Ernst Niebur

Quantifying neural health with a single measure remains a major unmet challenge. Here, we report a physiological marker based on the spatial distribution of signal-derived energy correlates in the resting brain. We hypothesize that energy organization in the healthy brain approaches an entropy-maximizing, homeostatically regulated state characterized by five properties: lognormal energy distributions, balanced spatial allocation, statistical independence between regions, low energy variance, and temporal stability. Together, these properties define an energetic profile that the Neural Health Index (NHI) summarizes as a single score, computable from (scalp/intracranial) electroencephalography, magnetoencephalography, or functional magnetic resonance imaging. Across 808 participants spanning neurological, psychiatric, neurodevelopmental, and neurodegenerative disorders, NHI distinguished healthy from clinical populations with sensitivity and specificity exceeding 95% in internal and external validation, scaled with disease severity, tracked pharmacological treatment exposure, and generalized to unseen diagnoses. These findings support NHI as a disease-agnostic, modality-invariant candidate biomarker for disease stratification and treatment monitoring.

Biological vision has evolved to make efficient use of the limited information processing capability and tight energy budget of the brain by preferentially processing the most salient features of visual scenes. In contrast, modern deep vision models rely on expansive, high-dimensional representations. This may offer potential recognition gains but increases computing costs. As a consequence, the computer vision community has been exploring sparsity-enforcing techniques such as activation dropout and weight pruning. Beyond ameliorating the burden of computation, sparsity techniques such as random dropout have been shown to regularize model training, thus allowing for better generalization. Here, we pursue both dropout and weight pruning in tandem and adopt a biologically plausible saliency-informed dropout technique as an explainable alternative to the unstructured standard random dropout approach. Our approach involves a hierarchical “retinotopic" gating of convolutional feature maps, which promotes efficient deletion of “redundant" weights by iterative magnitude pruning. We explore the effectiveness of saliency-informed dropout based on different approaches to dropping based on the saliency map, dropout through all layers or only early layers, and by additionally applying dropout at inference. We compare throughout with standard random dropout. We present empirical results on regimes where such structured dynamic and static (weight) sparsities interact optimally to prune a ResNet model on the Imagenette dataset.

Real-life decisions typically involve multiple options, each with multiple attributes affecting value. In such complex situations, sequential shifts of attention to specific options and attributes are thought to guide the decision process. Using a task that allowed us to monitor attention during such multi-attribute decisions, we recorded decision-related signals in pre-supplementary motor area neurons from two male macaques. Attention influences activity in these neurons through two mechanisms. First, attention enhances the activity of neurons representing the currently sampled option, independent of its value, without fully suppressing the representation of other options. Second, attention up-regulates the gain of information integration towards the evolving value estimate for the attended option. In contrast, we found no evidence for a third suggested mechanism, in which only the attended option is represented. Instead, attention influences the ongoing parallel information accumulation and competition process by modulating the strength of the value information that drives this circuit.

Our ultimate goal is to understand mechanisms of decision-making, a fundamental cognitive function. Models of multi-attribute decision-making vary on whether preference formation is based on within-option or within-attribute processing. We carry out a combined empirical and computational study using lottery options with varying task complexities. We monitor eye gaze during the decision formation to determine which decision-relevant information participants attend and when. We compare models of different levels of complexity in their ability to account for the choices made by individual participants. We find that two models outperform all others. The first is the two-layer leaky-competing accumulator based on prospect theory (LCA-PT), which predicts human choices on simple tasks better than any other model. For complex tasks a new model based on operations research performs best, with both its performance as well as that of the second-ranked LCA-PT model significantly exceeding that of all other models. Both models use the sequence of observed eye movements for each participant to capture the allocation of attention to specific options and attributes during the decision process, but make different assumptions about the effect of attention on decision-making. Our results suggest that, when faced with complex choice problems, people form preferences primarily based on attention-guided pairwise, within-attribute value comparisons. Suboptimal decisions are at the basis of many societal ills, from drug abuse to eating disorders to displaying inappropriately violent behavior. Understanding their underlying mechanisms has the potential of developing remedies for these maladaptive behaviors.

