Semi-Supervised Multimodal Representation Learning Through a Global Workspace.

Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations or to translate signals from one domain to another (as in image captioning or text-to-image generation).

Natural scene reconstruction from fMRI signals using generative latent diffusion.

In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images.

Mathematical Derivation of Wave Propagation Properties in Hierarchical Neural Networks with Predictive Coding Feedback Dynamics.

Sensory perception (e.g., vision) relies on a hierarchy of cortical areas, in which neural activity propagates in both directions, to convey information not only about sensory inputs but also about cognitive states, expectations and predictions.

A Traveling Waves Perspective on Temporal Binding.

Brain oscillations are involved in many cognitive processes, and several studies have investigated their role in cognition. In particular, the phase of certain oscillations has been related to temporal binding and integration processes, with some authors arguing that perception could be an inherently rhythmic process. However, previous research on oscillations mostly overlooked their spatial component: how oscillations propagate through the brain as traveling waves, with systematic phase delays between brain regions.

Random Tactile Noise Stimulation Reveals Beta-Rhythmic Impulse Response Function of the Somatosensory System.

Both passive tactile stimulation and motor actions result in dynamic changes in beta band (15-30 Hz Hz) oscillations over somatosensory cortex. Similar to alpha band (8-12 Hz) power decrease in the visual system, beta band power also decreases following stimulation of the somatosensory system. This relative suppression of α and β oscillations is generally interpreted as an increase in cortical excitability.

Distinct roles of forward and backward alpha-band waves in spatial visual attention.

Previous research has associated alpha-band [8-12 Hz] oscillations with inhibitory functions: for instance, several studies showed that visual attention increases alpha-band power in the hemisphere ipsilateral to the attended location. However, other studies demonstrated that alpha oscillations positively correlate with visual perception, hinting at different processes underlying their dynamics. Here, using an approach based on traveling waves, we demonstrate that there are two functionally distinct alpha-band oscillations propagating in different directions.

On the role of feedback in image recognition under noise and adversarial attacks: A predictive coding perspective.

Brain-inspired machine learning is gaining increasing consideration, particularly in computer vision. Several studies investigated the inclusion of top-down feedback connections in convolutional networks; however, it remains unclear how and when these connections are functionally helpful. Here we address this question in the context of object recognition under noisy conditions.

Multimodal neural networks better explain multivoxel patterns in the hippocampus.

The human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP (Radford et al., 2021).

Periodic attention operates faster during more complex visual search.

Attention has been found to sample visual information periodically, in a wide range of frequencies below 20 Hz. This periodicity may be supported by brain oscillations at corresponding frequencies. We propose that part of the discrepancy in periodic frequencies observed in the literature is due to differences in attentional demands, resulting from heterogeneity in tasks performed.

Understanding the Computational Demands Underlying Visual Reasoning.

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the synthetic visual reasoning test (SVRT) challenge, a collection of 23 visual reasoning problems.

α Phase-Amplitude Tradeoffs Predict Visual Perception.

Spontaneous α oscillations (∼10 Hz) have been associated with various cognitive functions, including perception. Their phase and amplitude independently predict cortical excitability and subsequent perceptual performance. However, the causal role of α phase-amplitude tradeoffs on visual perception remains ill-defined.

Nudging the N170 forward with prior stimulation-Bridging the gap between N170 and recognition potential.

Evoked response potentials are often divided up into numerous components, each with their own body of literature. But is there less variety than we might suppose? In this study, we nudge one component into looking like another. Both the N170 and recognition potential (RP) are N1 components in response to familiar objects.

Predictive coding feedback results in perceived illusory contours in a recurrent neural network.

Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision is that they do not appear to perceive illusory contours (e.

Theta-phase dependent neuronal coding during sequence learning in human single neurons.

The ability to maintain a sequence of items in memory is a fundamental cognitive function. In the rodent hippocampus, the representation of sequentially organized spatial locations is reflected by the phase of action potentials relative to the theta oscillation (phase precession). We investigated whether the timing of neuronal activity relative to the theta brain oscillation also reflects sequence order in the medial temporal lobe of humans.

Deep learning and the Global Workspace Theory.

Recent advances in deep learning have allowed artificial intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace Theory (GWT) refers to a large-scale system integrating and distributing information among networks of specialized modules to create higher-level forms of cognition and awareness.

Occipital alpha-TMS causally modulates temporal order judgements: Evidence for discrete temporal windows in vision.

Recent advances in neuroscience have challenged the view of conscious visual perception as a continuous process. Behavioral performance, reaction times and some visual illusions all undergo periodic fluctuations that can be traced back to oscillatory activity in the brain. These findings have given rise to the idea of a discrete sampling mechanism in the visual system.

Tentative fMRI signatures of perceptual echoes in early visual cortex.

The visual Impulse Response Function (IRF) can be estimated by cross-correlating random luminance sequences with concurrently recorded EEG. It typically contains a strong 10 Hz oscillatory component, suggesting that visual information reverberates in the human brain as a "perceptual echo". The neural origin of these echoes remains unknown.

Representational Content of Oscillatory Brain Activity during Object Recognition: Contrasting Cortical and Deep Neural Network Hierarchies.

Numerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g.

Conscious perception and perceptual echoes: a binocular rivalry study.

Alpha rhythms (∼10Hz) in the human brain are classically associated with idling activities, being predominantly observed during quiet restfulness with closed eyes. However, recent studies demonstrated that alpha (∼10Hz) rhythms can directly relate to visual stimulation, resulting in oscillations, which can last for as long as one second. This alpha reverberation, dubbed perceptual echoes (PE), suggests that the visual system actively samples and processes visual information within the alpha-band frequency.

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning.

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs.

Differential Involvement of EEG Oscillatory Components in Sameness versus Spatial-Relation Visual Reasoning Tasks.

The development of deep convolutional neural networks (CNNs) has recently led to great successes in computer vision, and CNNs have become de facto computational models of vision. However, a growing body of work suggests that they exhibit critical limitations on tasks beyond image categorization. Here, we study one such fundamental limitation, concerning the judgment of whether two simultaneously presented items are the same or different (SD) compared with a baseline assessment of their spatial relationship (SR).

Turning the Stimulus On and Off Changes the Direction of α Traveling Waves.

Traveling waves have been studied to characterize the complex spatiotemporal dynamics of the brain. Several studies have suggested that the propagation direction of α traveling waves can be task dependent. For example, a recent electroencephalography (EEG) study from our group found that forward waves (i.

DMT alters cortical travelling waves.

Psychedelic drugs are potent modulators of conscious states and therefore powerful tools for investigating their neurobiology. N,N, Dimethyltryptamine (DMT) can rapidly induce an extremely immersive state of consciousness characterized by vivid and elaborate visual imagery. Here, we investigated the electrophysiological correlates of the DMT-induced altered state from a pool of participants receiving DMT and (separately) placebo (saline) while instructed to keep their eyes closed.