Frequency transitions in odor-evoked neural oscillations

Plasticity in inhibitory networks improves pattern separation in early olfactory processing

Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and changing ratios in complex mixtures. Changes in nectar production throughout the day and potentially many times within a forager's lifetime add to the complexity. The honeybee olfactory system, containing less than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity between AL neurons but its role in odor learning remains poorly understood. We used a computational network model and live imaging of the honeybee's AL to explore the neural mechanisms and functions of plasticity in the early olfactory system. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our Calcium imaging data support these predictions. Analysis of a Graph Convolutional Network in machine learning performing an odor categorization task revealed a similar mechanism of contrast enhancement. Our model provides insights into how inhibitory plasticity in the early olfactory network reshapes coding for efficient learning of complex odors.

Significance Statement

By combining computational modeling, machine learning, and analysis of calcium imaging data, we demonstrate that associative and non-associative plasticity in the honeybee antennal lobe (AL) - first relay of the insect olfactory system - work together to enhance the contrast between rewarded and unrewarded odors. Training the AL's inhibitory network within specific odor environments enables the suppression of neural responses to common odor components, while amplifying responses to distinctive ones. This study sheds light on the olfactory system's ability to adapt and efficiently learn new odor-reward associations across varying environments, and it proposes innovative, energy-efficient principles applicable to artificial intelligence.

Olfactory system structure and function in newly hatched and adult locusts

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.

Fly seizure EEG: field potential activity in the Drosophila brain

Hypersynchronous neural activity is a characteristic feature of seizures. Although many Drosophila mutants of epilepsy-related genes display clear behavioral spasms and motor unit hyperexcitability, field potential measurements of aberrant hypersynchronous activity across brain regions during seizures have yet to be described. Here, we report a straightforward method to observe local field potentials (LFPs) from the Drosophila brain to monitor ensemble neural activity during seizures in behaving tethered flies. High frequency stimulation across the brain reliably triggers a stereotypic sequence of electroconvulsive seizure (ECS) spike discharges readily detectable in the dorsal longitudinal muscle (DLM) and coupled with behavioral spasms. During seizure episodes, the LFP signal displayed characteristic large-amplitude oscillations with a stereotypic temporal correlation to DLM flight muscle spiking. ECS-related LFP events were clearly distinct from rest- and flight-associated LFP patterns. We further characterized the LFP activity during different types of seizures originating from genetic and pharmacological manipulations. In the 'bang-sensitive' sodium channel mutant bangsenseless (bss), the LFP pattern was prolonged, and the temporal correlation between LFP oscillations and DLM discharges was altered. Following administration of the pro-convulsant GABA_A blocker picrotoxin, we uncovered a qualitatively different LFP activity pattern, which consisted of a slow (1-Hz), repetitive, waveform, closely coupled with DLM bursting and behavioral spasms. Our approach to record brain LFPs presents an initial framework for electrophysiological analysis of the complex brain-wide activity patterns in the large collection of Drosophila excitability mutants.

Olfactory encoding within the insect antennal lobe: The emergence and role of higher order temporal correlations in the dynamics of antennal lobe spiking activity

In this review, we focus on the antennal lobe (AL) of three insect species - the fruit fly, sphinx moth, and locust. We first review the experimentally elucidated anatomy and physiology of the early olfactory system of each species; empirical studies of AL activity, however, often focus on assessing firing rates (averaged over time scales of about 100 ms), and hence the AL odor code is often analyzed in terms of a temporally evolving vector of firing rates. However, such a perspective necessarily misses the possibility of higher order temporal correlations in spiking activity within a single cell and across multiple cells over shorter time scales (of about 10 ms). Hence, we then review our prior theoretical work, where we constructed biophysically detailed, species-specific AL models within the fly, moth, and locust, finding that in each case higher order temporal correlations in spiking naturally emerge from model dynamics (i.e., without a prioriincorporation of elements designed to produce correlated activity). We therefore use our theoretical work to argue the perspective that temporal correlations in spiking over short time scales, which have received little experimental attention to-date, may provide valuable coding dimensions (complementing the coding dimensions provided by the vector of firing rates) that nature has exploited in the encoding of odors within the AL. We further argue that, if the AL does indeed utilize temporally correlated activity to represent odor information, such an odor code could be naturally and easily deciphered within the Mushroom Body.

Adaptive temporal processing of odor stimuli

The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals’ physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Slow presynaptic mechanisms that mediate adaptation in the olfactory pathway of Drosophila

The olfactory system encodes odor stimuli as combinatorial activity of populations of neurons whose response depends on stimulus history. How and on which timescales previous stimuli affect these combinatorial representations remains unclear. We use in vivo optical imaging in Drosophila to analyze sensory adaptation at the first synaptic step along the olfactory pathway. We show that calcium signals in the axon terminals of olfactory receptor neurons (ORNs) do not follow the same adaptive properties as the firing activity measured at the antenna. While ORNs calcium responses are sustained on long timescales, calcium signals in the postsynaptic projection neurons (PNs) adapt within tens of seconds. We propose that this slow component of the postsynaptic response is mediated by a slow presynaptic depression of vesicle release and enables the combinatorial population activity of PNs to adjust to the mean and variance of fluctuating odor stimuli.

Odor Stimuli: Not Just Chemical Identity

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals' physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Differential effects of adaptation on odor discrimination

Adaptation of neural responses is ubiquitous in sensory systems and can potentially facilitate many important computational functions. Here we examined this issue with a well-constrained computational model of the early olfactory circuits. In the insect olfactory system, the responses of olfactory receptor neurons (ORNs) on the antennae adapt over time. We found that strong adaptation of sensory input is important for rapidly detecting a fresher stimulus encountered in the presence of other background cues and for faithfully representing its identity. However, when the overlapping odorants were chemically similar, we found that adaptation could alter the representation of these odorants to emphasize only distinguishing features. This work demonstrates novel roles for peripheral neurons during olfactory processing in complex environments. NEW & NOTEWORTHY Olfactory systems face the problem of distinguishing salient information from a complex olfactory environment. The neural representations of specific odor sources should be consistent regardless of the background. How are olfactory representations robust to varying environmental interference? We show that in locusts the extraction of salient information begins in the periphery. Olfactory receptor neurons adapt in response to odorants. Adaptation can provide a computational mechanism allowing novel odorant components to be highlighted during complex stimuli.

