Biology subjects

Alamia, A.

Publications and source records attributed to Alamia, A..

21 records · Page 2Linked to original sources

bioRxiv2020

Turning the stimulus on and off dynamically changes the direction of alpha travelling waves

Traveling waves have been studied to characterize the complex spatiotemporal dynamics of the brain. Several studies have suggested that the propagation direction of alpha traveling waves can be task-dependent. For example, a recent EEG study from our group found that forward waves (i.e. occipital to frontal, FW waves) were observed during visual processing, whereas backward waves (i.e. frontal to occipital, BW waves) mostly occurred in the absence of sensory input. These EEG recordings, however, were obtained from different experimental sessions and different groups of subjects. To further examine how the waves direction changes between task conditions, 13 participants were tested on a target detection task while EEG signals were recorded simultaneously. We alternated visual stimulation (5 s display of visual luminance sequences) and resting state (5 s of black screen) within each single trial, allowing us to monitor the moment-to-moment progression of traveling waves. As expected, the direction of alpha waves was closely linked with task conditions. First, FW waves from occipital to frontal regions, absent during rest, emerged as a result of visual processing, while BW waves in the opposite direction dominated in the absence of visual inputs, and were reduced (but not eliminated) by external visual inputs. Second, during visual stimulation (but not rest), both waves coexisted on average, but were negatively correlated. In summary, we conclude that the functional role of alpha traveling waves is closely related with their propagating direction, with stimulus-evoked FW waves supporting visual processing and spontaneous BW waves involved more in top-down control.

neuroscience↗

bioRxiv2019

Differential involvement of EEG oscillatory components in sameness vs. spatial-relation visual reasoning tasks

The development of deep convolutional networks (DCNs) has recently led to great successes in computer vision and have become de facto computational models of vision. However, a growing body of work suggests that they exhibit critical limitations beyond image categorization. Here, we study a fundamental limitation of DCNs for judging whether two items are the same or different (SD) compared to a baseline assessment of their spatial relationship (SR). We test the prediction that SD tasks recruit additional cortical mechanisms which underlie critical aspects of visual cognition that are not explained by current computational models. We thus recorded EEG signals from 14 participants engaged in the same tasks as the computational models. Importantly, the two tasks were matched in terms of difficulty by an adaptive psychometric procedure: yet, on top of a modulation of evoked potentials, our results revealed higher activity in the low beta (13-20Hz) band in the SD compared to the SR conditions, which we surmise as reflecting the crucial involvement of recurrent mechanisms sustaining working memory and attention. Author SummaryDespite the impressive progress of deep convolutional networks (DCNs) in object recognition, recent studies demonstrated that state-of-the-art vision algorithms encounter severe limitations when performing certain visual reasoning tasks: for instance, convolutional networks can easily solve problems involving spatial relations, but fail in identifying whether two items are identical or different (same-different task). This conclusion led us to test the hypothesis that different computational mechanisms are needed to successfully perform these tasks also in the visual system. First, we confirmed in our simulations that DCNs can successfully perform spatial relationship tasks but struggle with same-different ones. Then, we tested 14 participants on the same experimental design while recording their EEG signals. Remarkably, our results revealed a significant difference between the tasks in the occipital brain regions both in evoked potentials and in the oscillatory dynamics. Specifically, an increase of activity was found when performing the SD over the SR condition. We interpret these results as reflecting the fundamental involvement of recurrent mechanisms implementing cognitive functions such as working memory and attention.

neuroscience↗

bioRxiv2018

Alpha oscillations and travelling waves: signatures of predictive coding?

Predictive coding is a key mechanism to understand the computational processes underlying brain functioning: in a hierarchical network, higher layers predict the activity of lower layers, and the unexplained residuals (i.e. prediction errors) are sent through. Because of its iterative nature, we wondered whether predictive coding could be related to brain oscillatory dynamics. First, we show that a simple 2-layers predictive coding model of visual cortex, with physiological communication delays between layers, naturally gives rise to alpha-band rhythms, similar to experimental observations. Then, we demonstrate that a multi-layer version of the same model can explain the occurrence of oscillatory travelling waves across layers, both feedforward (during visual stimulation) and backward (during rest). Remarkably, the predictions of our model are matched by the analysis of two independent EEG datasets, in which we observed oscillatory travelling waves in both directions.

neuroscience↗