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Garagnani, M.

Publications and source records attributed to Garagnani, M..

2 recordsLinked to original sources

Attention reshapes the information dynamics of thalamic and cortical prediction-error learning

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

neuroscience↗

Simulating semantic dementia in a brain-constrained model of action and object words learning

The nature of semantic knowledge - conceptual information stored in the brain - is highly debated in the field of cognitive science. Experimental and clinical data specify various cortical regions involved in the processing of meaning. Those include semantic hubs that take part in semantic processing in general as well as sensorimotor areas that process specific conceptual categories according to their modality. Biologically inspired neurocomputational models can help adjudicate between different theories about the exact roles of those regions in the functioning of the semantic system. Here, we used an existing neuroanatomically constrained model of frontotemporal brain areas implicated in language acquisition and grounding. We adapted it to replicate and explain the effects of semantic dementia on word processing abilities. Semantic dementia is a disease characterized by semantic knowledge deterioration that correlates with neural damage in the anterior temporal lobe. The behavior of our model is in accordance with clinical data - namely, word recognition performance decreases as SD lesions progress, whereas word repetition abilities remain preserved, or are less affected. Furthermore, our model makes novel predictions about category-specific effects of SD - namely, our simulation results indicate that word processing should be more impaired for object-than for action-related words, and that white matter degradation should lead to more severe consequences than the same proportion of grey matter degradation. The present results provide a mechanistic, cortical-level explanatory account of a range of language impairments as observed in clinical populations during the onset and progress of semantic dementia.

neuroscience↗