Duration analysis using matching pursuit algorithm reveals longer bouts of gamma rhythm

The gamma rhythm (30–80 Hz), often associated with high-level cortical functions, is believed to provide a temporal reference frame for spiking activity, for which it should have a stable center frequency and linear phase for an extended duration. However, recent studies that have estimated the power and phase of gamma as a function of time suggest that gamma occurs in short bursts and lacks the temporal structure required to act as a reference frame. Here, we show that the bursty appearance of gamma arises from the variability in the spectral estimator used in these studies. To overcome this problem, we use another duration estimator based on a matching pursuit algorithm that robustly estimates the duration of gamma in simulated data. Applying this algorithm to gamma oscillations recorded from implanted microelectrodes in the primary visual cortex of awake monkeys, we show that the median gamma duration is greater than 300 ms, which is three times longer than previously reported values.

NEW & NOTEWORTHY Gamma oscillations (30–80 Hz) have been hypothesized to provide a temporal reference frame for coordination of spiking activity, but recent studies have shown that gamma occurs in very short bursts. We show that existing techniques have severely underestimated the rhythm duration, use a technique based on the Matching Pursuit algorithm, which provides a robust estimate of the duration, and show that the median duration of gamma is greater than 300 ms, much longer than previous estimates.

Engaging and disengaging recurrent inhibition coincides with sensing and unsensing of a sensory stimulus

Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed. In addition, we find that ON and OFF ensemble neural activities differ in their ability to recruit recurrent inhibition, entrain field-potential oscillations and more importantly in their relevance to behaviour (initiate versus reset conditioned responses). Notably, we find that a strikingly similar strategy is also used for encoding sound onsets and offsets in the marmoset auditory cortex. In sum, our results suggest a general approach where recurrent inhibition is associated with stimulus ‘recognition' and ‘derecognition'.

Sensory stimuli evoke temporally dynamic responses. Here the authors report that responses to odour onset and offset are orthogonally represented in the locust antennal lobe, differentially entrain oscillations, and propose a model in which they are necessary for initiation and termination of behaviour.

Non-invasive aerosol delivery and transport of gold nanoparticles to the brain

Targeted delivery of nanoscale carriers containing packaged payloads to the central nervous system has potential use in many diagnostic and therapeutic applications. Moreover, understanding of the bio-interactions of the engineered nanoparticles used for tissue-specific delivery by non-invasive delivery approaches are also of paramount interest. Here, we have examined this issue systematically in a relatively simple invertebrate model using insects. We synthesized 5 nm, positively charged gold nanoparticles (AuNPs) and targeted their delivery using the electrospray aerosol generator. Our results revealed that after the exposure of synthesized aerosol to the insect antenna, AuNPs reached the brain within an hour. Nanoparticle accumulation in the brain increased linearly with the exposure time. Notably, electrophysiological recordings from neurons in the insect brain several hours after exposure did not show any significant alterations in their spontaneous and odor-evoked spiking properties. Taken together, our findings reveal that aerosolized delivery of nanoparticles can be an effective non-invasive approach for delivering nanoparticles to the brain, and also presents an approach to monitor the short-term nano-biointeractions.

Oscillatory integration windows in neurons

Oscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly test this idea in the locust olfactory system. We find that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we show that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrate that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

Measuring activity in olfactory receptor neurons in Drosophila: Focus on spike amplitude

Olfactory responses at the receptor level have been thoroughly described in Drosophila melanogaster by electrophysiological methods. Single sensilla recordings (SSRs) measure neuronal activity in intact individuals in response to odors. For sensilla that contain more than one olfactory receptor neuron (ORN), their different spontaneous spike amplitudes can distinguish each signal under resting conditions. However, activity is mainly described by spike frequency. Some reports on ORN response dynamics studied two components in the olfactory responses of ORNs: a fast component that is reflected by the spike frequency and a slow component that is observed in the LFP (local field potential, the single sensillum counterpart of the electroantennogram, EAG). However, no apparent correlation was found between the two elements. In this report, we show that odorant stimulation produces two different effects in the fast component, affecting spike frequency and spike amplitude. Spike amplitude clearly diminishes at the beginning of a response, but it recovers more slowly than spike frequency after stimulus cessation, suggesting that ORNs return to resting conditions long after they recover a normal spontaneous spike frequency. Moreover, spike amplitude recovery follows the same kinetics as the slow voltage component measured by the LFP, suggesting that both measures are connected. These results were obtained in ab2 and ab3 sensilla in response to two odors at different concentrations. Both spike amplitude and LFP kinetics depend on odorant, concentration and neuron, suggesting that like the EAG they may reflect olfactory information.

Linking dynamics of the inhibitory network to the input structure

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network's response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives.

Spatially resolved time-frequency analysis of odour coding in the insect antennal lobe

Antennal lobes constitute the first neurophils in the insect brain involved in coding and processing of olfactory information. With their stereotyped functional and anatomical organization, they provide an accessible model with which to investigate information processing of an external stimulus in a neural network in vivo. Here, by combining functional calcium imaging with time-frequency analysis, we have been able to monitor the oscillatory components of neural activity upon olfactory stimulation. The aim of this study is to investigate the presence of stimulus-induced oscillatory patterns in the honeybee antennal lobe, and to analyse the distribution of those patterns across the antennal lobe glomeruli. Fast two-photon calcium imaging reveals the presence of low-frequency oscillations, the intensity of which is perturbed by an incoming stimulus. Moreover, analysis of the spatial arrangement of this activity indicates that it is not homogeneous throughout the antennal lobe. On the contrary, each glomerulus displays an odorant-specific time-frequency profile, and acts as a functional unit of the oscillatory activity. The presented approach allows simultaneous recording of complex activity patterns across several nodes of the antennal lobe, providing the means to better understand the network dynamics regulating olfactory coding and leading to perception.

