Relation between stimulus and response in frog olfactory receptor neurons in vivo

Estimation of firing rate from instantaneous interspike intervals

The rate coding hypothesis is the oldest and still one of the most accepted hypotheses of neural coding. Consequently, many approaches have been devised for the firing rate estimation, ranging from simple binning of the time axis to advanced statistical methods. Nonetheless the concept of firing rate, while informally understood, can be mathematically defined in several distinct ways. These definitions may yield mutually incompatible results unless implemented properly. Recently it has been shown that the notions of the instantaneous and the classical firing rates can be made compatible, at least in terms of their averages, by carefully discerning the time instant at which the neuronal activity is observed. In this paper we revisit the properties of instantaneous interspike intervals in order to derive several novel firing rate estimators, which are free of additional assumptions or parameters and their temporal resolution is 'locally self-adaptive'. The estimators are simple to implement and are numerically efficient even for very large sets of data.

Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees

Geosmin is an odorant produced by bacteria in moist soil. It has been found to be extraordinarily relevant to some insects, but the reasons for this are not yet fully understood. Here we report the first tests of the effect of geosmin on honey bees. A stinging assay showed that the defensive behaviour elicited by the bee's alarm pheromone component isoamyl acetate (IAA) is strongly suppressed by geosmin. Surprisingly, the suppression is, however, only present at very low geosmin concentrations, and disappears at higher concentrations. We investigated the underlying mechanisms at the level of the olfactory receptor neurons by means of electroantennography, finding the responses to mixtures of geosmin and IAA to be lower than to pure IAA, suggesting an interaction of both compounds at the olfactory receptor level. Calcium imaging of the antennal lobe (AL) revealed that neuronal responses to geosmin decreased with increasing concentration, correlating well with the observed behaviour. Computational modelling of odour transduction and coding in the AL suggests that a broader activation of olfactory receptor types by geosmin in combination with lateral inhibition could lead to the observed non-monotonic increasing-decreasing responses to geosmin and thus underlie the specificity of the behavioural response to low geosmin concentrations.

Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons

The olfactory system in insects has evolved to process the dynamic changes in the concentration of food odors or sex pheromones to localize the nutrients or conspecific mating partners. Experimental studies have suggested that projection neurons (PNs) in insects encode not only the stimulus intensity but also its rate-of-change (input gradient). In this study, we aim to develop a simple computational model for a PN to understand the mechanism underlying the coding of the rate-of-change information. We show that the spike frequency adaptation is a potential key mechanism for reproducing the phasic response pattern of the PN in Drosophila. We also demonstrate that this adaptation mechanism enables the PN to encode the rate-of-change of the input firing rate. Finally, our model predicts that the PN exhibits the intensity-invariant response for the pulse and ramp odor stimulus. These results suggest that the developed model is useful for investigating the coding principle underlying olfactory information processing in insects.

Principles of odor coding in vertebrates and artificial chemosensory systems

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall I-O relationships. Up to this point, our account of the systems goes along similar lines. The next processing steps differ considerably: while in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers were little studied. Only recently there has been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little connected fields.

A Comparison of the Primary Sensory Neurons Used in Olfaction and Vision

Vision, hearing, smell, taste, and touch are the tools used to perceive and navigate the world. They enable us to obtain essential resources such as food and highly desired resources such as mates. Thanks to the investments in biomedical research the molecular unpinning’s of human sensation are rivaled only by our knowledge of sensation in the laboratory mouse. Humans rely heavily on vision whereas mice use smell as their dominant sense. Both modalities have many features in common, starting with signal detection by highly specialized primary sensory neurons—rod and cone photoreceptors (PR) for vision, and olfactory sensory neurons (OSN) for the smell. In this chapter, we provide an overview of how these two types of primary sensory neurons operate while highlighting the similarities and distinctions.

Review: Methods of firing rate estimation

Neuronal firing rate is traditionally defined as the number of spikes per time window. The concept is essential for the rate coding hypothesis, which is still the most commonly investigated scenario in neuronal activity analysis. The estimation of dynamically changing firing rate from neural data can be challenging due to the variability of spike times, even under identical external conditions; hence a wide range of statistical measures have been employed to solve this particular problem. In this paper, we review established firing rate estimation methods, briefly summarize the technical aspects of each approach and discuss their practical applications.

Statistics of inverse interspike intervals: The instantaneous firing rate revisited

The rate coding hypothesis is the oldest and still one of the most accepted and investigated scenarios in neuronal activity analyses. However, the actual neuronal firing rate, while informally understood, can be mathematically defined in several different ways. These definitions yield distinct results; even their average values may differ dramatically for the simplest neuronal models. Such an inconsistency, together with the importance of "firing rate," motivates us to revisit the classical concept of the instantaneous firing rate. We confirm that different notions of firing rate can in fact be compatible, at least in terms of their averages, by carefully discerning the time instant at which the neuronal activity is observed. Two general cases are distinguished: either the inspection time is synchronised with a reference time or with the neuronal spiking. The statistical properties of the instantaneous firing rate, including parameter estimation, are analyzed, and compatibility with the intuitively understood concept is demonstrated.

