Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

Human cognition recruits diverse neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multi-task representations across human cortex using functional MRI during 26 cognitive tasks in the same subjects. We measured the representational similarity across tasks within a region, and the alignment of representations between regions. We found a cortical topography of representational alignment following a hierarchical sensory-association-motor gradient, revealing compression-then-expansion of multi-task dimensionality along this gradient. To investigate computational principles of multi-task representations, we trained multi-layer neural network models to transform empirical visual to motor representations. Compression-then-expansion organization in models emerged exclusively in a training regime where internal representations are highly optimized for sensory-to-motor transformation, and not under generic signal propagation. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multi-task representations across human cortex to support flexible cognition.

Continuity within somatosensory cortical map shapes the integration of optogenetic input

The topographic organization of sensory cortices is a prominent feature, but its functional role remains unclear. Particularly, how activity is integrated within a cortical area depending on its topography is unknown. Here, we trained mice expressing channelrhodopsin in cortical excitatory neurons to track a bar photostimulation that rotated smoothly over the primary somatosensory cortex (S1). When photostimulation was aimed at vS1, the area which contains a contiguous representation of the whisker array at the periphery, mice could learn to discriminate angular positions of the bar to obtain a reward. In contrast, they could not learn the task when the photostimulation was aimed at the representation of the trunk and legs in S1, where neighboring zones represent distant peripheral body parts, introducing discontinuities. Mice demonstrated anticipation of reward availability, specifically when cortical topography enabled to predict future sensory activation. These results are particularly helpful for designing efficient cortical sensory neuroprostheses.

Is Sleep Essential for Neural Plasticity in Humans, and How Does It Affect Motor and Cognitive Recovery?

There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the “synaptic homeostasis hypothesis” and the “consolidation” hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.