Neuroethology of Olfactory-Guided Behavior and Its Potential Application in the Control of Harmful Insects

Harmful insects include pests of crops and storage goods, and vectors of human and animal diseases. Throughout their history, humans have been fighting them using diverse methods. The fairly recent development of synthetic chemical insecticides promised efficient crop and health protection at a relatively low cost. However, the negative effects of those insecticides on human health and the environment, as well as the development of insect resistance, have been fueling the search for alternative control tools. New and promising alternative methods to fight harmful insects include the manipulation of their behavior using synthetic versions of "semiochemicals", which are natural volatile and non-volatile substances involved in the intra- and/or inter-specific communication between organisms. Synthetic semiochemicals can be used as trap baits to monitor the presence of insects, so that insecticide spraying can be planned rationally (i.e., only when and where insects are actually present). Other methods that use semiochemicals include insect annihilation by mass trapping, attract-and- kill techniques, behavioral disruption, and the use of repellents. In the last decades many investigations focused on the neural bases of insect's responses to semiochemicals. Those studies help understand how the olfactory system detects and processes information about odors, which could lead to the design of efficient control tools, including odor baits, repellents or ways to confound insects. Here we review our current knowledge about the neural mechanisms controlling olfactory responses to semiochemicals in harmful insects. We also discuss how this neuroethology approach can be used to design or improve pest/vector management strategies.

Granule cell excitability regulates gamma and beta oscillations in a model of the olfactory bulb dendrodendritic microcircuit

Odors evoke gamma (40-100 Hz) and beta (20-30 Hz) oscillations in the local field potential (LFP) of the mammalian olfactory bulb (OB). Gamma (and possibly beta) oscillations arise from interactions in the dendrodendritic microcircuit between excitatory mitral cells (MCs) and inhibitory granule cells (GCs). When cortical descending inputs to the OB are blocked, beta oscillations are extinguished whereas gamma oscillations become larger. Much of this centrifugal input targets inhibitory interneurons in the GC layer and regulates the excitability of GCs, which suggests a causal link between the emergence of beta oscillations and GC excitability. We investigate the effect that GC excitability has on network oscillations in a computational model of the MC-GC dendrodendritic network with Ca(2+)-dependent graded inhibition. Results from our model suggest that when GC excitability is low, the graded inhibitory current mediated by NMDA channels and voltage-dependent Ca(2+) channels (VDCCs) is also low, allowing MC populations to fire in the gamma frequency range. When GC excitability is increased, the activation of NMDA receptors and other VDCCs is also increased, allowing the slow decay time constants of these channels to sustain beta-frequency oscillations. Our model argues that Ca(2+) flow through VDCCs alone could sustain beta oscillations and that the switch between gamma and beta oscillations can be triggered by an increase in the excitability state of a subpopulation of GCs.

Classification of odorants across layers in locust olfactory pathway

Olfactory processing takes place across multiple layers of neurons from the transduction of odorants in the periphery, to odor quality processing, learning, and decision making in higher olfactory structures. In insects, projection neurons (PNs) in the antennal lobe send odor information to the Kenyon cells (KCs) of the mushroom bodies and lateral horn neurons (LHNs). To examine the odor information content in different structures of the insect brain, antennal lobe, mushroom bodies and lateral horn, we designed a model of the olfactory network based on electrophysiological recordings made in vivo in the locust. We found that populations of all types (PNs, LHNs, and KCs) had lower odor classification error rates than individual cells of any given type. This improvement was quantitatively different from that observed using uniform populations of identical neurons compared with spatially structured population of neurons tuned to different odor features. This result, therefore, reflects an emergent network property. Odor classification improved with increasing stimulus duration: for similar odorants, KC and LHN ensembles reached optimal discrimination within the first 300-500 ms of the odor response. Performance improvement with time was much greater for a population of cells than for individual neurons. We conclude that, for PNs, LHNs, and KCs, ensemble responses are always much more informative than single-cell responses, despite the accumulation of noise along with odor information.

Spatiotemporal Coding of Individual Chemicals by the Gustatory System

Four of the five major sensory systems (vision, olfaction, somatosensation, and audition) are thought to use different but partially overlapping sets of neurons to form unique representations of vast numbers of stimuli. The only exception is gustation, which is thought to represent only small numbers of basic taste categories. However, using new methods for delivering tastant chemicals and making electrophysiological recordings from the tractable gustatory system of the moth Manduca sexta, we found chemical-specific information is as follows: (1) initially encoded in the population of gustatory receptor neurons as broadly distributed spatiotemporal patterns of activity; (2) dramatically integrated and temporally transformed as it propagates to monosynaptically connected second-order neurons; and (3) observed in tastant-specific behavior. Our results are consistent with an emerging view of the gustatory system: rather than constructing basic taste categories, it uses a spatiotemporal population code to generate unique neural representations of individual tastant chemicals.

SIGNIFICANCE STATEMENT

Our results provide a new view of taste processing. Using a new, relatively simple model system and a new set of techniques to deliver taste stimuli and to examine gustatory receptor neurons and their immediate followers, we found no evidence for labeled line connectivity, or basic taste categories such as sweet, salty, bitter, and sour. Rather, individual tastant chemicals are represented as patterns of spiking activity distributed across populations of receptor neurons. These representations are transformed substantially as multiple types of receptor neurons converge upon follower neurons, leading to a combinatorial coding format that uniquely, rapidly, and efficiently represents individual taste chemicals. Finally, we found that the information content of these neurons can drive tastant-specific behavior.