Distinct temporal filters in mitral cells and external tufted cells of the olfactory bulb

KEY POINTS

The release probability of the odorant receptor neuron (ORN) is reportedly one of the highest in the brain and is predicted to impose a transient temporal filter on postsynaptic cells. Mitral cells responded to high frequency ORN stimulation with sustained transmission, whereas external tufted cells responded transiently. The release probability of ORNs (0.7) was equivalent across mitral and external tufted cells and could be explained by a single pool of slowly recycling vesicles. The sustained response in mitral cells resulted from dendrodendritic amplification in mitral cells, which was blocked by NMDA and mGluR1 receptor antagonists, converting mitral cell responses to transient response profiles. Our results suggest that although the afferent ORN synapse shows strong synaptic depression, dendrodendritic circuitry in mitral cells produces robust amplification of brief afferent input, and thus the relative strength of axodendritic and dendrodendritic input determines the postsynaptic response profile.

ABSTRACT

Short-term synaptic plasticity is a critical regulator of neural circuits, and largely determines how information is temporally processed. In the olfactory bulb, afferent olfactory receptor neurons respond to increasing concentrations of odorants with barrages of action potentials, and their terminals have an extraordinarily high release probability. These features suggest that during naturalistic stimuli, afferent input to the olfactory bulb is subject to strong synaptic depression, presumably truncating the postsynaptic response to afferent stimuli. To examine this issue, we used single glomerular stimulation in mouse olfactory bulb slices to measure the synaptic dynamics of afferent-evoked input at physiological stimulus frequencies. In cell-attached recordings, mitral cells responded to high frequency stimulation with sustained responses, whereas external tufted cells responded transiently. Consistent with previous reports, olfactory nerve terminals onto both cell types had a high release probability (0.7), from a single pool of slowly recycling vesicles, indicating that the distinct responses of mitral and external tufted cells to high frequency stimulation did not originate presyaptically. Rather, distinct temporal response profiles in mitral cells and external tufted cells could be attributed to slow dendrodendritic responses in mitral cells, as blocking this slow current in mitral cells converted mitral cell responses to a transient response profile, typical of external tufted cells. Our results suggest that despite strong axodendritic synaptic depression, the balance of axodendritic and dendrodendritic circuitry in external tufted cells and mitral cells, respectively, tunes the postsynaptic responses to high frequency, naturalistic stimulation.

Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures

Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. We show how antagonism leads to an effective ‘normalization’ of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, leads to experimentally testable predictions and explains several psychophysical experiments in humans.

When ordering in a coffee shop, you probably recognize and enjoy the aroma of freshly roasted coffee beans. But as well as coffee, you can also smell the croissants behind the counter and maybe even the perfume or cologne of the person next to you. Each of these scents consists of a collection of chemicals, or odorants. To distinguish between the aroma of coffee and that of croissants, your brain must group the odorants appropriately and then keep the groups separate from each other.

This is not a trivial task. Odorants bind to proteins called odorant receptors found on the surface of cells in the nose called olfactory receptor neurons. But each odorant does not have its own dedicated receptor. Instead, a single odorant will bind to multiple types of odorant receptors, and thus, each olfactory receptor neuron may respond to multiple odorants. So how does the brain encode mixtures of odorants in a way that allows us to distinguish one aroma from another?

Reddy, Zak et al. have developed a computational model to explain how this process works. The model assumes that an odorant triggers a response in an olfactory receptor neuron via two steps. First, the odorant binds to an odorant receptor. Second, the bound odorant activates the receptor. But the odorant that binds most strongly to a receptor will not necessarily be the odorant that is best at activating that receptor.

This allows a phenomenon called competitive antagonism to occur. This is when one odorant in a mixture binds more strongly to a receptor than the other odorants, but only weakly activates that receptor. In so doing, the strongly bound odorant prevents the other odorants from binding to and activating the receptor. This helps tame the dominating influence of background odors, which might otherwise saturate the responses of individual olfactory receptor neurons.

Reddy, Zak et al. show that processes such as competitive antagonism enable olfactory receptor neurons to encode all of the odors within a mixture. The model can explain various phenomena observed in experiments and it adds to our understanding of how the brain generates our sense of smell. The model may also be relevant to other biological systems that must filter weak signals from a dominant background. These include the immune system, which must distinguish a small set of foreign proteins from the much larger number of proteins that make up our bodies.

Efficiency of rate and latency coding with respect to metabolic cost and time

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.

Accuracy of rate coding: When shorter time window and higher spontaneous activity help

It is widely accepted that neuronal firing rates contain a significant amount of information about the stimulus intensity. Nevertheless, theoretical studies on the coding accuracy inferred from the exact spike counting distributions are rare. We present an analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, for which we calculate the corresponding Fisher information to investigate the accuracy of rate coding. We analyze the effect of changing the duration of the time window and the influence of several parameters of the model, in particular the level of the presynaptic spontaneous activity and the level of random fluctuation of the membrane potential, which can be interpreted as noise of the system. The results show that the Fisher information is nonmonotonic with respect to the length of the observation period. This counterintuitive result is caused by the discrete nature of the count of spikes. We observe also that the signal can be enhanced by noise, since the Fisher information is nonmonotonic with respect to the level of spontaneous activity and, in some cases, also with respect to the level of fluctuation of the membrane potential.

