Seminars

The emergent dynamics exhibited by collections of living organisms often shows signatures of symmetries that are broken at the single-organism level. At the same time, organism development itself encompasses a well-coordinated sequence of symmetry breaking events that successively transform a single, nearly isotropic cell into an animal with well-defined body axis and various anatomical asymmetries. Combining these key aspects of collective phenomena and embryonic development, we describe here the spontaneous formation of hydrodynamically stabilized active crystals made of hundreds of starfish embryos that gather during early development near fluid surfaces. We describe a minimal hydrodynamic theory that is fully parameterized by experimental measurements of microscopic interactions among embryos. Using this theory, we can quantitatively describe the stability, formation and rotation of crystals and rationalize the emergence of mechanical properties that carry signatures of an odd elastic material. Our work thereby quantitatively connects developmental symmetry breaking events on the single-embryo level with remarkable macroscopic material properties of a novel living chiral crystal system.

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.

We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. However, the CTM is not a standard Turing Machine. It’s not the input-output map that gives the CTM its feeling of consciousness, but what’s under the hood. Nor is the CTM a standard GW model. In addition to its architecture, what gives the CTM its feeling of consciousness is its predictive dynamics (cycles of prediction, feedback and learning), its internal multi-modal language Brainish, and certain special Long Term Memory (LTM) processors, including its Inner Speech and Model of the World processors. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature. Reference. L. Blum and M. Blum, "A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine," PNAS, vol. 119, no. 21, 24 May 2022. https://www.pnas.org/doi/epdf/10.1073/pnas.2115934119

Accurate segmentation of medical images is a key step in developing Computer-Aided Diagnosis (CAD) and automating various clinical tasks such as image-guided interventions. The success of state-of-the-art methods for medical image segmentation is heavily reliant upon the availability of a sizable amount of labelled data. If the required quantity of labelled data for learning cannot be reached, the technology turns out to be fragile. The principle of consensus tells us that as humans, when we are uncertain how to act in a situation, we tend to look to others to determine how to respond. In this webinar, Dr Mehrtash Harandi will show how to model the principle of consensus to learn to segment medical data with limited labelled data. In doing so, we design multiple segmentation models that collaborate with each other to learn from labelled and unlabelled data collectively.

To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.

Biological materials gain complexity from the programmable nature of their components. To manufacture materials with comparable complexity synthetically, we need to create building blocks with low crosstalk so that they only bind to their desired partners. Canonically, these building blocks are made using DNA strands or proteins to achieve specificity. Here we propose a new materials platform, termed Magnetic Handshake Materials, in which we program interactions through designing magnetic dipole patterns. This is a completely synthetic platform, enabled by magnetic printing technology, which is easier to both model theoretically and control experimentally. In this seminar, I will give an overview of the development of the Magnetic Handshake Materials platform, ranging from interaction, assembly to function design.

Optimized Non-Invasive Brain Stimulation for Addiction Treatment

Parkinson’s Disease (PD), the most common neurodegenerative movement disorder, is based on a complex interplay between genetic predispositions, aging processes, and environmental influences. In order to better understand the gene-environment axis in PD, we pursue a multi-omics approach to comprehensively interrogate genome-wide changes in histone modifications, DNA methylation, and hydroxymethylation, accompanied by transcriptomic profiling in cell and animal models of PD as well as large patient cohorts. Furthermore, we assess the plasticity of epigenomic modifications under influence of environmental factors using longitudinal cohorts of sporadic PD cases as well as mouse models exposed to specific environmental factors. Here, we present gene expression changes in PD mouse models in context of aging as well as environmental enrichment and high-fat diet.

The cerebral cortex contains an extraordinary diversity of excitatory projection neuron (PN) and inhibitory interneurons (IN), wired together to form complex circuits. Spatiotemporally coordinated execution of intrinsic molecular programs by PNs and INs and activity-dependent processes, contribute to cortical development and cortical microcircuits formation. Alterations of these delicate processes have often been associated to neurological/neurodevelopmental disorders. However, despite the groundbreaking discovery that spontaneous activity in the embryonic brain can shape regional identities of distinct cortical territories, it is still unclear whether this early activity contributes to define subtype-specific neuronal fate as well as circuit assembly. In this study, we combined in utero genetic perturbations via CRISPR/Cas9 system and pharmacological inhibition of selected ion channels with RNA-sequencing and live imaging technologies to identify the activity-regulated processes controlling the development of different cortical PN classes, their wiring and the acquisition of subtype specific features. Moreover, we generated human induced pluripotent stem cells (iPSCs) form patients affected by a severe, rare and untreatable form of developmental epileptic encephalopathy. By differentiating cortical organoids form patient-derived iPSCs we create human models of early electrical alterations for studying molecular, structural and functional consequences of the genetic mutations during cortical development. Our ultimate goal is to define the activity-conditioned processes that physiologically occur during the development of cortical circuits, to identify novel therapeutical paths to address the pathological consequences of neonatal epilepsies.