Behavioural correlates of combinatorial versus temporal features of odour codes

Most sensory stimuli evoke spiking responses that are distributed across neurons and are temporally structured. Whether the temporal structure of ensemble activity is modulated to facilitate different neural computations is not known. Here, we investigated this issue in the insect olfactory system. We found that an odourant can generate synchronous or asynchronous spiking activity across a neural ensemble in the antennal lobe circuit depending on its relative novelty with respect to a preceding stimulus. Regardless of variations in temporal spiking patterns, the activated combinations of neurons robustly represented stimulus identity. Consistent with this interpretation, locusts reliably recognized both solitary and sequential introductions of trained odourants in a quantitative behavioural assay. However, predictable behavioural responses across locusts were observed only to novel stimuli that evoked synchronized spiking patterns across neural ensembles. Hence, our results indicate that the combinatorial ensemble response encodes for stimulus identity, whereas the temporal structure of the ensemble response selectively emphasizes novel stimuli.

In the olfactory system, odourants typically evoke spiking responses in neurons that are both spatially and temporally structured. Here, the authors demonstrate that odour identity is encoded purely by the combinations of neurons activated and is insensitive to changes in temporal structure.

Trade-off between information format and capacity in the olfactory system

As information about the sensory environment passes between layers within the nervous system, the format of the information often changes. To examine how information format affects the capacity of neurons to represent stimuli, we measured the rate of information transmission in olfactory neurons in intact, awake locusts (Schistocerca americana) while pharmacologically manipulating patterns of correlated neuronal activity. Blocking the periodic inhibition underlying odor-elicited neural oscillatory synchronization increased information transmission rates. This suggests oscillatory synchrony, which serves other information processing roles, comes at a cost to the speed with which neurons can transmit information. Our results provide an example of a trade-off between benefits and costs in neural information processing.

Learning modifies odor mixture processing to improve detection of relevant components

Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.

Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons

In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs), all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs) within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information—primarily the identity and intensity of the stimulus—is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency) or on the average response of many (firing rate). Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations.

Understanding how sensory signals are optimally encoded by nervous systems is of strong interest to neuroscientists, and also to engineers as it may lead to more efficient artificial detection systems. This is particularly relevant to olfaction, because the current electronic noses are far outperformed by their biological counterparts in terms of speed and sensitivity. We here use the moth sex pheromone processing system as a relatively simple model to understand early olfactory coding. We found that performance increases when olfactory information passes from first- to second-order neurons. Second-order neurons respond on average with shorter latency and higher sensitivity than first-order neurons. We show that two critical factors, convergent architecture and neuronal heterogeneity, are needed to account for increased performance.

Central processing in the mushroom bodies

The mushroom bodies in the insect brain serve as a central information processing area. Here, focusing mainly on olfaction, we discuss functionally related roles the mushroom bodies play in signal gain control, response sparsening, the separation of similar signals (decorrelation), and learning and memory. In sum, the mushroom bodies assemble and format a context-appropriate representation of the insect's world.

Temporal features of spike trains in the moth antennal lobe revealed by a comparative time-frequency analysis

The discrimination of complex sensory stimuli in a noisy environment is an immense computational task. Sensory systems often encode stimulus features in a spatiotemporal fashion through the complex firing patterns of individual neurons. To identify these temporal features, we have developed an analysis that allows the comparison of statistically significant features of spike trains localized over multiple scales of time-frequency resolution. Our approach provides an original way to utilize the discrete wavelet transform to process instantaneous rate functions derived from spike trains, and select relevant wavelet coefficients through statistical analysis. Our method uncovered localized features within olfactory projection neuron (PN) responses in the moth antennal lobe coding for the presence of an odor mixture and the concentration of single component odorants, but not for compound identities. We found that odor mixtures evoked earlier responses in biphasic response type PNs compared to single components, which led to differences in the instantaneous firing rate functions with their signal power spread across multiple frequency bands (ranging from 0 to 45.71 Hz) during a time window immediately preceding behavioral response latencies observed in insects. Odor concentrations were coded in excited response type PNs both in low frequency band differences (2.86 to 5.71 Hz) during the stimulus and in the odor trace after stimulus offset in low (0 to 2.86 Hz) and high (22.86 to 45.71 Hz) frequency bands. These high frequency differences in both types of PNs could have particular relevance for recruiting cellular activity in higher brain centers such as mushroom body Kenyon cells. In contrast, neurons in the specialized pheromone-responsive area of the moth antennal lobe exhibited few stimulus-dependent differences in temporal response features. These results provide interesting insights on early insect olfactory processing and introduce a novel comparative approach for spike train analysis applicable to a variety of neuronal data sets.

A spatiotemporal coding mechanism for background-invariant odor recognition

Sensory stimuli evoke neural activity that evolves over time. What features of these spatiotemporal responses allow the robust encoding of stimulus identity in a multistimulus environment? Here we examined this issue in the locust (Schistocerca americana) olfactory system. We found that sensory responses evoked by an odorant (foreground) varied when presented atop or after an ongoing stimulus (background). These inconsistent sensory inputs triggered dynamic reorganization of ensemble activity in the downstream antennal lobe. As a result, partial pattern matches between neural representations encoding the same foreground stimulus across conditions were achieved. The degree and segments of response overlaps varied; however, any overlap observed was sufficient to drive background-independent responses in the downstream neural population. Notably, recognition performance of locusts in behavioral assays correlated well with our physiological findings. Hence, our results reveal how background-independent recognition of odors can be achieved using spatiotemporal patterns of neural activity.

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models.

