Local interneurons and projection neurons in the antennal lobe from a spiking point of view

Combinatorial encoding of odors in the mosquito antennal lobe

Among the cues that a mosquito uses to find a host for blood-feeding, the smell of the host plays an important role. Previous studies have shown that host odors contain hundreds of chemical odorants, which are detected by different receptors on the peripheral sensory organs of mosquitoes. But how individual odorants are encoded by downstream neurons in the mosquito brain is not known. We developed an in vivo preparation for patch-clamp electrophysiology to record from projection neurons and local neurons in the antennal lobe of Aedes aegypti. Combining intracellular recordings with dye-fills, morphological reconstructions, and immunohistochemistry, we identify different sub-classes of antennal lobe neurons and their putative interactions. Our recordings show that an odorant can activate multiple neurons innervating different glomeruli, and that the stimulus identity and its behavioral preference are represented in the population activity of the projection neurons. Our results provide a detailed description of the second-order olfactory neurons in the central nervous system of mosquitoes and lay a foundation for understanding the neural basis of their olfactory behaviors.

Novelty detection in early olfactory processing of the honey bee, Apis mellifera

Animals are constantly bombarded with stimuli, which presents a fundamental problem of sorting among pervasive uninformative stimuli and novel, possibly meaningful stimuli. We evaluated novelty detection behaviorally in honey bees as they position their antennae differentially in an air stream carrying familiar or novel odors. We then characterized neuronal responses to familiar and novel odors in the first synaptic integration center in the brain-the antennal lobes. We found that the neurons that exhibited stronger initial responses to the odor that was to be familiarized are the same units that later distinguish familiar and novel odors, independently of chemical identities. These units, including both tentative projection neurons and local neurons, showed a decreased response to the familiar odor but an increased response to the novel odor. Our results suggest that the antennal lobe may represent familiarity or novelty to an odor stimulus in addition to its chemical identity code. Therefore, the mechanisms for novelty detection may be present in early sensory processing, either as a result of local synaptic interaction or via feedback from higher brain centers.

Olfactory coding in the antennal lobe of the bumble bee Bombus terrestris

Sociality is classified as one of the major transitions in evolution, with the largest number of eusocial species found in the insect order Hymenoptera, including the Apini (honey bees) and the Bombini (bumble bees). Bumble bees and honey bees not only differ in their social organization and foraging strategies, but comparative analyses of their genomes demonstrated that bumble bees have a slightly less diverse family of olfactory receptors than honey bees, suggesting that their olfactory abilities have adapted to different social and/or ecological conditions. However, unfortunately, no precise comparison of olfactory coding has been performed so far between honey bees and bumble bees, and little is known about the rules underlying olfactory coding in the bumble bee brain. In this study, we used in vivo calcium imaging to study olfactory coding of a panel of floral odorants in the antennal lobe of the bumble bee Bombus terrestris. Our results show that odorants induce reproducible neuronal activity in the bumble bee antennal lobe. Each odorant evokes a different glomerular activity pattern revealing this molecule's chemical structure, i.e. its carbon chain length and functional group. In addition, pairwise similarity among odor representations are conserved in bumble bees and honey bees. This study thus suggests that bumble bees, like honey bees, are equipped to respond to odorants according to their chemical features.

Olfactory coding in honeybees

With less than a million neurons, the western honeybee Apis mellifera is capable of complex olfactory behaviors and provides an ideal model for investigating the neurophysiology of the olfactory circuit and the basis of olfactory perception and learning. Here, we review the most fundamental aspects of honeybee's olfaction: first, we discuss which odorants dominate its environment, and how bees use them to communicate and regulate colony homeostasis; then, we describe the neuroanatomy and the neurophysiology of the olfactory circuit; finally, we explore the cellular and molecular mechanisms leading to olfactory memory formation. The vastity of histological, neurophysiological, and behavioral data collected during the last century, together with new technological advancements, including genetic tools, confirm the honeybee as an attractive research model for understanding olfactory coding and learning.

