A fundamental characteristic of living systems is sensing and integrating multi-dimensional sensory signals with memory in order to generate complex self-organized behaviors in continuously changing environments. Using computations on the level of signaling networks in single-cells, we have identified that cells utilize dynamical ghost states as a memory-generating mechanism in order to...
The development of multicellular organisms is a dynamic process in which cells divide, rearrange, and interpret molecular signals to adopt specific cell fates. This results in the emergence of gene expression patterns, that later on give raise to different body parts and organs. We still lack full understanding of how these patterns could emerge in precise and reproducible way during embryonic...
The way organismic agents come to know the world, and the way algorithms solve problems, are fundamentally different. The most sensible course of action for an organism does not simply follow from logical rules of inference. Before it can even use such rules, the organism must tackle the problem of relevance. It must turn ill-defined problems into well-defined ones, turn semantics into syntax....
Molecular components of cells must communicate with each other through physical mechanisms that necessarily consume energy [1]; for example, ion channels communicate electrically, by modulating ionic currents which are sensed as resulting charge accumulation at the membrane by distant voltage gated channels. I will first argue in general that powering such communication must incur large costs...
Brain network simulations enable us to understand how different entities in the brain interact to generate function. Moreover, such computational model simulations provide means of understanding principles of cognition and causes of performance variability across individuals. Importantly, personalized brain network avatars hold the potential for a multitude of clinical applications to improve...
I will show using examples that many neural circuits and computational algorithms of the brain perform efficiently amid severe resource constraints. By extension, I will argue that the processes we call cognition and learning are only needed because of these limitations: circuits of the brain must adaptively infer minimal summaries, syntheses and approximations of the world. These...
Adaptation is a recurring theme in biology, offering vital survival mechanisms in dynamic environments through precise regulation of physiological variables. This talk dives into the intriguing concept of robust perfect adaptation (RPA), a phenomenon where a system maintains a specific variable at a setpoint despite persistent perturbations. The objective of this talk is to explore the...
Most natural and engineered information-processing systems transmit information via signals that vary in time. Computing the information transmission rate or the information encoded in the temporal characteristics of these signals, requires the mutual information between the input and output signals as a function of time, i.e. between the input and output trajectories. Yet, this is notoriously...
Deep Neural Networks (DNNs) have excelled in many fields, largely due to their proficiency in supervised learning tasks. However, the dependence on vast labeled data becomes a constraint when such data is scarce. Self-Supervised Learning (SSL), a promising approach, harnesses unlabeled data to derive meaningful representations. Yet, how SSL filters irrelevant information without explicit...
In the machine learning community, structured representations have demonstrated themselves to be hugely beneficial for efficient learning from limited data and generalization far beyond the training set. Examples of such structured representations include the spatially organized feature maps of convolutional neural networks, and the group structured activations of other equivariant models. To...
Information theory guides the design of information processing systems. It permits the construction of optimal communication channels between systems to sense, process, and represent information. A key concept thereby is relative entropy, quantifying the amount of information lost in a transaction. Relative entropy has, however, no sense of relevance: a bit of information on an irrelevant...
I will describe recent advances at the interface of physics-inspired AI, advanced computing, and automated workflows, and how these novel and complementary approaches are pushing the frontiers of knowledge and elevating human insight across disciplines.
Dynamical description of natural systems has generally focused on fixed points, with saddles and saddle-based phase-space objects such as heteroclinic channels/cycles being central concepts behind the emergence of quasi-stable long transients. Reliable and robust transient dynamics observed for real, inherently noisy systems is, however, not met by saddle-based dynamics, as demonstrated here....
The delicate balance necessary for ensuring reliable segregation of cell lineages is an intriguing problem in developmental biology. For mammals, and specifically for the early mouse embryo, cell fate decisions have been extensively researched, but the underlying mechanisms remain poorly understood. Current theoretical approaches to this problem still primarily rely on deterministic modeling,...
Quantum computing promises unprecedented possibilities for important computing tasks such as quantum simulations in chemistry and materials science or optimization and machine learning. With this potential, quantum computing is increasingly attracting interest from industry and scientific communities that use high performance computing (HPC) for their...
Biological neuronal networks exhibit hallmark features such as oscillatory dynamics, heterogeneity, modularity, and conduction delays. To investigate which of these features support computations or are epiphenomena, we study recurrent networks of damped harmonic oscillators and endow them with such biological features. Analyses of network dynamics uncovered a novel, powerful computational...
Sensory systems need to achieve a delicate balance between external and internal influences in order to accurately represent relevant information. Dynamic adjustments of the sensory code to these influences have been traditionally categorized depending on their origin and studied separately. Sensory adaptation is a response of a neuron to exogenous changes in stimulus statistics, while...
