@article{21282, abstract = {Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through cellular interactions. We propose that both extremes—as well as the continuum of intermediate cases—can be conceptualized as information-processing systems, whose operation can be described using “Marr's three levels of analysis”: the computational problem being solved, the algorithms employed, and their molecular implementation. At the first level, we argue that normative theories, such as information-theoretic optimization principles, provide a formalization of the computational problem. At the second level, we show how simplified information-processing architectures provide a framework for developmental algorithms, which are formalized mathematically using dynamical systems theory. At the third level, the implementation of developmental algorithms is described by mechanistic biophysical and gene regulatory network models.}, author = {Brückner, David and Tkačik, Gašper}, issn = {2835-8279}, journal = {PRX Life}, publisher = {American Physical Society}, title = {{Marr's three levels for embryonic development: Information, dynamical systems, gene networks}}, doi = {10.1103/fdcf-dkws}, volume = {4}, year = {2026}, } @article{21759, abstract = {Promoters and enhancers are cis-regulatory elements (CREs), DNA sequences that bind transcription factor (TF) proteins to up- or down-regulate target genes. Decades-long efforts yielded TF-DNA interaction models that predict how strongly an individual TF binds arbitrary DNA sequences and how individual binding events on the CRE combine to affect gene expression. These insights can be synthesized into a global, biophysically realistic, and quantitative genotype-phenotype (GP) map for gene regulation, a ‘holy grail’ for the application of evolutionary theory. A global map provides a rare opportunity to simulate the long-term evolution of regulatory sequences and pose several fundamental questions: How long does it take to evolve CREs de novo? How many non-trivial regulatory functions exist in sequence space? How connected are they? For which regulatory architecture is CRE evolution most rapid and evolvable? In this article, the second of a two-part series, we review the application of evolutionary concepts — epistasis, robustness, evolvability, tunability, plasticity, and bet-hedging — to the evolution of gene regulatory sequences. We then evaluate the potential for a unifying theory for the evolution of regulatory sequences and identify key open challenges.}, author = {Mascolo, Elia and Körei, Reka E and Borst, Noa O. and Barton, Nicholas H and Crocker, Justin and Tkačik, Gašper}, issn = {1879-0380}, journal = {Current Opinion in Genetics and Development}, publisher = {Elsevier}, title = {{Long-term evolution of regulatory DNA sequences. Part 2: Theory and future challenges}}, doi = {10.1016/j.gde.2026.102472}, volume = {98}, year = {2026}, } @article{19785, abstract = {We consider a family of totally asymmetric simple exclusion processes (TASEPs), consisting of particles on a lattice that require binding by a “token” in various physical configurations to advance over the lattice. Using a combination of theory and simulations, we address the following questions: (i) How does token binding kinetics affect the current-density relation on the lattice? (ii) How does this current-density relation depend on the scarcity of tokens? (iii) How do tokens propagate the effects of the locally imposed disorder (such as a slow site) over the entire lattice? (iv) How does a shared pool of tokens couple concurrent TASEPs running on multiple lattices? and (v) How do our results translate to TASEPs with open boundaries that exchange particles with the reservoir? Since real particle motion (including in biological systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.}, author = {Kavcic, Bor and Tkačik, Gašper}, issn = {2470-0053}, journal = {Physical Review E}, number = {5}, publisher = {American Physical Society}, title = {{Token-driven totally asymmetric simple exclusion processes}}, doi = {10.1103/physreve.111.054122}, volume = {111}, year = {2025}, } @article{18849, abstract = {Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks.}, author = {Sokolowski, Thomas R and Gregor, Thomas and Bialek, William and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {1}, publisher = {National Academy of Sciences}, title = {{Deriving a genetic regulatory network from an optimization principle}}, doi = {10.1073/pnas.2402925121}, volume = {122}, year = {2025}, } @article{18850, abstract = {Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs). Under what conditions and by how much can chromatin reduce regulatory errors on a global scale? We use a theoretical approach to compare two scenarios for gene regulation: one that relies on TF binding to free DNA alone and one that uses a combination of TFs and chromatin-regulating PFs to achieve desired gene expression patterns. We find, first, that chromatin effectively silences groups of genes that should be simultaneously OFF, thereby allowing more accurate graded control of expression for the remaining ON genes. Second, chromatin buffers the deleterious consequences of nontarget binding as the number of OFF genes grows, permitting a substantial expansion in regulatory complexity. Third, chromatin-based regulation productively co-opts nontarget TF binding for ON genes in order to establish a “leaky” baseline expression level, which targeted activator or repressor binding subsequently up- or down-modulates. Thus, on a global scale, using chromatin simultaneously alleviates pressure for high specificity of regulatory interactions and enables an increase in genome size with minimal impact on global expression error.}, author = {Perkins, Mindy Liu and Crocker, Justin and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {1}, publisher = {National Academy of Sciences}, title = {{Chromatin enables precise and scalable gene regulation with factors of limited specificity}}, doi = {10.1073/pnas.2411887121}, volume = {122}, year = {2025}, } @article{18936, abstract = {A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by 2 proteins, allowing us to connect any promoter genotype to 6 phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties—the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation—can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.