Image Processing ePosters

The fact that the eyes are constantly in motion, even during ‘fixation’, implies that retinal ganglion cells' spike times carry information about the visual scene even when the scene is static. Moreover, this motion implies that fine details of the visual scene could not be decoded from pure spatial retinal…

Introduction: Spontaneous activity in visual cortex is high-dimensional and state-dependent, yet its organizing principles are unclear (Stringer et al. Science 2019; Berkes et al. Science 2011). We combine large-scale imaging, modelling, and theory to ask: 1. Does spontaneous activity share the structure of evoked…

Primates visually identify objects primarily by shape and also by texture. Models of primate vision based on convolutional neural networks (CNNs) achieve high object recognition performance but, unlike primates, emphasize texture over shape. Overcoming this texture bias and making models more brain-like requires data…

Dwarf cuttlefish, like octopus, are masters of camouflage. To camouflage the cuttlefish brain must solve a hard computational problem; the brain needs to process the visual environment, compute an appropriate pattern, and then orchestrate the expansion and contraction of hundreds of thousands of pigment filled sacs to…

The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain “sees”. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction…

Predictions about sensory inputs (e.g. contextual signals from associative cortical areas to sensory areas) arrive primarily in the superficial layers of the cortical sheet, where they are received by the distal apical dendrites of pyramidal neurons. These distal apical dendrites are long tree-like extensions which…

Dendrites perform non-linear processing of synaptic inputs, and drive active processes such as plateau potentials which are critical to computation and plasticity. Therefore, the ability to image voltage across an entire dendritic tree would represent a major advance in systems neuroscience. Recent advances in…

An important open question in vision neuroscience is how the brain integrates prior knowledge with sensory data to perceive the world. The Bayesian brain hypothesis suggests that the brain performs posterior inference based on an internal model to construct representations that explain the stimulus. Indeed, there is…

Deep neural networks (DNNs) are extremely vulnerable to adversarial attacks, where small changes to the input, unnoticeable to humans, can drastically alter the model’s predictions. One reason for their poor performance is that they process as much information as possible and make decisions based on the input…

We study the statistics of the 3D color space of natural images and develop a model for efficiently representing this structure. Analysis of a dataset of 503 calibrated natural images reveals striking non-Gaussian structure with heavy tails in distinct directions that are asymmetrically distributed in 3D color space.…

Disentangling independent latent components from neural data is a desirable goal in neuroscience. Independent component analysis (ICA) has been used to identify disentangled latent neural trajectories and their corresponding sub-brain maps from recorded imaging sequences. However, enforcing full independence between…

Animals are dynamical systems possessing a wide range of capabilities mediated by interactions between their constitutive cells. The brain, central integrator and coordinator of bodily output, solves problems in the context of bodily needs, driven by external environmental signals and internal bodily signals. While…

Anatomically segregated apical and basal dendrites of pyramidal neurons receive functionally distinct inputs, but it is unknown if this results in compartment-level functional diversity. Hippocampal place cells exhibit representational drift (Ziv et al., 2013), but this has only been characterized with somatic…

Synchronous movements of the entire face, from grimacing to chewing, offer significant insights into internal states. Mice, with discernible facial responses and evolutionarily conserved mammalian facial movement control circuits, provide an ideal model to unravel the link between facial movement and internal…

Recognizing the causes of ischemic stroke is crucial for defining secondary preventive strategies. Etiology-based classification systems integrate clinical, imaging, and laboratory findings to assign stroke etiology[1-4]. However, this requires significant time and resources, and these systems still fail to assign…

Understanding how visual attributes influence cognitive properties, such as memorability and emotional valence, is crucial for advancements in computational neuroscience and AI-driven image processing. Previous work has introduced a research framework that uses generative models to learn the complex nonlinear mapping…

Applying the principles of predictive coding to reading has successfully increased our understanding of the neuro-cognitive processes underlying the extraction of meaning from text. Central is integrating prior knowledge with the sensory input to highlight the informative parts of a visual percept, thus increasing the…

The fusiform gyrus (FG) in the human brain is essential for higher cortical functions such as recognizing faces and human bodies [1, 2]. Though, the relationship of its structural and functional decompositions in the anterior portion is yet to be better understood [3]. Here, the anterior FG was investigated in ten…

Brain-computer interfaces (BCIs) are a powerful tool in both clinical applications and basic science research. Traditionally, BCIs rely on electrical signals collected by electrode arrays, which usually suffer from instability due to electrode movements and cell loss [1]. Recent approaches to address this issue…

Comparative neuroanatomy has been a challenging research field, even with the methodological innovations in recent years. In this context, cortical surface reconstructions emerged as a new venue to provide further insights on brain morphology, but they are mostly limited to humans and a few other primates, hindering a…

A growing number of neuroimaging tools are being developed to enable data analysis in a more tool-independent manner when analyzing three- and four-dimensional brain imaging data in various statistical settings. Ideally, such tools still offer user interfaces that are accessible to those with a basic understanding of…

Automated axon tracing via fully supervised learning requires annotated 3D brain imagery, which necessitates expertise and labor to produce. Reducing the amount of annotated training data needed for supervised algorithms for axon tracing would expedite critical brain research. In this work, we developed and integrated…

Introduction. There exists no method for non-invasive imaging of neural activity throughout the brain with a millisecond and millimetre resolution. EIT can theoretically achieve this by injecting a constant current with surface electrodes, measuring the voltage change associated with neural depolarization and…

Accurate localization of EEG scalp electrodes is crucial for precisely estimating the underlying neuronal activity from observed electrical potential changes on the scalp. Various techniques for electrode localization have been proposed, categorized into direct measurements on the subject's head or indirect methods…