The Ca2+-activated Cl- channel TMEM16B regulates action potential firing and axonal targeting in olfactory sensory neurons

TMEM16B is expressed in olfactory sensory neurons, but previous attempts to establish a physiological role in olfaction have been unsuccessful. Pietra et al. find that genetic ablation of TMEM16B results in defects in the olfactory behavior of mice and the cellular physiology of olfactory sensory neurons.

The Ca²⁺-activated Cl⁻ channel TMEM16B is highly expressed in the cilia of olfactory sensory neurons (OSNs). Although a large portion of the odor-evoked transduction current is carried by Ca²⁺-activated Cl⁻ channels, their role in olfaction is still controversial. A previous report (Billig et al. 2011. Nat. Neurosci.http://dx.doi.org/10.1038/nn.2821) showed that disruption of the TMEM16b/Ano2 gene in mice abolished Ca²⁺-activated Cl⁻ currents in OSNs but did not produce any major change in olfactory behavior. Here we readdress the role of TMEM16B in olfaction and show that TMEM16B knockout (KO) mice have behavioral deficits in odor-guided food-finding ability. Moreover, as the role of TMEM16B in action potential (AP) firing has not yet been studied, we use electrophysiological recording methods to measure the firing activity of OSNs. Suction electrode recordings from isolated olfactory neurons and on-cell loose-patch recordings from dendritic knobs of neurons in the olfactory epithelium show that randomly selected neurons from TMEM16B KO mice respond to stimulation with increased firing activity than those from wild-type (WT) mice. Because OSNs express different odorant receptors (ORs), we restrict variability by using a mouse line that expresses a GFP-tagged I7 OR, which is known to be activated by heptanal. In response to heptanal, we measure dramatic changes in the firing pattern of I7-expressing neurons from TMEM16B KO mice compared with WT: responses are prolonged and display a higher number of APs. Moreover, lack of TMEM16B causes a markedly reduced basal spiking activity in I7-expressing neurons, together with an alteration of axonal targeting to the olfactory bulb, leading to the appearance of supernumerary I7 glomeruli. Thus, TMEM16B controls AP firing and ensures correct glomerular targeting of OSNs expressing I7. Altogether, these results show that TMEM16B does have a relevant role in normal olfaction.

Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding

The time to the first spike after stimulus onset typically varies with the stimulation intensity. Experimental evidence suggests that neural systems use such response latency to encode information about the stimulus. We investigate the decoding accuracy of the latency code in relation to the level of noise in the form of presynaptic spontaneous activity. Paradoxically, the optimal performance is achieved at a nonzero level of noise and suprathreshold stimulus intensities. We argue that this phenomenon results from the influence of the spontaneous activity on the stabilization of the membrane potential in the absence of stimulation. The reported decoding accuracy improvement represents a novel manifestation of the noise-aided signal enhancement.

Effect of spontaneous activity on stimulus detection in a simple neuronal model

It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.

Dynamic range in the C. elegans brain network

We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.

A review of the methods for neuronal response latency estimation

Neuronal response latency is usually vaguely defined as the delay between the stimulus onset and the beginning of the response. It contains important information for the understanding of the temporal code. For this reason, the detection of the response latency has been extensively studied in the last twenty years, yielding different estimation methods. They can be divided into two classes, one of them including methods based on detecting an intensity change in the firing rate profile after the stimulus onset and the other containing methods based on detection of spikes evoked by the stimulation using interspike intervals and spike times. The aim of this paper is to present a review of the main techniques proposed in both classes, highlighting their advantages and shortcomings.

Circuit formation and function in the olfactory bulb of mice with reduced spontaneous afferent activity

The type of neuronal activity required for circuit development is a matter of significant debate. We addressed this issue by analyzing the topographic organization of the olfactory bulb in transgenic mice engineered to have very little afferent spontaneous activity due to the overexpression of the inwardly rectifying potassium channel Kir2.1 in the olfactory sensory neurons (Kir2.1 mice). In these conditions, the topography of the olfactory bulb was unrefined. Odor-evoked responses were readily recorded in glomeruli with reduced spontaneous afferent activity, although the functional maps were coarser than in controls and contributed to altered olfactory discrimination behavior. In addition, overexpression of Kir2.1 in adults induced a regression of the already refined connectivity to an immature (i.e., coarser) status. Our data suggest that spontaneous activity plays a critical role not only in the development but also in the maintenance of the topography of the olfactory bulb and in sensory information processing.

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.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

Promising Aedes aegypti repellent chemotypes identified through integrated QSAR, virtual screening, synthesis, and bioassay

Molecular field topology analysis, scaffold hopping, and molecular docking were used as complementary computational tools for the design of repellents for Aedes aegypti, the insect vector for yellow fever, chikungunya, and dengue fever. A large number of analogues were evaluated by virtual screening with Glide molecular docking software. This produced several dozen hits that were either synthesized or procured from commercial sources. Analysis of these compounds by a repellent bioassay resulted in a few highly active chemicals (in terms of minimum effective dosage) as viable candidates for further hit-to-lead and lead optimization effort.

