Wiktor F. Młynarski

The paper shows that sensory population codes change their statistical structure and content in response to internal and behavioral states, not just external stimuli.

The paper identifies intrinsic population dynamics in the superior colliculus that modulate visuospatial attention and enhance visual responses during a detection task.

The paper shows that locomotion modulates sensory neuron tuning in the mouse visual cortex, enabling multiplexed representation of sensory and movement signals.

The paper develops a computational theory for rapid behavioral inferences under time pressure, showing how adaptive belief tracking improves escape behavior in fruit flies.

The paper shows that locomotion optimizes sensory representations by aligning neural coding with changes in stimulus statistics across rodents and primates.

The paper shows that dynamic contrast adaptation in auditory cortex predicts perception of sounds in noise.

The paper shows human perceptual decisions align with optimal Bayesian inference in dynamic environments, with decision bias depending on context reliability, environmental stability, and time since context change.

The paper shows that retinal ganglion cell receptive fields change shape along the dorsoventral axis to match natural scene statistics, supporting efficient coding principles.

This study develops a theoretical framework to analyze adaptive sensory codes and shows that codes optimized for stationary environments can perform poorly when the environment changes, proposing codes that balance encoding accuracy and adaptation speed.

The paper develops a model of population coding in the visual cortex that optimizes perceptual inference while minimizing activity cost, reproducing attentional modulation properties and generating hypotheses about top-down feedback and noise correlations.

This paper unifies normative theories and statistical inference in a Bayesian framework for biological systems analysis, demonstrating its application to neural data.

The paper identifies auditory grouping cues by analyzing natural sound statistics, revealing both known and novel principles of perceptual grouping.

The paper discusses principles of adaptive encoding in sensory systems and derives schemes that balance encoding cost and inference error.