Attentional effects on local V1 microcircuits explain selective V1-V4 communication

Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modelled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.

Dynamic Causal Modelling of Hierarchical Planning

Hierarchical planning (HP) is a strategy that optimizes the planning by storing the steps towards the goal (lower-level planning) into subgoals (higher-level planning). In the framework of model-based reinforcement learning, HP requires the computation through the transition value between higher-level hierarchies. Previous study identified the dmPFC, PMC and SPL were involved in the computation process of HP respectively. However, it is still unclear about how these regions interaction with each other to support the computation in HP, which could deepen our understanding about the implementation of plan algorithm in hierarchical environment. To address this question, we conducted an fMRI experiment using a virtual subway navigation task. We identified the activity of the dmPFC, premotor cortex (PMC) and superior parietal lobe (SPL) with general linear model (GLM) in HP. Then, Dynamic Causal Modelling (DCM) was performed to quantify the influence of the higher- and lower-planning on the connectivity between the brain areas identified by the GLM. The strongest modulation effect of the higher-level planning was found on the dmPFC→right PMC connection. Furthermore, using Parametric Empirical Bayes (PEB), we found the modulation of higher-level planning on the dmPFC→right PMC and right PMC→SPL connections could explain the individual difference of the response time. We conclude that the dmPFC-related connectivity takes the response to the higher-level planning, while the PMC acts as the bridge between the higher-level planning to behavior outcome.

Increased prefrontal top-down control in older adults predicts motor performance and age-group association

Bimanual motor control declines during ageing, affecting the ability of older adults to maintain independence. An important underlying factor is cortical atrophy, particularly affecting frontal and parietal areas in older adults. As these regions and their interplay are highly involved in bimanual motor preparation, we investigated age-related connectivity changes between prefrontal and premotor areas of young and older adults during the preparatory phase of complex bimanual movements using high-density electroencephalography. Generative modelling showed that excitatory inter-hemispheric prefrontal to premotor coupling in older adults predicted age-group affiliation and was associated with poor motor-performance. In contrast, excitatory intra-hemispheric prefrontal to premotor coupling enabled older adults to maintain motor-performance at the cost of lower movement speed. Our results disentangle the complex interplay in the prefrontal-premotor network during movement preparation underlying reduced bimanual control and the well-known speed-accuracy trade-off seen in older adults.

The role of Hebbian learning in human perception: a methodological and theoretical review of the human Visual Long-Term Potentiation paradigm

Long-term potentiation (LTP) is one of the most widely studied forms of neural plasticity, and is thought to be the principle mechanism underlying long-term memory and learning in the brain. Sensory paradigms utilising electroencephalography (EEG) and sensory stimulation to induce LTP have allowed translation from rodent and primate invasive research to non-invasive human investigations. This review focusses on visual sensory LTP induced using repetitive visual stimulation, resulting in changes in the visually evoked response recorded at the scalp with EEG. Across 15 years of use and replication in humans several major paradigm variants for eliciting visual LTP have emerged. The application of different paradigms, and the broad implementation of visual LTP across different populations combines to provide a rich and sensitive account of Hebbian LTP, and potentially non-Hebbian plasticity mechanisms. This review will conclude with a discussion of how these findings have advanced existing theories of perceptual learning by positioning Hebbian learning both alongside and within other major theories such as Predictive Coding and The Free Energy Principle.