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.

Population coding is essential for rapid information processing in the moth antennal lobe

We investigated how odorant information is transmitted by neurons in the moth antennal lobe (AL). The neurons were repeatedly stimulated by three different odorants and their activity was intracellularly recorded. First, the response properties of single neurons were analyzed. The neurons exhibited highly reliable responses to the odorants and 43% of AL neurons responded to two or three odorants. The population distribution of firing rates in response to odorant stimulation was relatively broad in moth AL neurons, which is consistent across insects. Second, we attempted to decode the odorant identity from the activity of the recorded neurons using the maximum likelihood method. The decoding performance rapidly improves with increasing the number of neurons. Notably, an increase in the size of neural population results in faster transfer of information and increased the duration to retain odorant information. In conclusion, the AL neurons encode odorant information reliably and the population coding can transmit odorant information to olfactory centers. Population coding allows AL to encode and transmit olfactory information faster than the discrimination latency demonstrated in behavioral experiments. This article is part of a Special Issue entitled Neural Coding 2012.

Spontaneous and sensory-evoked activity in mouse olfactory sensory neurons with defined odorant receptors

Sensory systems need to tease out stimulation-evoked activity against a noisy background. In the olfactory system, the odor response profile of an olfactory sensory neuron (OSN) is dependent on the type of odorant receptor it expresses. OSNs also exhibit spontaneous activity, which plays a role in establishing proper synaptic connections and may also increase the sensitivity of the cells. However, where the spontaneous activity originates and whether it informs sensory-evoked activity remain unclear. We addressed these questions by examining patch-clamp recordings of genetically labeled mouse OSNs with defined odorant receptors in intact olfactory epithelia. We show that OSNs expressing different odorant receptors had significantly different rates of basal activity. Additionally, OSNs expressing an inactive mutant I7 receptor completely lacked spontaneous activity, despite being able to fire action potentials in response to current injection. This finding strongly suggests that the spontaneous firing of an OSN originates from the spontaneous activation of its G protein-coupled odorant receptor. Moreover, OSNs expressing the same receptor displayed considerable variation in their spontaneous activity, and the variation was broadened upon odor stimulation. Interestingly, there is no significant correlation between the spontaneous and sensory-evoked activity in these neurons. This study reveals that the odorant receptor type determines the spontaneous firing rate of OSNs, but the basal activity does not correlate with the activity induced by near-saturated odor stimulation. The implications of these findings on olfactory information processing are discussed.

Interactions of odorants with olfactory receptors and other preprocessing mechanisms: how complex and difficult to predict?

In this issue of Chemical Senses, Münch et al. present a thorough analysis of how mixtures of odorants interact with olfactory receptors (ORs) borne by olfactory receptor neurons (ORNs). Using fruit fly ORNs expressing the receptor OR22a, they provide a clear example of mixture interaction and confirm that the response of an ORN to a binary mixture can be sometimes predicted quantitatively knowing the ORN responses to its components as shown previously in rat ORNs. The prediction is based on a nonlinear model that assumes a classical 2-step activation of the OR and competition of the 2 odorants in the mixture for the same binding site. Can this success be generalized to all odorant-receptor pairs? This would be an encouraging perspective, especially for the fragrance and flavor industries, as it would permit the prediction of all mixtures. To address this question, I outline its conceptual framework and discuss the variety of mixture interactions found so far. In accordance with the effects described in the study of other receptors, several kinds of mixture interactions have been found that are not easily predictable. The relative importance of the predictable and less predictable effects thus appears as a major issue for future developments.

Identification of noisy response latency

In many physical systems there is a time delay before an applied input (stimulation) has an impact on the output (response), and the quantification of this delay is of paramount interest. If the response can only be observed on top of an indistinguishable background signal, the estimation can be highly unreliable, unless the background signal is accounted for in the analysis. In fact, if the background signal is ignored, however small it is compared to the response and however large the delay is, the estimate of the time delay will go to zero for any reasonable estimator when increasing the number of observations. Here we propose a unified concept of response latency identification in event data corrupted by a background signal. It is done in the context of information transfer within a neural system, more specifically on spike trains from single neurons. The estimators are compared on simulated data and the most suitable for specific situations are recommended.

SELECTIVITY IMPROVEMENT IN A MODEL OF OLFACTORY RECEPTOR NEURON WITH ADSORPTION-DESORPTION NOISE

In biological olfactory systems, interaction of odorant molecules with olfactory receptor proteins is driven by Brownian motion. As a result, at chemical equilibrium, the total number of bound receptors changes randomly in time. Here we investigate the role of this effect, known in physics as adsorption-desorption noise, in the discriminating ability of olfactory receptor neurons. For this purpose we developed a computer program, which generates the adsorption-desorption process in a model neuron. We compared the processes resulting from two different odorants with different affinities for the receptor proteins. We took into account the threshold at which spikes are triggered and we calculated the neuronal selectivity due to the differences in the threshold-crossing statistics for the processes resulting from both odorants. We conclude that selectivity of the spiking response of the whole neuron is much greater than that of its receptor proteins in the near-threshold range of odorant concentrations.

