Sensory adaptation

…a variety of mechanisms, ranging from sensory adaptation over efficient coding to various flavors of probabilistic estimation, variably called Bayesian or predictive or optimal. Here we want to demonstrate with several examples that some reasons for attraction vs. repulsion have been overlooked, neglected, or not acknowledged for specific domains. One example for st

Visual decisions typically require accumulation of uncertain sensory information to make inferences about the state of the world. This inference process is complicated by the fact that the state of the world can change, which often requires adjustments in how sensory information is accumulated. Previous work has modeled optimal inference in these changing environments [1-2]. One key feature of these models is a leakage of accumulated evidence, where the leak rate depends on environmental stability. For example, in more unstable environments, an increased leak rate helps remove pre-change evide

…thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Sensory adaptation is a neural mechanism that filters background by minimizing responses to stable sensory stimuli, and a fundamental feature of sensory systems. Adaptation over relatively fast timescales (milliseconds to minutes) have been reported in many sensory systems. However, adaptation to persistent environmental stimuli over longer timescales (hours to days) have bee

Perceptual decisions are ubiquitous and essential in life, and even in their simplest forms open a window into the neural mechanisms underlying cognition. However, the properties of decision mechanisms inferred in simple behavioral paradigms are not independent of the properties of employed stimuli and task. One key task property is the type of information provided by a single stimulus, which can be either stationary or dynamic. Stationary paradigms are based on evidence with fixed central tendency, often obscured by noise, whereas in dynamic paradigms the mean of the evidence can vary within

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste

Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating