An important question in neuroscience is how sensory systems change as animals grow and interact with the environment. Exploring sensory systems in animals as they develop can reveal how networks of neurons process information as the neurons themselves grow and the needs of the animal change. Here we compared the structure and function of peripheral parts of the olfactory pathway in newly hatched and adult locusts. We found that populations of olfactory sensory neurons (OSNs) in hatchlings and adults responded with similar tunings to a panel of odors. The morphologies of local neurons (LNs) and projection neurons (PNs) in the antennal lobes (ALs) were very similar in both age groups, though they were smaller in hatchlings, they were proportional to overall brain size. The odor evoked responses of LNs and PNs were also very similar in both age groups, characterized by complex patterns of activity including oscillatory synchronization. Notably, in hatchlings, spontaneous and odor-evoked firing rates of PNs were lower, and LFP oscillations were lower in frequency, than in the adult. Hatchlings have smaller antennae with fewer OSNs; removing antennal segments from adults also reduced LFP oscillation frequency. Thus, consistent with earlier computational models, the developmental increase in frequency is due to increasing intensity of input to the oscillation circuitry. Overall, our results show that locusts hatch with a fully formed olfactory system that structurally and functionally matches that of the adult, despite its small size and lack of prior experience with olfactory stimuli.

Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume’s composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.

Many animals display innate preferences for some odors, but the physiological mechanisms underlying these preferences are poorly understood. Here, with behavioral tests, we establish a model system well suited to investigating olfactory mechanisms, the locust Schistocerca americana. We conducted open field tests in an arena designed to provide only olfactory cues to guide navigation choices. We found that newly hatched locusts navigated toward, and spent more time near, the odor of wheat grass than humidified air. In similar tests, we found that hatchlings avoided moderate concentrations of major individual components of the food blend odor, 1-hexanol (1% v/v) and hexanal (0.9% v/v) diluted in mineral oil relative to control presentations of unscented mineral oil. Hatchlings were neither attracted nor repelled by a lower concentration (0.1% v/v) of 1-hexanol but were moderately attracted to a low concentration (0.225% v/v) of hexanal. We quantified the behavior of the animals by tracking their positions with the Argos software toolkit. Our results establish that hatchlings have a strong, innate preference for food odor blend, but the valence of the blend's individual components may be different and may change depending on the concentration. Our results provide a useful entry point for an analysis of physiological mechanisms underlying innate sensory preferences.

Gap junctions, too small to spot in images used to create connectome maps, play outsized roles in shaping neural activity. A recent study reveals a surprising new gap junction function: they can stabilize a neuron's membrane potential against unwanted oscillations.

Locusts depend upon their sense of smell and provide useful models for understanding olfaction. Extending this understanding requires knowledge of the molecular and structural organization of the olfactory system. Odor sensing begins with olfactory receptor neurons (ORNs), which express odorant receptors (ORs). In insects, ORNs are housed, in varying numbers, in olfactory sensilla. Because the organization of ORs within sensilla affects their function, it is essential to identify the ORs they contain. Here, using RNA sequencing, we identified 179 putative ORs in the transcriptomes of the two main olfactory organs, antenna and palp, of the locust Schistocerca americana . Quantitative expression analysis showed most putative ORs (140) are expressed in antennae while only 31 are in the palps. Further, our analysis identified one OR detected only in the palps and seven ORs that are expressed differentially by sex. An in situ analysis of OR expression suggested ORs are organized in non-random combinations within antennal sensilla. A phylogenetic comparison of OR predicted protein sequences revealed homologous relationships among two other Acrididae species. Our results provide a foundation for understanding the organization of the first stage of the olfactory system in S. americana , a well-studied model for olfactory processing.

Automatically tracking the positions of multiple animals is often necessary for studying behaviours. This task involves multiple object tracking, a challenging problem in computer vision. Recent advances in machine learning applied to video analysis have been helpful for animal tracking. However, existing tools work well only in homogeneous environments with uniform illumination, features rarely found in natural settings. Moreover, available algorithms cannot effectively process discontinuities in animal motion such as sudden jumps, thus requiring laborious manual review. Here we present Argos, a software toolkit for tracking multiple animals in inhomogeneous environments. Argos includes tools for compressing videos based on animal movement, for generating training sets for a convolutional neural network (CNN) to detect animals, for tracking multiple animals in a video and for facilitating review and correction of the tracks manually, with simple graphical user interfaces. We demonstrate that Argos can help reduce the amount of video data to be stored and analysed, speed up analysis and allow analysing difficult and ambiguous conditions in a scene. Thus, Argos supports multiple approaches to animal tracking suited for varying recording conditions and available computational resources. Together, these tools allow the recording and tracking of movements of multiple markerless animals in inhomogeneous environments over many hours.

Better understanding myelination of peripheral nerves would benefit patients affected by peripheral neuropathies, including Charcot-Marie-Tooth disease. Little is known about the role the Golgi compartment plays in Schwann cell (SC) functions. Here, we studied the role of Golgi in myelination of peripheral nerves in mice through SC-specific genetic inactivation of phosphatidylinositol 4-kinase beta (PI4KB), a Golgi-associated lipid kinase. Sciatic nerves of such mice showed thinner myelin of large diameter axons and gross aberrations in myelin organization affecting the nodes of Ranvier, the Schmidt-Lanterman incisures, and Cajal bands. Non-myelinating SCs showed a striking inability to engulf small diameter nerve fibers. SCs of mutant mice showed a distorted Golgi morphology and disappearance of OSBP at the cis-Golgi compartment, together with a complete loss of GOLPH3 from the entire Golgi. Accordingly, the cholesterol and sphingomyelin contents of sciatic nerves were greatly reduced and so was the number of caveolae observed in SCs. Although the conduction velocity of sciatic nerves of mutant mice showed an 80% decrease, the mice displayed only subtle impairment in their motor functions. Our analysis revealed that Golgi functions supported by PI4KB are critically important for proper myelination through control of lipid metabolism, protein glycosylation, and organization of microvilli in the nodes of Ranvier of peripheral nerves.

Inhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Our simulations suggest that depolarizing GGN at its input branch can globally inhibit KCs several hundred microns away. Our in vivorecordings show that GGN responds to odors with complex temporal patterns of depolarization and hyperpolarization that can vary with odors and across animals, leading our model to predict the existence of a yet-undiscovered olfactory pathway. Our analysis reveals basic new features of GGN and the olfactory network surrounding it.

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity of associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with relatively simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense and complex spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Finally, in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse. Sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition that KCs receive from an inhibitory neuron, the Giant GABAergic neuron. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Importantly, as observed in vivo, the KC-MBON synapse is highly plastic, and, therefore, changes in synaptic strength after learning can change the balance of excitation and inhibition, potentially leading to changes in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub-optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs. A level of sparseness that was optimal before learning remains optimal post-learning.

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity as associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Then in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse and that the sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition KCs receive. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Further, in vivo , the KC–MBON synapse is highly plastic, and changes in synaptic strength after learning can change the balance of excitation and inhibition and may lead to a change in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub–optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs and do not lead to a concomitant change in the optimal sparseness.Author Summary Kenyon cells (KCs) of the mushroom body represent odors as a sparse code. When viewed from the perspective of follower neurons, mushroom body output neurons (MBONs), an optimal level of KC sparseness maximally separates the representations of odors. However, the KC–MBON synapse is highly plastic and may be potentiated or depressed by odor–driven experience that could, in turn, perturb the optimality formed by pre–synaptic circuits. Contrary to this expectation, we show that synaptic plasticity based on spike timing of pre- and postsynaptic neurons improves the ability of the system to distinguish between the representations of similar odors while preserving the optimality determined by pre–synaptic circuits.

New studies show that, as in mammals, perceptual decision-making behavior in fruit flies involves the integration of sensory information that accumulates over time; this involves a process of dendritic integration that depends on the transcription factor FoxP.