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 styryl dye FM1-43 suppresses odorant responses in a subset of olfactory neurons by blocking cyclic nucleotide-gated (CNG) channels

Many olfactory receptor neurons use a cAMP-dependent transduction mechanism to transduce odorants into depolarizations. This signaling cascade is characterized by a sequence of two currents: a cation current through cyclic nucleotide-gated channels followed by a chloride current through calcium-activated chloride channels. To date, it is not possible to interfere with these generator channels under physiological conditions with potent and specific blockers. In this study we identified the styryl dye FM1-43 as a potent blocker of native olfactory cyclic nucleotide-gated channels. Furthermore, we characterized this substance to stain olfactory receptor neurons that are endowed with cAMP-dependent transduction. This allows optical differentiation and pharmacological interference with olfactory receptor neurons at the level of the signal transduction.

Olfactory receptor neuron responses coding for rapid odour sampling

Vertebrate olfactory receptor neurons (ORNs) are stimulated in a rhythmic manner in vivo, driven by delivery of odorants to the nasal cavity carried by the inhaled air, making olfaction a sense where animals can control the frequency of stimulus delivery. How ORNs encode repeated stimulation at resting, low breathing frequencies and at increased sniffing frequencies is not known, nor is it known if the olfactory transduction cascade is accurate and fast enough to follow high frequency stimulation. We investigated mouse olfactory responses to stimulus frequencies mimicking odorant exposure during low (2Hz) and high (5Hz) frequency sniffing. ORNs reliably follow low frequency stimulations with high fidelity by generating bursts of action potentials at each stimulation at intermediate odorant concentrations, but fail to do so at high odorant concentrations. Higher stimulus frequencies across all odorant concentrations reduced the likelihood of action potential generation, increased the latency of response, and decreased there liability of encoding the onset of stimulation. Thus an increase in stimulus frequency degrades and at high odorant concentrations entirely prevents action potential generation in individual ORNs, causing reduced signalling to the olfactory bulb. These results demonstrate that ORNs do not simply relay timing and concentration of an odorous stimulus, but also process and modulate the stimulus in a frequency-dependent manner which is controlled by the chosen sniffing rate.

System identification of Drosophila olfactory sensory neurons

The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration. Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

First-Spike Latency in the Presence of Spontaneous Activity

A new statistical method for the estimation of the response latency is proposed. When spontaneous discharge is present, the first spike after the stimulus application may be caused by either the stimulus itself, or it may appear due to the prevailing spontaneous activity. Therefore, an appropriate method to deduce the response latency from the time to the first spike after the stimulus is needed. We develop a nonparametric estimator of the response latency based on repeated stimulations. A simulation study is provided to show how the estimator behaves with an increasing number of observations and for different rates of spontaneous and evoked spikes. Our nonparametric approach requires very few assumptions. For comparison, we also consider a parametric model. The proposed probabilistic model can be used for both single and parallel neuronal spike trains. In the case of simultaneously recorded spike trains in several neurons, the estimators of joint distribution and correlations of response latencies are also introduced. Real data from inferior colliculus auditory neurons obtained from a multielectrode probe are studied to demonstrate the statistical estimators of response latencies and their correlations in space.

Olfactory response termination involves Ca2+-ATPase in vertebrate olfactory receptor neuron cilia

In vertebrate olfactory receptor neurons (ORNs), odorant-induced activation of the transduction cascade culminates in production of cyclic AMP, which opens cyclic nucleotide–gated channels in the ciliary membrane enabling Ca²⁺ influx. The ensuing elevation of the intraciliary Ca²⁺ concentration opens Ca²⁺-activated Cl⁻ channels, which mediate an excitatory Cl⁻ efflux from the cilia. In order for the response to terminate, the Cl⁻ channel must close, which requires that the intraciliary Ca²⁺ concentration return to basal levels. Hitherto, the extrusion of Ca²⁺ from the cilia has been thought to depend principally on a Na⁺–Ca²⁺ exchanger.

In this study, we show using simultaneous suction pipette recording and Ca²⁺-sensitive dye fluorescence measurements that in fire salamander ORNs, withdrawal of external Na⁺ from the solution bathing the cilia, which incapacitates Na⁺–Ca²⁺exchange, has only a modest effect on the recovery of the electrical response and the accompanying decay of intraciliary Ca²⁺ concentration. In contrast, exposure of the cilia to vanadate or carboxyeosin, a manipulation designed to block Ca²⁺-ATPase, has a substantial effect on response recovery kinetics. Therefore, we conclude that Ca²⁺-ATPase contributes to Ca²⁺ extrusion in ORNs, and that Na⁺–Ca²⁺exchange makes only a modest contribution to Ca²⁺ homeostasis in this species.