Figure-ground organisation is a perceptual grouping mechanism for detecting objects and boundaries, essential for an agent interacting with the environment. Current figure-ground segmentation methods rely on classical computer vision or deep learning, requiring extensive computational resources, especially during training. Inspired by the primate visual system, we developed a bio-inspired perception system for the neuromorphic robot iCub. The model uses a hierarchical, biologically plausible architecture and event-driven vision to distinguish foreground objects from the background. Unlike classical approaches, event-driven cameras reduce data redundancy and computation. The system has been qualitatively and quantitatively assessed in simulations and with event-driven cameras on iCub in various scenarios. It successfully segments items in diverse real-world settings, showing comparable results to its frame-based version on simple stimuli and the Berkeley Segmentation dataset. This model enhances hybrid systems, complementing conventional deep learning models by processing only relevant data in Regions of Interest (ROI), enabling low-latency autonomous robotic applications.

Selective attention is an essential mechanism to filter sensory input and to select only its most important components, allowing the capacity-limited cognitive structures of the brain to process them in detail. The saliency map model, originally developed to understand the process of selective attention in the primate visual system, has also been extensively used in computer vision. Due to the wide-spread use of frame-based video, this is how dynamic input from non-stationary scenes is commonly implemented in saliency maps. However, the temporal structure of this input modality is very different from that of the primate visual system. Retinal input to the brain is massively parallel, local rather than frame-based, asynchronous rather than synchronous, and transmitted in the form of discrete events, neuronal action potentials (spikes). These features are captured by event-based cameras. We show that a computational saliency model can be obtained organically from such vision sensors, at minimal computational cost. We assess the performance of the model by comparing its predictions with the distribution of overt attention (fixations) of human observers, and we make available an event-based dataset that can be used as ground truth for future studies.

Decision-making is a cognitive process involving working memory, executive function, and attention. However, the connectivity of large-scale brain networks during decision-making is not well understood. This is because gaining access to large-scale brain networks in humans is still a novel process. Here, we used SEEG (stereoelectroencephalography) to record neural activity from the default mode network (DMN), dorsal attention network (DAN), and frontoparietal network (FN) in ten humans while they performed a gambling task in the form of the card game, “War”. By observing these networks during a decision-making period, we related the activity of and connectivity between these networks. In particular, we found that gamma band activity was directly related to a participant’s ability to bet logically, deciding what betting amount would result in the highest monetary gain or lowest monetary loss throughout a session of the game. We also found connectivity between the DAN and the relation to a participant’s performance. Specifically, participants with higher connectivity between and within these networks had higher earnings. Our preliminary findings suggest that connectivity and activity between these networks are essential during decision-making.

Figure-ground organisation is a mechanism of perceptual grouping that serves the detection of objects and their boundaries, needed by any agent to interact with the environment. Current approaches to figure-ground segmentation rely on classical computer vision or deep learning, requiring vast computational resources, in particular during the training phase of the latter. Inspired by the primate visual system, we developed a bio-inspired perception system to compute figure-ground organisation for the neuromorphic robot iCub. The proposed model distinguishes foreground objects from the background, thanks to a hierarchical biologically plausible architecture, exploiting event-driven vision. Differing from classical approaches, the use of event-driven cameras reduces data redundancy and computation. The system has been qualitatively and quantitatively assessed both in simulation and using the event-driven cameras mounted on the iCub looking at different scenarios and stimuli. The model successfully segments items in diverse real-world scenarios, showing comparable results against its frame-based version on simple stimuli and on the Berkeley Segmentation dataset benchmark (D. Martin, 2001). This model further offers a valuable addition to hybrid systems, complementing conventional computer vision deep learning models by processing only relevant data in detected Regions of Interest (ROI), thereby enabling low-latency autonomous robotic applications without redundant information.

Real-life decisions typically involve multiple options, each with multiple attributes affecting value. In such complex cases, sequential shifts of attention to specific options and attributes are thought to guide the decision process. We designed a task that allowed us to monitor attention in monkeys engaged in such multi-attribute decisions. We recorded pre-supplementary motor area neurons encoding action value signals reflecting the decision process. Attention guides this process through two mechanisms. First, attention enhances the activity of neurons representing the currently sampled option, independent of the attended option value. Second, attention up-regulates the gain of information integration towards the evolving value estimate for the attended option. In contrast, we found no evidence for a third suggested mechanism, in which only the attended option is represented. Instead, attention influences the ongoing information accumulation and competition between the options by modulating the strength of the value information that drives this circuit.