}, author = {Grah, Rok and Guet, Calin C and Tkačik, Gašper and Lagator, Mato}, issn = {1943-2631}, journal = {Genetics}, number = {2}, publisher = {Oxford University Press}, title = {{Linking molecular mechanisms to their evolutionary consequences: a primer}}, doi = {10.1093/genetics/iyae191}, volume = {229}, year = {2025}, } @article{19453, abstract = {A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks.}, author = {Chiossi, Heloisa and Nardin, Michele and Tkačik, Gašper and Csicsvari, Jozsef L}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {11}, publisher = {National Academy of Sciences}, title = {{Learning reshapes the hippocampal representation hierarchy}}, doi = {10.1073/pnas.2417025122}, volume = {122}, year = {2025}, } @misc{19658, abstract = {We consider a family of totally asymmetric simple exclusion processes (TASEPs), consisting of particles on a lattice that require binding by a "token" in various physical configurations to advance over the lattice. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation on the lattice; (ii) How this current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such as a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in biological systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.}, author = {Tkačik, Gašper}, publisher = {Institute of Science and Technology Austria}, title = {{Token-driven totally asymmetric simple exclusion processes}}, doi = {10.15479/AT:ISTA:19658}, year = {2025}, } @article{19701, abstract = {Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.}, author = {Tkačik, Gašper and Wolde, Pieter Rein Ten}, issn = {1936-1238}, journal = {Annual review of biophysics}, pages = {249--274}, publisher = {Annual Reviews}, title = {{Information processing in biochemical networks}}, doi = {10.1146/annurev-biophys-060524-102720}, volume = {54}, year = {2025}, } @article{19626, abstract = {Active regulation of gene expression, orchestrated by complex interactions of activators and repressors at promoters, controls the fate of organisms. In contrast, basal expression at uninduced promoters is considered to be a dynamically inert mode of nonfunctional “promoter leakiness,” merely a byproduct of transcriptional regulation. Here, we investigate the basal expression mode of the mar operon, the main regulator of intrinsic multiple antibiotic resistance in Escherichia coli, and link its dynamic properties to the noncanonical, yet highly conserved start codon of marR across Enterobacteriaceae. Real-time, single-cell measurements across tens of generations reveal that basal expression consists of rare stochastic gene expression pulses, which maximize variability in wildtype and, surprisingly, transiently accelerate cellular elongation rates. Competition experiments show that basal expression confers fitness advantages to wildtype across several transitions between exponential and stationary growth by shortening lag times. The dynamically rich basal expression of the mar operon has likely been evolutionarily maintained for its role in growth homeostasis of Enterobacteria within the gut environment, thereby allowing other ancillary gene regulatory roles to evolve, e.g., control of costly-to-induce multidrug efflux pumps. Understanding the complex selection forces governing genetic systems involved in intrinsic multidrug resistance is crucial for effective public health measures.}, author = {Jain, Kirti and Hauschild, Robert and Bochkareva, Olga and Römhild, Roderich and Tkačik, Gašper and Guet, Calin C}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {15}, publisher = {National Academy of Sciences}, title = {{Pulsatile basal gene expression as a fitness determinant in bacteria}}, doi = {10.1073/pnas.2413709122}, volume = {122}, year = {2025}, } @misc{19294, abstract = {Active regulation of gene expression, orchestrated by complex interactions of activators and repressors at promoters, controls the fate of organisms. In contrast, basal expression at uninduced promoters is considered to be a dynamically inert mode of non-functional “promoter leakiness”, merely a byproduct of transcriptional regulation. Here, we investigate the basal expression mode of the mar operon, the main regulator of intrinsic multiple antibiotic resistance in Escherichia coli, and link its dynamic properties to the non-canonical, yet highly conserved start codon of marR across Enterobacteriaceae. Real-time, single-cell measurements across tens of generations reveal that basal expression consists of rare stochastic gene expression pulses, which maximize variability in wildtype and, surprisingly, transiently accelerate cellular elongation rates. Competition experiments show that basal expression confers fitness advantages to wildtype across several transitions between exponential and stationary growth by shortening lag times. The dynamically rich basal expression of the mar operon has likely been evolutionarily maintained for its role in growth homeostasis of Enterobacteria within the gut environment, thereby allowing other ancillary gene regulatory roles to evolve, e.g. control of costly-to-induce multi-drug efflux pumps. Understanding the complex selection forces governing genetic systems involved in intrinsic multi-drug resistance is crucial for effective public health measures.}, author = {Jain, Kirti and Hauschild, Robert and Bochkareva, Olga and Römhild, Roderich and Tkačik, Gašper and Guet, Calin C}, publisher = {Institute of Science and Technology Austria}, title = {{Data for "Pulsatile basal gene expression as a fitness determinant in bacteria"}}, doi = {10.15479/AT:ISTA:19294}, year = {2025}, } @article{17123, abstract = {A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.