Odor information processing by the olfactory bulb analyzed in gene-targeted mice

In mammals, olfactory sensory neurons (OSNs) expressing a specific odorant receptor (OR) gene project with precise stereotypy onto mitral/tufted (M/T) cells in the main olfactory bulb (MOB). It remains challenging to understand how incoming olfactory signals are transformed into outputs of M/T cells. By recording from OSNs expressing mouse I7 receptor and their postsynaptic neurons in the bulb, we found that I7 OSNs and their corresponding M/T cells exhibit similarly selective tuning profiles at low concentrations. Increasing the concentration significantly reduces response selectivity for both OSNs and M/T cells, although the tuning curve of M/T cells remains comparatively narrow. By contrast, interneurons in the MOB are broadly tuned, and blocking GABAergic neurotransmission reduces selectivity of M/T cells at high odorant concentrations. Our results indicate that olfactory information carried by an OR is channeled to its corresponding M/T cells and support the role of lateral inhibition via interneurons in sharpening the tuning of M/T cells.

Modelling and sensitivity analysis of the reactions involving receptor, G-protein and effector in vertebrate olfactory receptor neurons

A biochemical model of the receptor, G-protein and effector (RGE) interactions during transduction in the cilia of vertebrate olfactory receptor neurons (ORNs) was developed and calibrated to experimental recordings of cAMP levels and the receptor current (RC). The model describes the steps from odorant binding to activation of the effector enzyme which catalyzes the conversion of ATP to cAMP, and shows how odorant stimulation is amplified and delayed by the RGE transduction cascade. A time-dependent sensitivity analysis was performed on the model. The model output-the cAMP production rate-is particularly sensitive to a few, dominant parameters. During odorant stimulation it depends mainly on the initial density of G-proteins and the catalytic constant for cAMP production.

Modeling the response of a population of olfactory receptor neurons to an odorant

We modeled the firing rate of populations of olfactory receptor neurons (ORNs) responding to an odorant at different concentrations. Two cases were considered: a population of ORNs that all express the same olfactory receptor (OR), and a population that expresses many different ORs. To take into account ORN variability, we replaced single parameter values in a biophysical ORN model with values drawn from statistical distributions, chosen to correspond to experimental data. For ORNs expressing the same OR, we found that the distributions of firing frequencies are Gaussian at all concentrations, with larger mean and standard deviation at higher concentrations. For a population expressing different ORs, the distribution of firing frequencies can be described as the superposition of a Gaussian distribution and a lognormal distribution. Distributions of maximum value and dynamic range of spiking frequencies in the simulated ORN population were similar to experimental results.

Computational model of the insect pheromone transduction cascade

A biophysical model of receptor potential generation in the male moth olfactory receptor neuron is presented. It takes into account all pre-effector processes—the translocation of pheromone molecules from air to sensillum lymph, their deactivation and interaction with the receptors, and the G-protein and effector enzyme activation—and focuses on the main post-effector processes. These processes involve the production and degradation of second messengers (IP₃ and DAG), the opening and closing of a series of ionic channels (IP₃-gated Ca²⁺ channel, DAG-gated cationic channel, Ca²⁺-gated Cl⁻ channel, and Ca²⁺- and voltage-gated K⁺ channel), and Ca²⁺ extrusion mechanisms. The whole network is regulated by modulators (protein kinase C and Ca²⁺-calmodulin) that exert feedback inhibition on the effector and channels. The evolution in time of these linked chemical species and currents and the resulting membrane potentials in response to single pulse stimulation of various intensities were simulated. The unknown parameter values were fitted by comparison to the amplitude and temporal characteristics (rising and falling times) of the experimentally measured receptor potential at various pheromone doses. The model obtained captures the main features of the dose–response curves: the wide dynamic range of six decades with the same amplitudes as the experimental data, the short rising time, and the long falling time. It also reproduces the second messenger kinetics. It suggests that the two main types of depolarizing ionic channels play different roles at low and high pheromone concentrations; the DAG-gated cationic channel plays the major role for depolarization at low concentrations, and the Ca²⁺-gated Cl⁻ channel plays the major role for depolarization at middle and high concentrations. Several testable predictions are proposed, and future developments are discussed.

All sensory neurons transduce their natural stimulus, whether a molecule, a photon, or a mechanical force, in an electrical current flowing through their sensory membrane via similar molecular and ionic mechanisms. Olfactory receptor neurons (ORNs), whose stimuli are volatile molecules, are no exception, including one of the best known: the exquisitely sensitive ORNs of male moths that detect the sexual pheromone released by conspecific females. We provide a detailed computational model of the intracellular molecular mechanisms at work in this ORN type. We describe qualitatively and quantitatively how the initial event, the interaction of pheromone molecules with specialized receptors at the ORN surface, is amplified through a sequence of linked biochemical and electrical events into a whole cell response, the receptor potential. We detail the respective roles of the upward activating reactions involving a cascade of ionic channels permeable to cations, chloride and potassium, their control by feedback inactivating mechanisms, and the central regulatory role of calcium. This computational model contributes to an integrated understanding of this signalling pathway, provides testable hypotheses, and suggests new experimental approaches.