Intensity invariant dynamics and odor-specific latencies in olfactory receptor neuron response

Odors elicit spatiotemporal patterns of activity in the brain. Spatial patterns arise from the specificity of the interaction between odorants and odorant receptors expressed in different olfactory receptor neurons (ORNs), but the origin of temporal patterns of activity and their role in odor coding remain unclear. We investigate how physiological aspects of ORN response and physical aspects of odor stimuli give rise to diverse responses in Drosophila ORNs. We show that odor stimuli have intrinsic dynamics that depend on odor type and strongly affect ORN response. Using linear-nonlinear modeling to remove the contribution of the stimulus dynamics from the ORN dynamics, we study the physiological properties of the response to different odorants and concentrations. For several odorants and receptor types, the ORN response dynamics normalized by the peak response are independent of stimulus intensity for a large portion of the dynamic range of the neuron. Adaptation to a background odor changes the gain and dynamic range of the response but does not affect normalized response dynamics. Stimulating ORNs with various odorants reveals significant odor-dependent delays in the ORN response functions. However, these differences can be dominated by differences in stimulus dynamics. In one case the response of one ORN to two odorants is predicted solely from measurements of the odor signals. Within a large portion of their dynamic range, ORNs can capture information about stimulus dynamics independently from intensity while introducing odor-dependent delays. How insects might use odor-specific stimulus dynamics and ORN dynamics in discrimination and navigation tasks remains an open question.

Responses of single neurons and neuronal ensembles in frog first- and second-order olfactory neurons

A major challenge in sensory neuroscience is to elucidate the coding and processing of stimulus representations in successive populations of neurons. Here we recorded the spiking activity of receptor neurons (RNs) and mitral/tufted cells (MCs) in the frog olfactory epithelium and olfactory bulb respectively, in response to four odorants applied at precisely controlled concentrations. We compared how RN responses are translated in MCs. We examined the time course of the instantaneous firing frequency before and after stimulation in neuron ensembles and the dependency on odorant concentration of the number of action potentials fired in a preselected 5-s time window (dose-response curves) in both single neurons and neuron ensembles. In RNs and MCs, the dose-response curves typically increase then decrease and are well described by alpha functions. We established the main quantitative properties of these curves, including the distributions of concentrations at threshold and maximum responses. We showed that the main transformations occurring in the transition from RNs to MCs is the lowering of the firing threshold and a large decrease in the total number of spikes fired. We also found that the number of action potentials fired by recorded neurons and hence their energy consumption is independent of odorant concentration, and that this is a consequence of their time- and concentration-dependent activities. This article is part of a Special Issue entitled Neural Coding 2012.

Mixture and odorant processing in the olfactory systems of insects: a comparative perspective

Natural olfactory stimuli are often complex mixtures of volatiles, of which the identities and ratios of constituents are important for odor-mediated behaviors. Despite this importance, the mechanism by which the olfactory system processes this complex information remains an area of active study. In this review, we describe recent progress in how odorants and mixtures are processed in the brain of insects. We use a comparative approach toward contrasting olfactory coding and the behavioral efficacy of mixtures in different insect species, and organize these topics around four sections: (1) Examples of the behavioral efficacy of odor mixtures and the olfactory environment; (2) mixture processing in the periphery; (3) mixture coding in the antennal lobe; and (4) evolutionary implications and adaptations for olfactory processing. We also include pertinent background information about the processing of individual odorants and comparative differences in wiring and anatomy, as these topics have been richly investigated and inform the processing of mixtures in the insect olfactory system. Finally, we describe exciting studies that have begun to elucidate the role of the processing of complex olfactory information in evolution and speciation.

Parallel processing in the honeybee olfactory pathway: structure, function, and evolution

Animals face highly complex and dynamic olfactory stimuli in their natural environments, which require fast and reliable olfactory processing. Parallel processing is a common principle of sensory systems supporting this task, for example in visual and auditory systems, but its role in olfaction remained unclear. Studies in the honeybee focused on a dual olfactory pathway. Two sets of projection neurons connect glomeruli in two antennal-lobe hemilobes via lateral and medial tracts in opposite sequence with the mushroom bodies and lateral horn. Comparative studies suggest that this dual-tract circuit represents a unique adaptation in Hymenoptera. Imaging studies indicate that glomeruli in both hemilobes receive redundant sensory input. Recent simultaneous multi-unit recordings from projection neurons of both tracts revealed widely overlapping response profiles strongly indicating parallel olfactory processing. Whereas lateral-tract neurons respond fast with broad (generalistic) profiles, medial-tract neurons are odorant specific and respond slower. In analogy to “what-” and “where” subsystems in visual pathways, this suggests two parallel olfactory subsystems providing “what-” (quality) and “when” (temporal) information. Temporal response properties may support across-tract coincidence coding in higher centers. Parallel olfactory processing likely enhances perception of complex odorant mixtures to decode the diverse and dynamic olfactory world of a social insect.

Parallel processing via a dual olfactory pathway in the honeybee

In their natural environment, animals face complex and highly dynamic olfactory input. Thus vertebrates as well as invertebrates require fast and reliable processing of olfactory information. Parallel processing has been shown to improve processing speed and power in other sensory systems and is characterized by extraction of different stimulus parameters along parallel sensory information streams. Honeybees possess an elaborate olfactory system with unique neuronal architecture: a dual olfactory pathway comprising a medial projection-neuron (PN) antennal lobe (AL) protocerebral output tract (m-APT) and a lateral PN AL output tract (l-APT) connecting the olfactory lobes with higher-order brain centers. We asked whether this neuronal architecture serves parallel processing and employed a novel technique for simultaneous multiunit recordings from both tracts. The results revealed response profiles from a high number of PNs of both tracts to floral, pheromonal, and biologically relevant odor mixtures tested over multiple trials. PNs from both tracts responded to all tested odors, but with different characteristics indicating parallel processing of similar odors. Both PN tracts were activated by widely overlapping response profiles, which is a requirement for parallel processing. The l-APT PNs had broad response profiles suggesting generalized coding properties, whereas the responses of m-APT PNs were comparatively weaker and less frequent, indicating higher odor specificity. Comparison of response latencies within and across tracts revealed odor-dependent latencies. We suggest that parallel processing via the honeybee dual olfactory pathway provides enhanced odor processing capabilities serving sophisticated odor perception and olfactory demands associated with a complex olfactory world of this social insect.