Early olfactory, but not gustatory processing, is affected by the selection of heritable cognitive phenotypes in honey bee

Associative learning enables animals to predict rewards or punishments by their associations with predictive stimuli, while non-associative learning occurs without reinforcement. The latter includes latent inhibition (LI), whereby animals learn to ignore an inconsequential 'familiar' stimulus. Individual honey bees display heritable differences in expression of LI. We examined the behavioral and neuronal responses between honey bee genetic lines exhibiting high and low LI. We observed, as in previous studies, that high LI lines learned a familiar odor more slowly than low LI bees. By measuring gustatory responses to sucrose, we determined that perception of sucrose reward was similar between both lines, thereby not contributing to the LI phenotype. We then used extracellular electrophysiology to determine differences in neural responses of the antennal lobe (AL) to familiar and novel odors between the lines. Low LI bees responded significantly more strongly to both familiar and novel odors than the high LI bees, but the lines showed equivalent differences in response to the novel and familiar odors. This work suggests that some effects of genotype are present in early olfactory processing, and those effects could complement how LI is manifested at later stages of processing in brains of bees in the different lines.

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System

Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system, the encoding of odor information undergoes a transition from a dense spatiotemporal population code in the antennal lobe to a sparse code in the mushroom body. However, the exact mechanisms shaping odor representations and their role in sensory processing are incompletely identified. Here, we investigate the transformation from dense to sparse odor representations in a spiking model of the insect olfactory system, focusing on two ubiquitous neural mechanisms: spike frequency adaptation at the cellular level and lateral inhibition at the circuit level. We find that cellular adaptation is essential for sparse representations in time (temporal sparseness), while lateral inhibition regulates sparseness in the neuronal space (population sparseness). The interplay of both mechanisms shapes spatiotemporal odor representations, which are optimized for the discrimination of odors during stimulus onset and offset. Response pattern correlation across different stimuli showed a nonmonotonic dependence on the strength of lateral inhibition with an optimum at intermediate levels, which is explained by two counteracting mechanisms. In addition, we find that odor identity is stored on a prolonged timescale in the adaptation levels but not in the spiking activity of the principal cells of the mushroom body, providing a testable hypothesis for the location of the so-called odor trace.

Comparative study of chemical neuroanatomy of the olfactory neuropil in mouse, honey bee, and human

Despite divergent evolutionary origins, the organization of olfactory systems is remarkably similar across phyla. In both insects and mammals, sensory input from receptor cells is initially processed in synaptically dense regions of neuropil called glomeruli, where neural activity is shaped by local inhibition and centrifugal neuromodulation prior to being sent to higher-order brain areas by projection neurons. Here we review both similarities and several key differences in the neuroanatomy of the olfactory system in honey bees, mice, and humans, using a combination of literature review and new primary data. We have focused on the chemical identity and the innervation patterns of neuromodulatory inputs in the primary olfactory system. Our findings show that serotonergic fibers are similarly distributed across glomeruli in all three species. Octopaminergic/tyraminergic fibers in the honey bee also have a similar distribution, and possibly a similar function, to noradrenergic fibers in the mammalian OBs. However, preliminary evidence suggests that human OB may be relatively less organized than its counterparts in honey bee and mouse.

Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities

The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

Modeling latency code processing in the electric sense: from the biological template to its VLSI implementation

Understanding the coding of sensory information under the temporal constraints of natural behavior is not yet well resolved. There is a growing consensus that spike timing or latency coding can maximally exploit the timing of neural events to make fast computing elements and that such mechanisms are essential to information processing functions in the brain. The electric sense of mormyrid fish provides a convenient biological model where this coding scheme can be studied. The sensory input is a physically ordered spatial pattern of current densities, which is coded in the precise timing of primary afferent spikes. The neural circuits of the processing pathway are well known and the system exhibits the best known illustration of corollary discharge, which provides the reference to decoding the sensory afferent latency pattern. A theoretical model has been constructed from available electrophysiological and neuroanatomical data to integrate the principal traits of the neural processing structure and to study sensory interaction with motor-command-driven corollary discharge signals. This has been used to explore neural coding strategies at successive stages in the network and to examine the simulated network capacity to reproduce output neuron responses. The model shows that the network has the ability to resolve primary afferent spike timing differences in the sub-millisecond range, and that this depends on the coincidence of sensory and corollary discharge-driven gating signals. In the integrative and output stages of the network, corollary discharge sets up a proactive background filter, providing temporally structured excitation and inhibition within the network whose balance is then modulated locally by sensory input. This complements the initial gating mechanism and contributes to amplification of the input pattern of latencies, conferring network hyperacuity. These mechanisms give the system a robust capacity to extract behaviorally meaningful features of the electric image with high sensitivity over a broad working range. Since the network largely depends on spike timing, we finally discuss its suitability for implementation in robotic applications based on neuromorphic hardware.