Modulation of spontaneous and odorant-evoked activity of rat olfactory sensory neurons by two anorectic peptides, insulin and leptin

In mammals, the sense of smell is modulated by the status of satiety, which is mainly signaled by blood-circulating peptide hormones. However, the underlying mechanisms linking olfaction and food intake are poorly understood. Here we investigated the effects of two anorectic peptides, insulin and leptin, on the functional properties of olfactory sensory neurons (OSNs). Using patch-clamp recordings, we analyzed the spontaneous activity of rat OSNs in an in vitro intact epithelium preparation. Bath perfusion of insulin and leptin significantly increased the spontaneous firing frequency in 91.7% (n = 24) and 75.0% (n = 24) of the cells, respectively. When the activity was electrically evoked, both peptides shortened the latency to the first action potential by approximately 25% and decreased the interspike intervals by approximately 13%. While insulin and leptin enhanced the electrical excitability of OSNs in the absence of odorants, they surprisingly reduced the odorant-induced activity in the olfactory epithelium. Insulin and leptin decreased the peak amplitudes of isoamyl acetate-induced electroolfactogram (EOG) signals to 46 and 38%, respectively. When measured in individual cells by patch-clamp recordings, insulin and leptin decreased odorant-induced transduction currents and receptor potentials. Therefore by increasing the spontaneous activity but reducing the odorant-induced activity of OSNs, an elevated insulin and leptin level (such as after a meal) may result in a decreased global signal-to-noise ratio in the olfactory epithelium, which matches the smell ability to the satiety status.

Odor concentration invariance by chemical ratio coding

Many animal species rely on chemical signals to extract ecologically important information from the environment. Yet in natural conditions chemical signals will frequently undergo concentration changes that produce differences in both level and pattern of activation of olfactory receptor neurons. Thus, a central problem in olfactory processing is how the system is able to recognize the same stimulus across different concentrations. To signal species identity for mate recognition, some insects use the ratio of two components in a binary chemical mixture to produce a code that is invariant to dilution. Here, using psychophysical methods, we show that rats also classify binary odor mixtures according to the molar ratios of their components, spontaneously generalizing over at least a tenfold concentration range. These results indicate that extracting chemical ratio information is not restricted to pheromone signaling and suggest a general solution for concentration-invariant odor recognition by the mammalian olfactory system.

Competitive and noncompetitive odorant interactions in the early neural coding of odorant mixtures

Most olfactory receptor neurons (ORNs) express a single type of olfactory receptor that is differentially sensitive to a wide variety of odorant molecules. The diversity of possible odorant-receptor interactions raises challenging problems for the coding of complex mixtures of many odorants, which make up the vast majority of real world odors. Pure competition, the simplest kind of interaction, arises when two or more agonists can bind to the main receptor site, which triggers receptor activation, although only one can be bound at a time. Noncompetitive effects may result from various mechanisms, including agonist binding to another site, which modifies the receptor properties at the main binding site. Here, we investigated the electrophysiological responses of rat ORNs in vivo to odorant agonists and their binary mixtures and interpreted them in the framework of a quantitative model of competitive interaction between odorants. We found that this model accounts for all concentration-response curves obtained with single odorants and for about half of those obtained with binary mixtures. In the other half, the shifts of curves along the concentration axis and the changes of maximal responses with respect to model predictions, indicate that noncompetitive interactions occur and can modulate olfactory receptors. We conclude that, because of their high frequency, the noncompetitive interactions play a major role in the neural coding of natural odorant mixtures. This finding implies that the CNS activity caused by mixtures will not be easily analyzed into components, and that mixture responses will be difficult to generalize across concentration.

Dynamic range of hypercubic stochastic excitable media

We study the response properties of d-dimensional hypercubic excitable networks to a stochastic stimulus. Each site, modeled either by a three-state stochastic susceptible-infected-recovered-susceptible system or by the probabilistic Greenberg-Hastings cellular automaton, is continuously and independently stimulated by an external Poisson rate h. The response function (mean density of active sites rho versus h) is obtained via simulations (for d=1,2,3,4) and mean-field approximations at the single-site and pair levels (for all d). In any dimension, the dynamic range and sensitivity of the response function are maximized precisely at the nonequilibrium phase transition to self-sustained activity, in agreement with a reasoning recently proposed. Moreover, the maximum dynamic range attained at a given dimension d is a decreasing function of d.

Information processing in the olfactory systems of insects and vertebrates

Insects and vertebrates separately evolved remarkably similar mechanisms to process olfactory information. Odors are sampled by huge numbers of receptor neurons, which converge type-wise upon a much smaller number of principal neurons within glomeruli. There, odor information is transformed by inhibitory interneuron-mediated, cross-glomerular circuit interactions that impose slow temporal structures and fast oscillations onto the firing patterns of principal neurons. The transformations appear to improve signal-to-noise characteristics, define odor categories, achieve precise odor identification, extract invariant features, and begin the process of sparsening the neural representations of odors for efficient discrimination, memorization, and recognition.