Functional roles for synaptic-depression within a model of the fly antennal lobe

Several experiments indicate that there exists substantial synaptic-depression at the synapses between olfactory receptor neurons (ORNs) and neurons within the drosophila antenna lobe (AL). This synaptic-depression may be partly caused by vesicle-depletion, and partly caused by presynaptic-inhibition due to the activity of inhibitory local neurons within the AL. While it has been proposed that this synaptic-depression contributes to the nonlinear relationship between ORN and projection neuron (PN) firing-rates, the precise functional role of synaptic-depression at the ORN synapses is not yet fully understood. In this paper we propose two hypotheses linking the information-coding properties of the fly AL with the network mechanisms responsible for ORNAL synaptic-depression. Our first hypothesis is related to variance coding of ORN firing-rate information — once stimulation to the ORNs is sufficiently high to saturate glomerular responses, further stimulation of the ORNs increases the regularity of PN spiking activity while maintaining PN firing-rates. The second hypothesis proposes a tradeoff between spike-time reliability and coding-capacity governed by the relative contribution of vesicle-depletion and presynaptic-inhibition to ORNAL synaptic-depression. Synaptic-depression caused primarily by vesicle-depletion will give rise to a very reliable system, whereas an equivalent amount of synaptic-depression caused primarily by presynaptic-inhibition will give rise to a less reliable system that is more sensitive to small shifts in odor stimulation. These two hypotheses are substantiated by several small analyzable toy models of the fly AL, as well as a more physiologically realistic large-scale computational model of the fly AL involving glomerular channels.

Understanding the intricacies of sensory processing is a major scientific challenge. In this paper we examine the early stages of the olfactory system of the fruit-fly. Many experiments have revealed a great deal regarding the architecture of this system, including the types of neurons within it, as well as the connections those neurons make amongst one another. In this paper we examine the potential dynamics produced by this neuronal network. Specifically, we construct a computational model of this early olfactory system and study the effects of synaptic-depression within this system. We find that the dynamics and coding properties of this system depend strongly on the strength, and sources of, synaptic-depression. This work has ramifications for understanding the coding properties of other insect olfactory systems, and perhaps even other sensory modalities in other animals.

Olfactory coding: tagging and tuning odor-activated synapses for memory

A recent study in the locust olfactory system shows how neuromodulators can alter the rules of synaptic plasticity to form associative memories through the use of 'tagged' synapses.

Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization

Synchronous neural oscillations are found throughout the brain and are thought to contribute to neural coding and the propagation of activity. Several proposed mechanisms of synchronization have gained support through combined theoretical and experimental investigation, including mechanisms based on coupling and correlated input. Here, we ask how correlation-induced synchrony is affected by physiological heterogeneity across neurons. To address this question, we examined cell-to-cell differences in phase-response curves (PRCs), which characterize the response of periodically firing neurons to weak perturbations. Using acute slice electrophysiology, we measured PRCs across a single class of principal neurons capable of sensory-evoked oscillations in vivo: the olfactory bulb mitral cells (MCs). Periodically firing MCs displayed a broad range of PRCs, each of which was well fit by a simple three-parameter model. MCs also displayed differences in firing rate-current relationships and in preferred firing rate ranges. Both the observed PRC heterogeneity and moderate firing rate differences (∼10 Hz) separately reduced the maximum correlation-induced synchrony between MCs by up to 25-30%. Simulations further demonstrated that these components of heterogeneity alone were sufficient to account for the difference in synchronization among heterogeneous vs. homogeneous populations in vitro. Within this simulation framework, independent modulation of specific PRC features additionally revealed which aspects of PRC heterogeneity most strongly impact correlation-induced synchronization. Finally, we demonstrated good agreement of novel mathematical theory with our experimental and simulation results, providing a theoretical basis for the influence of heterogeneity on correlation-induced neural synchronization.

Synaptic inhibition controls transient oscillatory synchronization in a model of the insect olfactory system

In a variety of neuronal systems it has been hypothesized that inhibitory interneurons corral principal neurons into synchronously firing groups that encode sensory information and sub-serve behavior (Buzsáki and Chrobak, 1995; Buzsáki, 2008). This mechanism is particularly relevant to the olfactory system where spatiotemporal patterns of projection neuron (PN) activity act as robust markers of odor attributes (Laurent et al., 1996; Wehr and Laurent, 1996). In the insect antennal lobe (AL), a network of local inhibitory interneurons arborizes extensively throughout the AL (Leitch and Laurent, 1996) providing inhibitory input to the cholinergic PNs. Our theoretical work has attempted to elaborate the exact role of inhibition in the generation of odor specific PN responses (Bazhenov et al., 2001a,b; Assisi et al., 2011). In large-scale AL network models we characterized the inhibitory sub-network by its coloring (Assisi et al., 2011) and showed that it can entrain excitatory PNs to the odor specific patterns of transient synchronization. In this focused review, we further examine the dynamics of entrainment in more detail by simulating simple model networks in various parameter regimes. Our simulations in conjunction with earlier studies point to the key role played by lateral (between inhibitory interneurons) and feedback (from inhibitory interneurons to principal cells) inhibition in the generation of experimentally observed patterns of transient synchrony.

