You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Projection neurons in Drosophila antennal lobes signal the acceleration of odor concentrations}
Article:
Abstract:
Temporal experience of odor gradients is important in spatial orientation of animals. The fruit fly Drosophila melanogaster exhibits robust odor-guided behaviors in an odor gradient field. In order to investigate how early olfactory circuits process temporal variation of olfactory stimuli, we subjected flies to precisely defined odor concentration waveforms and examined spike patterns of olfactory sensory neurons( OSNs) and projection neurons( PNs). We found a significant temporal transformation between OSN and PN spike patterns, manifested by the PN output strongly signaling the OSN spike rate and its rate of change. A simple two-dimensional model admitting the OSN spike rate and its rate of change as inputs closely predicted the PN output. When cascaded with the rate-of-change encoding by OSNs, PNs primarily signal the acceleration and the rate of change of dynamic odor stimuli to higher brain centers, thereby enabling animals to reliably respond to the onsets of odor concentrations.
Introduction:
Odor distribution in nature is intermittent and dynamic( Murlis et al., 1992; Vickers et al., 2001), and animals have evolved the ability to detect and respond to temporal variation of odor stimuli( David et al., 1983; Thesen et al., 1993; Vickers et al., 2001; Porter et al., 2007; Semmelhack and Wang, 2009; Kato et al., 2014). In one of the most sophisticated examples, Drosophila larvae with only a single functional olfactory sensory neuron( OSN) are capable of moving toward a droplet of an attractive odor by actively orienting themselves( Louis et al., 2008). Similarly, adult fruit flies exhibit robust odor-guided behaviors such as turning upwind in flight upon contact with an attractive odor plume( Budick and Dickinson, 2006) and staying within a specific odor zone( Semmelhack and Wang, 2009). In order to enable such odor-guided tasks, it is essential for any olfactory system to process time-varying features of olfactory stimuli and supply behaviorally relevant information to higher brain centers. Several recent studies have investigated how dynamic olfactory stimuli are processed in insect early olfactory systems( systems consisting principally of OSNs and projection neurons[PNs]) and observed significant temporal processing of odor signals( Bhandawat et al., 2007; Geffen et al., 2009; Kim et al., 2011; Nagel and Wilson, 2011; Martelli et al., 2013). Most of these studies employed a simple odor delivery system that generated step-pulse-like odor stimuli without directly monitoring the actual odor concentration levels. For a rigorous understanding of sensory processing, however, it is essential to precisely measure the input stimuli and systematically explore the input space, as has been successfully done in the field of vision and audition( Wu et al., 2006). Moreover, natural odor plumes are encountered in various spatiotemporal patterns, and their dynamics and statistics can influence the neural encoding mechanism( Brenner et al., 2000; Vickers et al., 2001). In Drosophila, OSNs expressing the same receptors connect with PNs in one of roughly 50 spherical compartments, termed olfactory glomeruli, which constitute a deutocerebral neuropil called the antennal lobe. PNs subsequently relay olfactory information to higher brain centers such as the mushroom body and the lateral horn( Stocker et al., 1990). Two recent studies independently reported that Drosophila OSNs encode not only the odor concentration but also its rate of change as a function of time( Kim et al., 2011; Nagel and Wilson, 2011). Building on this recent advance, we asked how PNs further contribute to creating internal representations of dynamic olfactory environments. We tested OSNs and PNs with short plume-like odor stimuli in a variety of settings and analyzed the correlation structure of input/output signals in the odor-OSN-PN pathway. We also constructed a two-dimensional( 2D) linear–nonlinear( LN) model of the OSN-to-PN transformation by inducing an ensemble of triangle-shaped OSN spike rates via a systematic design of olfactory stimuli.