Modelling the early steps of transduction in insect olfactory receptor neurons

Olfactory transduction is a multistep process whose basic function is to convert a low energy reaction, the odorant-receptor interaction that may involve a single odorant molecule, into a whole cell electrical response, the receptor potential, which triggers the firing of one or more action potentials. Although much effort has been devoted to the experimental analysis of transduction in olfactory receptor neurons (ORNS), especially in the favorable moth sex-pheromone receptor neuron, its modelling is less advanced. The model we investigated, which takes into account the translocation of pheromone molecules from air to the extracellular space, their deactivation and their interaction with receptors, focuses on the membrane cascade. It involves the interaction of receptors, G-proteins and effector enzymes, whose reaction rates are limited by lateral diffusion in the membrane. The evolutions in time of these species in response to single pulse stimulation of various intensities were compared to one another and to the experimentally measured electrical response. The results obtained suggest that the receptor-to-effector conversion is fast with respect to the receptor response, that it presents a small amplification factor, contrary to the photoreceptor, and that most of the amplification is achieved in the post-effector processes involving the second messenger and ionic channels.

Optimal signal in sensory neurons under an extended rate coding concept

We define an optimal signal in parametric neuronal models on the basis of interspike interval data and rate coding schema. Under the classical approach the optimal signal is located where the frequency transfer function is steepest. Its position coincides with the inflection point of this curve. This concept is extended here by using Fisher information which is the inverse asymptotic variance of the best estimator and its dependence on the parameter value indicates accuracy of estimation. We compare the signal producing maximal Fisher information with the inflection point of the sigmoidal frequency transfer function.

EARLY EVENTS IN OLFACTORY PROCESSING

Olfactory space has a higher dimensionality than does any other class of sensory stimuli, and the olfactory system receives input from an unusually large number of unique information channels. This suggests that aspects of olfactory processing may differ fundamentally from processing in other sensory modalities. This review summarizes current understanding of early events in olfactory processing. We focus on how odors are encoded by the activity of primary olfactory receptor neurons, how odor codes may be transformed in the olfactory bulb, and what relevance these codes may have for downstream neurons in higher brain centers. Recent findings in synaptic physiology, neural coding, and psychophysics are discussed, with reference to both vertebrate and insect model systems.

Response of electrically coupled spiking neurons: a cellular automaton approach

Experimental data suggest that some classes of spiking neurons in the first layers of sensory systems are electrically coupled via gap junctions or ephaptic interactions. When the electrical coupling is removed, the response function (firing rate vs. stimulus intensity) of the uncoupled neurons typically shows a decrease in dynamic range and sensitivity. In order to assess the effect of electrical coupling in the sensory periphery, we calculate the response to a Poisson stimulus of a chain of excitable neurons modeled by n-state Greenberg-Hastings cellular automata in two approximation levels. The single-site mean field approximation is shown to give poor results, failing to predict the absorbing state of the lattice, while the results for the pair approximation are in good agreement with computer simulations in the whole stimulus range. In particular, the dynamic range is substantially enlarged due to the propagation of excitable waves, which suggests a functional role for lateral electrical coupling. For probabilistic spike propagation the Hill exponent of the response function is alpha=1, while for deterministic spike propagation we obtain alpha=1/2, which is close to the experimental values of the psychophysical Stevens exponents for odor and light intensities. Our calculations are in qualitative agreement with experimental response functions of ganglion cells in the mammalian retina.

Odor and chemesthesis from brief exposures to TXIB

An experiment explored ability of subjects to detect vapors of the plasticizer TXIB (2,2,4-trimethyl-1,3-pentanediol diisobutyrate) and ethanol via olfaction and via ocular and nasal chemesthesis, i.e. chemically stimulated feel. Testing, tailored to the sensitivity of each subject, produced psychometric functions for individuals. Olfactory detection of TXIB began at concentrations below 1 ppb (v/v), with 50% correct detection at 1.2 ppb. (Comparable detection for ethanol occurred almost two orders of magnitude higher.) Chemesthetic detection of TXIB began at about 500 ppb, with 50% correct detection at 2.1 ppm for the eye and 4.6 ppm for the nose, both close to saturated vapor concentration. (Comparable detection for ethanol occurred essentially three orders of magnitude higher.) Suggestions that TXIB plays a role in generation of irritative symptoms at concentrations in the range of parts-per-billion need to reckon with a conservatively estimated 200-fold gap between the levels putatively 'responsible' for the symptoms and those even minimally detectable via chemesthesis. Neither the variable of exposure duration nor that of mixing offers a likely explanation. Inclusion of ethanol in the study allowed comparisons pertinent to issues of variability in human chemoreception. An interpretation of the psychometric functions for individuals across materials and perceptual continua led to the conclusion that use of concentration as the metric of detection in olfaction inflates individual differences.

PRACTICAL IMPLICATIONS

This study indicated that the plasticizer TXIB could contribute odor at concentrations in the range of parts-per-billion, but could hardly contribute sensory irritation per se, as alleged in reports of some field studies where TXIB has existed amongst many other organic compounds.

Optimal Signal Estimation in Neuronal Models

We study optimal estimation of a signal in parametric neuronal models on the basis of interspike interval data. Fisher information is the inverse asymptotic variance of the best estimator. Its dependence on the parameter value indicates accuracy of estimation. Our models assume that the input signal is estimated from neuronal output interspike interval data where the frequency transfer function is sigmoidal. If the coefficient of variation of the interspike interval is constant with respect to the signal, the Fisher information is unimodal, and its maximum for the most estimable signal can be found. We obtain a general result and compare the signal producing maximal Fisher information with the inflection point of the sigmoidal transfer function in several basic neuronal models.