Spontaneous olfactory receptor neuron activity determines follower cell response properties

Noisy or spontaneous activity is common in neural systems and poses a challenge to detecting and discriminating signals. Here we use the locust to answer fundamental questions about noise in the olfactory system: Where does spontaneous activity originate? How is this activity propagated or reduced throughout multiple stages of neural processing? What mechanisms favor the detection of signals despite the presence of spontaneous activity? We found that spontaneous activity long observed in the secondary projection neurons (PNs) originates almost entirely from the primary olfactory receptor neurons (ORNs) rather than from spontaneous circuit interactions in the antennal lobe, and that spontaneous activity in ORNs tonically depolarizes the resting membrane potentials of their target PNs and local neurons (LNs) and indirectly tonically depolarizes tertiary Kenyon cells (KCs). However, because these neurons have different response thresholds, in the absence of odor stimulation, ORNs and PNs display a high spontaneous firing rate but KCs are nearly silent. Finally, we used a simulation of the olfactory network to show that discrimination of signal and noise in the KCs is best when threshold levels are set so that baseline activity in PNs persists. Our results show how the olfactory system benefits from making a signal detection decision after a point of maximal information convergence, e.g., after KCs pool inputs from many PNs.

Olfactory ecology and the processing of complex mixtures

Natural olfactory stimuli typically are mixtures of which the identities, concentrations, and ratios of chemical constituents are important for many odor-mediated behaviors. Despite abundant behavioral examples, links between odor-evoked behavior and the processing and discrimination of complex olfactory stimuli remains an area of active study. Coupling electrophysiological and behavioral experiments, recent studies in a variety of different insect models have provided new insights into the perceptual and neural mechanisms about how natural olfactory stimuli are processed, and how plasticity and internal state of the insect may influence the odor representation. These studies show that complex stimuli are represented in unique percepts that are different from their individual constituents, and that the representation may be modulated by experience and influenced by other sensory modalities.

Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee

The honeybee Apis mellifera has a remarkable ability to detect and locate food sources during foraging, and to associate odor cues with food rewards. In the honeybee's olfactory system, sensory input is first processed in the antennal lobe (AL) network. Uniglomerular projection neurons (PNs) convey the sensory code from the AL to higher brain regions via two parallel but anatomically distinct pathways, the lateral and the medial antenno-cerebral tract (l- and m-ACT). Neurons innervating either tract show characteristic differences in odor selectivity, concentration dependence, and representation of mixtures. It is still unknown how this differential stimulus representation is achieved within the AL network. In this contribution, we use a computational network model to demonstrate that the experimentally observed features of odor coding in PNs can be reproduced by varying lateral inhibition and gain control in an otherwise unchanged AL network. We show that odor coding in the l-ACT supports detection and accurate identification of weak odor traces at the expense of concentration sensitivity, while odor coding in the m-ACT provides the basis for the computation and following of concentration gradients but provides weaker discrimination power. Both coding strategies are mutually exclusive, which creates a tradeoff between detection accuracy and sensitivity. The development of two parallel systems may thus reflect an evolutionary solution to this problem that enables honeybees to achieve both tasks during bee foraging in their natural environment, and which could inspire the development of artificial chemosensory devices for odor-guided navigation in robots.

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Sensory systems, both in the living and in machines, have to be optimized with respect to their environmental conditions. The pheromone subsystem of the olfactory system of moths is a particularly well-defined example in which rapid variations of odor content in turbulent plumes require fast, concentration-invariant neural representations. It is not clear how cellular and network mechanisms in the moth antennal lobe contribute to coding efficiency. Using computational modeling, we show that intrinsic potassium currents (I(A) and I(SK)) in projection neurons may combine with extrinsic inhibition from local interneurons to implement a dual latency code for both pheromone identity and intensity. The mean latency reflects stimulus intensity, whereas latency differences carry concentration-invariant information about stimulus identity. In accordance with physiological results, the projection neurons exhibit a multiphasic response of inhibition-excitation-inhibition. Together with synaptic inhibition, intrinsic currents I(A) and I(SK) account for the first and second inhibitory phases and contribute to a rapid encoding of pheromone information. The first inhibition plays the role of a reset to limit variability in the time to first spike. The second inhibition prevents responses of excessive duration to allow tracking of intermittent stimuli.

Detailed Characterization of Local Field Potential Oscillations and Their Relationship to Spike Timing in the Antennal Lobe of the Moth Manduca sexta

The transient oscillatory model of odor identity encoding seeks to explain how odorants with spatially overlapped patterns of input into primary olfactory networks can be discriminated. This model provides several testable predictions about the distributed nature of network oscillations and how they control spike timing. To test these predictions, 16 channel electrode arrays were placed within the antennal lobe (AL) of the moth Manduca sexta. Unitary spiking and multi site local field potential (LFP) recordings were made during spontaneous activity and in response to repeated presentations of an odor panel. We quantified oscillatory frequency, cross correlations between LFP recording sites, and spike-LFP phase relationships. We show that odor-driven AL oscillations in Manduca are frequency modulating (FM) from ∼100 to 30 Hz; this was odorant and stimulus duration dependent. FM oscillatory responses were localized to one or two recording sites suggesting a localized (perhaps glomerular) not distributed source. LFP cross correlations further demonstrated that only a small (r < 0.05) distributed and oscillatory component was present. Cross spectral density analysis demonstrated the frequency of these weakly distributed oscillations was state dependent (spontaneous activity = 25-55 Hz; odor-driven = 55-85 Hz). Surprisingly, vector strength analysis indicated that unitary phase locking of spikes to the LFP was strongest during spontaneous activity and dropped significantly during responses. Application of bicuculline, a GABA(A) receptor antagonist, significantly lowered the frequency content of odor-driven distributed oscillatory activity. Bicuculline significantly reduced spike phase locking generally, but the ubiquitous pattern of increased phase locking during spontaneous activity persisted. Collectively, these results indicate that oscillations perform poorly as a stimulus-mediated spike synchronizing mechanism for Manduca and hence are incongruent with the transient oscillatory model.