Discussion:
We tested first- and second-order neurons in the Drosophila olfactory system with a wide range of dynamically varying odor concentrations and constructed a simple 2D LN model for the temporal processing of olfactory information. The shape of the 2D nonlinearity and the relatively low prediction error of the model support the hypothesis that the PN spike rate is most strongly dependent on the rate of change of its feedforward OSN input. When combined with the dynamic sensory encoding by OSNs—encoding of odor concentration and its rate of change—PNs signal the rate of change and, most strongly, the acceleration of odor concentration signals to higher brain centers. One remarkable example of the ‘acceleration encoding’ by PNs is found in the OSN/PN responses to the parabolically rising odor input, where the onset is enormously amplified to create step-like responses in PNs( Figure 2C, I). To the best of our knowledge, the encoding of acceleration by an olfactory sensory system has not been demonstrated elsewhere. This sensory encoding mechanism is distinguished from other chemosensory systems. For example, in bacterial chemotaxis, the rate of change of a stimulus is computed and fed directly into its motor system that controls the flagellar rotation( Bourret et al., 1991). The nematode Caenorhabditis elegans also exhibits a local-gradient-based chemotaxis behavior, and neurons in its sensorimotor pathway are shown to compute the rate of change of concentration gradients( Suzuki et al., 2008). Why do fruit flies compute acceleration of odor concentrations? How is the acceleration of an olfactory stimulus relevant to a fly's chemotaxis? An efficient chemotaxis strategy should depend on the distribution of odor molecules in the behaviorally relevant range, which can be quantified using a Reynolds number( Weissburg, 2000). Habitats of both nematodes and bacteria have low Reynolds numbers( Re < 1), and thus the odor distribution is primarily determined by a diffusion process. In this environment, the steepest local odor gradient can be directly linked to the location of the odor source, and therefore animals in such environments are expected to encode the rate of change of odor concentrations in order to detect the steepest gradient. In contrast, flying insects experience fluid mechanics that have relatively high Reynolds numbers( Re > 10), with turbulence dictating the distribution of odor molecules over the diffusion process. In this regime, the local odor gradient within an odor plume would be less informative in determining the location of the odor source than the distribution of odor plumes over a macroscopic scale. In fact, it has been well established that insects, including fruit flies, use a strategy of turning upwind during odor plume encounters, a strategy that does not require interpreting local concentration gradients( Budick and Dickinson, 2006). Furthermore, the relative position of a flying animal and the distribution of odor plumes evolve rapidly over time. It is therefore crucial for flying insects to detect and respond to an encountered odor plume before the fly's position drifts relative to adjacent plumes. We showed that Drosophila's early olfactory system signals the acceleration of odor concentrations to higher brain centers. This encoding mechanism allows an animal to detect the odor onset at a very early phase( Figure 4). Therefore, it is tempting to speculate that the acceleration encoding has evolved in Drosophila to maximize its chance to locate the odor source in a turbulent environment. It has also been shown that fruit flies can respond to the spatial difference of the odor concentration between two antennae in both walking and flight( Duistermars et al., 2009; Gaudry et al., 2013). However, even with the lack of spatial sampling ability, fruit fly larvae can locate an odor source, based on the temporal sampling of odor gradients( Louis et al., 2008). Therefore, adult fruit flies may rely on both spatial and temporal sampling methods to locate an odor source, although their relative importance may vary depending on the locomotive state, as Reynolds numbers are different between walking and flight.
Fruit flies are attracted to the smell of rotting fruit , and use it to guide them to nearby food sources . However , this task is made more challenging by the fact that the distribution of scent or odor molecules in the air is constantly changing . Fruit flies therefore need to cope with , and exploit , this variation if they are to use odors as cues . Odor molecules bind to receptors on the surface of nerve cells called olfactory sensory neurons , and trigger nerve impulses that travel along these cells . The olfactory sensory neurons are connected to other cells called projection neurons that in turn relay information to the higher centers of the brain . While many studies have investigated how fruit flies can distinguish between different odors , less is known about how animals can use variation in the strength of an odor to guide them towards its source . Kim et al . have now addressed this question by devising a method for delivering precise quantities of odors in controlled patterns to fruit flies , and then measuring the responses of olfactory sensory neurons and projection neurons . These experiments revealed that olfactory sensory neurons—which are found mainly in the flies' antennae—responded most strongly whenever an odor changed rapidly in strength , and showed relatively little response to constant odors . An independent study by Schulze , Gomez-Marin et al . found that olfactory sensory neurons in fruit fly larvae also respond in a similar way . Kim et al . also found that the response of the projection neurons depended on both the rate of nerve impulses in the olfactory sensory neurons and on how quickly this rate was changing . But , unlike the olfactory sensory neurons , projection neurons showed their strongest responses immediately after an odor first appeared . Thus , in contrast to organisms such as bacteria and worms , which are highly sensitive to the local concentration gradients of odors , fruit flies instead appear to be more responsive to the sudden appearance of an odor in their environment . Kim et al . suggest that this difference may reflect the fact that for ground-based organisms , local gradients are generally reliable predictors of the location of an odor source . However , for flying insects , continually changing air currents mean that predictable local gradients are less common . Therefore , the ability to detect a hint of an odor before the wind changes is a more useful skill .