}, author = {Brückner, David and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {23}, publisher = {National Academy of Sciences}, title = {{Information content and optimization of self-organized developmental systems}}, doi = {10.1073/pnas.2322326121}, volume = {121}, year = {2024}, } @article{18525, abstract = {As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the “quantitative omnigenic model” (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.}, author = {Ruzickova, Natalia and Hledik, Michal and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {44}, publisher = {National Academy of Sciences}, title = {{Quantitative omnigenic model discovers interpretable genome-wide associations}}, doi = {10.1073/pnas.2402340121}, volume = {121}, year = {2024}, } @article{18902, author = {Zagorski, Marcin and Brandenberg, Nathalie and Lutolf, Matthias and Tkačik, Gašper and Bollenbach, Mark Tobias and Briscoe, James and Kicheva, Anna}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Assessing the precision of morphogen gradients in neural tube development}}, doi = {10.1038/s41467-024-45148-8}, volume = {15}, year = {2024}, } @article{14515, abstract = {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 difficult because of the high-dimensional nature of the trajectory space, and all existing techniques require approximations. We present an exact Monte Carlo technique called path weight sampling (PWS) that, for the first time, makes it possible to compute the mutual information between input and output trajectories for any stochastic system that is described by a master equation. The principal idea is to use the master equation to evaluate the exact conditional probability of an individual output trajectory for a given input trajectory and average this via Monte Carlo sampling in trajectory space to obtain the mutual information. We present three variants of PWS, which all generate the trajectories using the standard stochastic simulation algorithm. While direct PWS is a brute-force method, Rosenbluth-Rosenbluth PWS exploits the analogy between signal trajectory sampling and polymer sampling, and thermodynamic integration PWS is based on a reversible work calculation in trajectory space. PWS also makes it possible to compute the mutual information between input and output trajectories for systems with hidden internal states as well as systems with feedback from output to input. Applying PWS to the bacterial chemotaxis system, consisting of 182 coupled chemical reactions, demonstrates not only that the scheme is highly efficient but also that the number of receptor clusters is much smaller than hitherto believed, while their size is much larger.}, author = {Reinhardt, Manuel and Tkačik, Gašper and Ten Wolde, Pieter Rein}, issn = {2160-3308}, journal = {Physical Review X}, number = {4}, publisher = {American Physical Society}, title = {{Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories}}, doi = {10.1103/PhysRevX.13.041017}, volume = {13}, year = {2023}, } @article{12762, abstract = {Neurons in the brain are wired into adaptive networks that exhibit collective dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches. Although existing models account for oscillations and avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically or intractable to infer from data rigorously. Here we propose a feedback-driven Ising-like class of neural networks that captures avalanches and oscillations simultaneously and quantitatively. In the simplest yet fully microscopic model version, we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model’s two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations to collective behaviors of extreme events and neuronal avalanches. Importantly, the inferred parameters indicate that the co-existence of scale-specific (oscillations) and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.}, author = {Lombardi, Fabrizio and Pepic, Selver and Shriki, Oren and Tkačik, Gašper and De Martino, Daniele}, issn = {2662-8457}, journal = {Nature Computational Science}, pages = {254--263}, publisher = {Springer Nature}, title = {{Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain}}, doi = {10.1038/s43588-023-00410-9}, volume = {3}, year = {2023}, } @article{13127, abstract = {Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.}, author = {Casillas Perez, Barbara E and Bod'Ová, Katarína and Grasse, Anna V and Tkačik, Gašper and Cremer, Sylvia}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Dynamic pathogen detection and social feedback shape collective hygiene in ants}}, doi = {10.1038/s41467-023-38947-y}, volume = {14}, year = {2023}, } @article{14656, abstract = {Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.}, author = {Nardin, Michele and Csicsvari, Jozsef L and Tkačik, Gašper and Savin, Cristina}, issn = {1529-2401}, journal = {The Journal of Neuroscience}, number = {48}, pages = {8140--8156}, publisher = {Society for Neuroscience}, title = {{The structure of hippocampal CA1 interactions optimizes spatial coding across experience}}, doi = {10.1523/JNEUROSCI.0194-23.2023}, volume = {43}, year = {2023}, } @article{12156, abstract = {Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory—theory that prescribes rather than just describes—in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code.}, author = {Zoller, Benjamin and Gregor, Thomas and Tkačik, Gašper}, issn = {2452-3100}, journal = {Current Opinion in Systems Biology}, keywords = {Applied Mathematics, Computer Science Applications, Drug Discovery, General Biochemistry, Genetics and Molecular Biology, Modeling and Simulation}, number = {9}, publisher = {Elsevier}, title = {{Eukaryotic gene regulation at equilibrium, or non?}}, doi = {10.1016/j.coisb.2022.100435}, volume = {31}, year = {2022}, } @article{12332, abstract = {Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments.}, author = {Mlynarski, Wiktor F and Tkačik, Gašper}, issn = {1545-7885}, journal = {PLoS Biology}, number = {12}, pages = {e3001889}, publisher = {Public Library of Science}, title = {{Efficient coding theory of dynamic attentional modulation}}, doi = {10.1371/journal.pbio.3001889}, volume = {20}, year = {2022}, }