Synaptic circuitry of identified neurons in the antennal lobe of Drosophila melanogaster

In Drosophila melanogaster olfactory sensory neurons (OSNs) establish synapses with projection neurons (PNs) and local interneurons within antennal lobe (AL) glomeruli. Substantial knowledge regarding this circuitry has been obtained by functional studies, whereas ultrastructural evidence of synaptic contacts is scarce. To fill this gap, we studied serial sections of three glomeruli using electron microscopy. Ectopic expression of a membrane-bound peroxidase allowed us to map synaptic sites along PN dendrites. Our data prove for the first time that each of the three major types of AL neurons is both pre- and postsynaptic to the other two types, as previously indicated by functional studies. PN dendrites carry a large proportion of output synapses, with approximately one output per every three input synapses. Detailed reconstructions of PN dendrites showed that these synapses are distributed unevenly, with input and output sites partially segregated along a proximal-distal gradient and the thinnest branches carrying solely input synapses. Moreover, our data indicate synapse clustering, as we found evidence of dendritic tiling of PN dendrites. PN output synapses exhibited T-shaped presynaptic densities, mostly arranged as tetrads. In contrast, output synapses from putative OSNs showed elongated presynaptic densities in which the T-bar platform was supported by several pedestals and contacted as many as 20 postsynaptic profiles. We also discovered synaptic contacts between the putative OSNs. The average synaptic density in the glomerular neuropil was about two synapses/µm(3) . These results are discussed with regard to current models of olfactory glomerular microcircuits across species.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

Morphology and physiology of the olfactory system of blood-feeding insects

Several blood-feeding (hematophagous) insects are vectors of a number of diseases including dengue, Chagas disease and leishmaniasis which persistently affect public health throughout Latin America. The vectors of those diseases include mosquitoes, triatomine bugs and sandflies. As vector control is an efficient way to prevent these illnesses it is important to understand the sensory biology of those harmful insects. We study the physiology of the olfactory system of those insects and apply that knowledge on the development of methods to manipulate their behavior. Here we review some of the latest information on insect olfaction with emphasis on hematophagous insects. The insect olfactory sensory neurons are housed inside hair-like organs called sensilla which are mainly distributed on the antenna and mouthparts. The identity of many of the odor compounds that those neurons detect are already known in hematophagous insects. They include several constituents of host (vertebrate) odor, sex, aggregation and alarm pheromones, and compounds related to egg-deposition behavior. Recent work has contributed significant knowledge on how odor information is processed in the insect first odor-processing center in the brain, the antennal lobe. The quality, quantity, and temporal features of the odor stimuli are encoded by the neural networks of the antennal lobe. Information regarding odor mixtures is also encoded. While natural mixtures evoke strong responses, synthetic mixtures that deviate from their natural counterparts in terms of key constituents or proportions of those constituents evoke weaker responses. The processing of olfactory information is largely unexplored in hematophagous insects. However, many aspects of their olfactory behavior are known. As in other insects, responses to relevant single odor compounds are weak while natural mixtures evoke strong responses. Future challenges include studying how information about odor mixtures is processed in their brain. This could help develop highly attractive synthetic odor blends to lure them into traps.

Olfactory coding in the insect brain: data and conjectures

Much progress has been made recently in understanding how olfactory coding works in insect brains. Here, I propose a wiring diagram for the major steps from the first processing network (the antennal lobe) to behavioral readout. I argue that the sequence of lateral inhibition in the antennal lobe, non-linear synapses, threshold-regulating gated spring network, selective lateral inhibitory networks across glomeruli, and feedforward inhibition to the lateral protocerebrum cover most of the experimental results from different research groups and model species. I propose that the main difference between mushroom bodies and the lateral protocerebrum is not about learned vs. innate behavior. Rather, mushroom bodies perform odor identification, whereas the lateral protocerebrum performs odor evaluation (both learned and innate). I discuss the concepts of labeled line and combinatorial coding and postulate that, under restrictive experimental conditions, these networks lead to an apparent existence of 'labeled line' coding for special odors. Modulatory networks are proposed as switches between different evaluating systems in the lateral protocerebrum. A review of experimental data and theoretical conjectures both contribute to this synthesis, creating new hypotheses for future research.