Threshold for Odor Detection Adapts Based on Past Experience

Post by Shireen Parimoo

What's the science?

Animals react to sensory input from the environment, but sometimes the input isn’t strong enough to elicit a behavioral response. How much sensory input is needed for organisms to detect it? Several models attempt to explain how external sensory information, like sound, is detected in the brain. For example, the absolute threshold model proposes that a sound will be detected once it reaches a certain intensity (i.e. the threshold). According to the derivative model, the rate at which a sound’s intensity changes will determine when it is detected, whereas the fold change model posits that detection depends on how much the sound changes in proportion to its original intensity. Although these models have been applied to explain sensory detection in various organisms and across different modalities, no study has directly compared them with each other. This week in Neuron, Levy and Bargmann used computational modeling and calcium imaging to develop a unified model for odor detection in Caenorhabdtis elegans (roundworms).

How did they do it?

Roundworms have a simple nervous system that makes it possible to record the activity of specific neurons. The authors measured the sensory activity of an olfactory neuron called AWCON in response to changes in levels of the odorant butanone. Specifically, using a microfluidic setup, AWCON calcium activity was recorded in immobilized animals across a wide range of odor concentrations and timescales. Neuronal activity and navigation decisions were also examined in animals freely moving in odor gradients controlled by a specialized microfluidic device.

The authors rigorously tested many models that predict neuronal activity features (such as neuronal response and latency of response) and navigation behavior, including the absolute threshold, derivative, and fold change models. They also created an adaptive concentration threshold (ACT) model in which sensory activity is initiated when the odor concentration reaches a threshold, however, this threshold is continuously adapting to the odor. The ACT model includes (i) a threshold constant, which changes the neuron sensitivity, and (ii) adaptation time, which determines how long is the neuron memory of the external information. To determine whether the ACT model is generalizable, it was also tested on a separate dataset of neuronal activity in zebrafish in response to visual input. To identify the molecular basis of sensory detection, they examined the role of EGL-4, a protein kinase in the AWCON neuron that is involved in olfactory learning. They compared the effect of butanone concentration in loss-of-function mutants without functional EGL-4, gain-of-function mutants with enhanced EGL-4 activity, and wild-type animals. Finally, they performed theoretical studies to determine which model can allow both accurate and fast sensory responses, two key features for sensory neurons performance. 

What did they find?

Previous models did not adequately predict the observed neuronal responses and latencies, and could only match a subset of the experimental observations. For instance, they found that calcium responses depend on butanone concentration and the rate of concentration change, inconsistent with the absolute threshold and the derivative change models. The ACT model, on the other hand, predicted neuronal responses for both slow and fast changes in butanone concentration. The ACT model also predicted neuronal activity and aversive navigation decisions, like reversals and pauses, in more natural conditions, while animals freely navigated in odor gradients. This indicates that odor sensation and navigation are driven by an adaptive threshold mechanism that allows a comparison of past and current sensory inputs.

Interestingly, loss of EGL-4 function elongated the threshold adaptation time relative to wild-type animals and enhancement of EGL-4 function shortened it, suggesting that the protein kinase EGL-4 tunes the adaptation time of the sensory detection threshold. The ACT model also predicted activity in the optic tectum of zebrafish in response to visual input, demonstrating generalizability. Finally, the authors show that in contrast to alternative models, an adaptive-threshold mechanism allows sensory neurons to respond both fast and accurately to external stimuli, highlighting its benefit in reliable environment sensation.  

What's the impact?

Combining computational modeling with quantitative assays, this study is the first to systematically compare previous sensation models and to demonstrate how sensory detection is driven by a combination of current and past sensory inputs from the environment. The ACT model is powerful because it encompasses elements of previous models under different conditions and further generalizes to visual stimuli. These findings pave the way for future research to uncover the neurobiological basis of sensory detection and test the generalizability of the model across organisms and sensory modalities. 

Levy & Bargmann. An adaptive-threshold mechanism for odor sensation and animal navigation. Neuron (2020). Access the original scientific publication here.