynamics in the awake state to supercritical dynamics in the anesthetized state. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in the state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

Recent data suggest that preverbal infants possess remarkable capacities to process complex events and modify hypotheses about them facing inconsistent evidence.  I will present data about the behavioral and neural correlates of some of these processes in infants and adults, focusing on elementary logical inferences. I will critically discuss various proposals about the mental representations underlying these capacities.