@article{13230, abstract = {To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer’s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.}, author = {Charlton, Julie A. and Mlynarski, Wiktor F and Bai, Yoon H. and Hermundstad, Ann M. and Goris, Robbe L.T.}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {6}, publisher = {Public Library of Science}, title = {{Environmental dynamics shape perceptual decision bias}}, doi = {10.1371/journal.pcbi.1011104}, volume = {19}, year = {2023}, } @article{12349, abstract = {Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.}, author = {Gupta, Divyansh and Mlynarski, Wiktor F and Sumser, Anton L and Symonova, Olga and Svaton, Jan and Jösch, Maximilian A}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {606--614}, publisher = {Springer Nature}, title = {{Panoramic visual statistics shape retina-wide organization of receptive fields}}, doi = {10.1038/s41593-023-01280-0}, volume = {26}, year = {2023}, } @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}, } @article{7553, abstract = {Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.}, author = {Mlynarski, Wiktor F and Hledik, Michal and Sokolowski, Thomas R and Tkačik, Gašper}, journal = {Neuron}, number = {7}, pages = {1227--1241.e5}, publisher = {Cell Press}, title = {{Statistical analysis and optimality of neural systems}}, doi = {10.1016/j.neuron.2021.01.020}, volume = {109}, year = {2021}, } @article{9439, abstract = {The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.}, author = {Mlynarski, Wiktor F and Hermundstad, Ann M.}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {998--1009}, publisher = {Springer Nature}, title = {{Efficient and adaptive sensory codes}}, doi = {10.1038/s41593-021-00846-0}, volume = {24}, year = {2021}, }