Insect olfactory coding and memory at multiple timescales

Insects can learn, allowing them great flexibility for locating seasonal food sources and avoiding wily predators. Because insects are relatively simple and accessible to manipulation, they provide good experimental preparations for exploring mechanisms underlying sensory coding and memory. Here we review how the intertwining of memory with computation enables the coding, decoding, and storage of sensory experience at various stages of the insect olfactory system. Individual parts of this system are capable of multiplexing memories at different timescales, and conversely, memory on a given timescale can be distributed across different parts of the circuit. Our sampling of the olfactory system emphasizes the diversity of memories, and the importance of understanding these memories in the context of computations performed by different parts of a sensory system.

Modelling the signal delivered by a population of first-order neurons in a moth olfactory system

A statistical model of the population of first-order olfactory receptor neurons (ORNs) is proposed and analysed. It describes the relationship between stimulus intensity (odour concentration) and coding variables such as rate and latency of the population of several thousand sex-pheromone sensitive ORNs in male moths. Although these neurons likely express the same olfactory receptor, they exhibit, at any concentration, a relatively large heterogeneity of responses in both peak firing frequency and latency of the first action potential fired after stimulus onset. The stochastic model is defined by a multivariate distribution of six model parameters that describe the dependence of the peak firing rate and the latency on the stimulus dose. These six parameters and their mutual linear correlations were estimated from experiments in single ORNs and included in the multidimensional model distribution. The model is utilized to reconstruct the peak firing rate and latency of the message sent to the brain by the whole ORN population at different stimulus intensities and to establish their main qualitative and quantitative properties. Finally, these properties are shown to be in agreement with those found previously in a vertebrate ORN population. This article is part of a Special Issue entitled: Neural Coding.

The neurobiology of insect olfaction: sensory processing in a comparative context

The simplicity and accessibility of the olfactory systems of insects underlie a body of research essential to understanding not only olfactory function but also general principles of sensory processing. As insect olfactory neurobiology takes advantage of a variety of species separated by millions of years of evolution, the field naturally has yielded some conflicting results. Far from impeding progress, the varieties of insect olfactory systems reflect the various natural histories, adaptations to specific environments, and the roles olfaction plays in the life of the species studied. We review current findings in insect olfactory neurobiology, with special attention to differences among species. We begin by describing the olfactory environments and olfactory-based behaviors of insects, as these form the context in which neurobiological findings are interpreted. Next, we review recent work describing changes in olfactory systems as adaptations to new environments or behaviors promoting speciation. We proceed to discuss variations on the basic anatomy of the antennal (olfactory) lobe of the brain and higher-order olfactory centers. Finally, we describe features of olfactory information processing including gain control, transformation between input and output by operations such as broadening and sharpening of tuning curves, the role of spiking synchrony in the antennal lobe, and the encoding of temporal features of encounters with an odor plume. In each section, we draw connections between particular features of the olfactory neurobiology of a species and the animal's life history. We propose that this perspective is beneficial for insect olfactory neurobiology in particular and sensory neurobiology in general.

A subpopulation of mushroom body intrinsic neurons is generated by protocerebral neuroblasts in the tobacco hornworm moth, Manduca sexta (Sphingidae, Lepidoptera)

Subpopulations of Kenyon cells, the intrinsic neurons of the insect mushroom bodies, are typically sequentially generated by dedicated neuroblasts that begin proliferating during embryogenesis. When present, Class III Kenyon cells are thought to be the first born population of neurons by virtue of the location of their cell somata, farthest from the position of the mushroom body neuroblasts. In the adult tobacco hornworm moth Manduca sexta, the axons of Class III Kenyon cells form a separate Y tract and dorsal and ventral lobelet; surprisingly, these distinctive structures are absent from the larval Manduca mushroom bodies. BrdU labeling and immunohistochemical staining reveal that Class III Kenyon cells are in fact born in the mid-larval through adult stages. The peripheral position of their cell bodies is due to their genesis from two previously undescribed protocerebral neuroblasts distinct from the mushroom body neuroblasts that generate the other Kenyon cell types. These findings challenge the notion that all Kenyon cells are produced solely by the mushroom body neuroblasts, and may explain why Class III Kenyon cells are found sporadically across the insects, suggesting that when present, they may arise through de novo recruitment of neuroblasts outside of the mushroom bodies. In addition, lifelong neurogenesis by both the Class III neuroblasts and the mushroom body neuroblasts was observed, raising the possibility that adult neurogenesis may play a role in mushroom body function in Manduca.

Stability of fast oscillations in the mammalian olfactory bulb: experiments and modeling

In the rat olfactory bulb (OB), fast oscillations of the local field potential (LFP) are observed during the respiratory cycle. Gamma-range oscillations (40-90 Hz) occur at the end of inspiration, followed by beta-range oscillations (15-30 Hz) during exhalation. These oscillations are highly stereotypical, and their frequencies are stable under various conditions. In this study, we investigate the effect of stimulus intensity on activity in the OB. Using a double-cannulation protocol, we showed that although the frequency of the LFP oscillation does depend on the respiratory cycle phase, it is relatively independent of the intensity of odorant stimulation. In contrast, we found that the individual firing rate of mitral OB cells dramatically changed with the intensity of the stimulation. This suggests that OB fast oscillation parameters, particularly frequency, are fully determined by intrinsic OB network parameters. To test this hypothesis, we explored a model of the OB where fast oscillations are generated by the interplay between excitatory mitral/tufted cells and inhibitory granule cells with graded inhibition. We found that our model has two distinct activity regimes depending on the amount of noise. In a low-noise regime, the model displays oscillation in the beta range with a stable frequency across a wide range of excitatory inputs. In a high-noise regime, the model displays oscillatory dynamics with irregular cell discharges and fast oscillations, similar to what is observed during gamma oscillations but without stability of the oscillation frequency with respect to the network external input. Simulations of the full model and theoretical studies of the network's linear response show that the characteristics of the low-noise regime are induced by non-linearities in the model, notably, the saturation of graded inhibition. Finally, we discuss how this model can account for the experimentally observed stability of the oscillatory regimes.