An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

The cytoskeleton has the remarkable ability to self-organize into active materials which underlie diverse cellular processes ranging from motility to cell division. Actomyosin is a canonical example of an active material, which generates cellularscale contractility in part through the forces exerted by myosin motors on actin filaments. While the molecular players underlying actomyosin contractility have been well characterized, how cellular-scale deformation in disordered actomyosin networks emerges from filament-scale interactions is not well understood. In this talk, I’ll present work done in collaboration with Sebastian Fürthauer and Nikta Fakhri addressing this question in vivo using the meiotic surface contraction wave seen in oocytes of the bat star Patiria miniata as a model system. By perturbing actin polymerization, we find that the cellular deformation rate is a nonmonotonic function of cortical actin density peaked near the wild type density. To understand this, we develop an active fluid model coarse-grained from filament-scale interactions and find quantitative agreement with the measured data. The model makes further predictions, including the surprising prediction that deformation rate decreases with increasing motor concentration. We test these predictions through protein overexpression and find quantitative agreement. Taken together, this work is an important step for bridging the molecular and cellular length scales for cytoskeletal networks in vivo.

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a target mechanism in the sense that similar tests should lead to similar results. In this talk, using resting-state electroencephalogram (EEG) as an example, I will argue that this assumption does not necessarily hold true. Typically EEG studies are conducted selecting one analysis method thought to be representative of the research question asked. Using multiple methods, we extracted a variety of features from a single resting-state EEG dataset and conducted correlational and case-control analyses. We found that many EEG features revealed a significant effect in the case-control analyses. Similarly, EEG features correlated significantly with cognitive tasks. However, when we compared these features pairwise, we did not find strong correlations. A number of explanations to these results will be discussed.

This session is a double feature of the Cologne Theoretical Neuroscience Forum and the Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience of the Jülich Research Center.

The brain combines signals across the eyes. This process is well-characterized for the perceptual anatomical pathway through V1 that primarily codes contrast, where interocular normalization ensures that responses are approximately equal for monocular and binocular stimulation. But we have much less understanding of how luminance is combined binocularly, both in the cortex and in subcortical structures that govern pupil diameter. Here I will describe the results of experiments using a novel combined EEG and pupillometry paradigm to simultaneously index binocular combination of luminance flicker in parallel pathways. The results show evidence of a more linear process than for spatial contrast, that may reflect different operational constraints in distinct anatomical pathways.

Previous experiments have shown that a comparison of two written narratives highlights their shared relational structure, which in turn facilitates the retrieval of analogous narratives from the past (e.g., Gentner, Loewenstein, Thompson, & Forbus, 2009). However, analogical retrieval occurs across domains that appear more conceptually distant than merely different narratives, and the deepest analogies use matches in higher-order relational structure. The present study investigated whether comparison can facilitate analogical retrieval of higher-order relations across written narratives and abstract symbolic problems. Participants read stories which became retrieval targets after a delay, cued by either analogous stories or letter-strings. In Experiment 1 we replicated Gentner et al. who used narrative retrieval cues, and also found preliminary evidence for retrieval between narrative and symbolic domains. In Experiment 2 we found clear evidence that a comparison of analogous letter-string problems facilitated the retrieval of source stories with analogous higher-order relations. Experiment 3 replicated the retrieval results of Experiment 2 but with a longer delay between encoding and recall, and a greater number of distractor source stories. These experiments offer support for the schema induction account of analogical retrieval (Gentner et al., 2009) and show that the schemas abstracted from comparison of narratives can be transferred to non-semantic symbolic domains.

Neural correlates of visual working memory have been found in early visual, parietal, and prefrontal regions. These findings have spurred fruitful debate over how and where in the brain memories might be represented. Here, I will present data from multiple experiments to demonstrate how a focus on behavioral requirements can unveil a more comprehensive understanding of the visual working memory system. Specifically, items in working memory must be maintained in a highly robust manner, resilient to interference. At the same time, storage mechanisms must preserve a high degree of flexibility in case of changing behavioral goals. Several examples will be explored in which visual memory representations are shown to undergo transformations, and even shift their cortical locus alongside their coding format based on specifics of the task.