Patterns of spontaneous activity in single rat olfactory receptor neurons are different in normally breathing and tracheotomized animals

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

Respiratory influence on brain dynamics: the preponderant role of the nasal pathway and deep slow regime

As a possible body signal influencing brain dynamics, respiration is fundamental for perception, cognition, and emotion. The olfactory system has recently acquired its credentials by proving to be crucial in the transmission of respiratory influence on the brain via the sensitivity to nasal airflow of its receptor cells. Here, we present recent findings evidencing respiration-related activities in the brain. Then, we review the data explaining the fact that breathing is (i) nasal and (ii) being slow and deep is crucial in its ability to stimulate the olfactory system and consequently influence the brain. In conclusion, we propose a possible scenario explaining how this optimal respiratory regime can promote changes in brain dynamics of an olfacto-limbic-respiratory circuit, providing a possibility to induce calm and relaxation by coordinating breathing regime and brain state.

Odor-evoked increases in olfactory bulb mitral cell spiking variability

The spiking variability of neural networks has important implications for how information is encoded to higher brain regions. It has been well documented by numerous labs in many cortical and motor regions that spiking variability decreases with stimulus onset, yet whether this principle holds in the OB has not been tested. In stark contrast to this common view, we demonstrate that the onset of sensory input can cause an increase in the variability of neural activity in the mammalian OB. We show this in both anesthetized and awake rodents. Furthermore, we use computational models to describe the mechanisms of this phenomenon. Our findings establish sensory evoked increases in spiking variability as a viable alternative coding strategy.

Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models

The majority of olfaction studies focus on orthonasal stimulation where odors enter via the front nasal cavity, while retronasal olfaction, where odors enter the rear of the nasal cavity during feeding, is understudied. The coding of retronasal odors via coordinated spiking of neurons in the olfactory bulb (OB) is largely unknown despite evidence that higher level processing is different than orthonasal. To this end, we use multi-electrode array in vivo recordings of rat OB mitral cells (MC) in response to a food odor with both modes of stimulation, and find significant differences in evoked firing rates and spike count covariances (i.e., noise correlations). Differences in spiking activity often have implications for sensory coding, thus we develop a single-compartment biophysical OB model that is able to reproduce key properties of important OB cell types. Prior experiments in olfactory receptor neurons (ORN) showed retro stimulation yields slower and spatially smaller ORN inputs than with ortho, yet whether this is consequential for OB activity remains unknown. Indeed with these specifications for ORN inputs, our OB model captures the salient trends in our OB data. We also analyze how first and second order ORN input statistics dynamically transfer to MC spiking statistics with a phenomenological linear-nonlinear filter model, and find that retro inputs result in larger linear filters than ortho inputs. Finally, our models show that the temporal profile of ORN is crucial for capturing our data and is thus a distinguishing feature between ortho and retro stimulation, even at the OB. Using data-driven modeling, we detail how ORN inputs result in differences in OB dynamics and MC spiking statistics. These differences may ultimately shape how ortho and retro odors are coded.

Olfaction is a key sense for many cognitive and behavioral tasks, and is particularly unique because odors can naturally enter the nasal cavity from the front or rear, i.e., ortho- and retro-nasal, respectively. Yet little is known about the differences in coordinated spiking in the olfactory bulb with ortho versus retro stimulation, let alone how these different modes of olfaction may alter coding of odors. We simultaneously record many cells in rat olfactory bulb to assess the differences in spiking statistics, and develop a biophysical olfactory bulb network model to study the reasons for these differences. Using theoretical and computational methods, we find that the olfactory bulb transfers input statistics differently for retro stimulation relative to ortho stimulation. Furthermore, our models show that the temporal profile of inputs is crucial for capturing our data and is thus a distinguishing feature between ortho and retro stimulation, even at the olfactory bulb. Understanding the spiking dynamics of the olfactory bulb with both ortho and retro stimulation is a key step for ultimately understanding how the brain codes odors with different modes of olfaction.

Olfactory marker protein contributes to the evaluation of odour values by olfactory glomerular processing

Olfaction starts from olfactory receptor neurons (ORNs) that express olfactory marker protein (OMP). OMP deficit results in various behavioural phenotypes indicating olfactory dysfunction due to the impaired responses of ORNs. Recently, OMP was demonstrated to maintain strong olfaction by buffering olfactory cAMP signalling. However, the impact of OMP on olfaction behaviours, the assessment of which requires time to evaluate odour values, remains largely unexplained. Here, we examined the behaviour of heterozygous OMP^+/GFP (HET) mice vs. homologous GFP-knock-in OMP-deficient OMP ^{GFP/ GFP} (KI) mice during the olfactory investigation of odours with different values. When a swab containing an organic odour was presented, both HET and KI mice swiftly approached and investigated the swab with gradual habituation over test sessions. However, when another similar odour was presented, KI mice investigated the new swab much less intensively than HET mice. Next, mice were placed in a chamber with an aversive odour source in one corner of a test chamber. KI mice more frequently approached the compartment containing the aversive odour source than HET mice. Finally, we trained mice to associate two odours with solutions by utilizing reward-penalty values. HET mice stayed close to the reward-associated odour, while KI mice initially approached the reward-associated odour, occasionally turned towards the penalty-associated odour source and eventually stayed in the reward-odour compartment. Histologically, c-Fos-expressing juxtaglomerular cells were fewer and more broadly distributed around glomeruli in KI mice than HET mice. In conclusion, OMP contributes to the evaluation of odour values by glomerular processing during an olfactory investigation task.

Olfactory marker protein directly buffers cAMP to avoid depolarization-induced silencing of olfactory receptor neurons

Olfactory receptor neurons (ORNs) use odour-induced intracellular cAMP surge to gate cyclic nucleotide-gated nonselective cation (CNG) channels in cilia. Prolonged exposure to cAMP causes calmodulin-dependent feedback-adaptation of CNG channels and attenuates neural responses. On the other hand, the odour-source searching behaviour requires ORNs to be sensitive to odours when approaching targets. How ORNs accommodate these conflicting aspects of cAMP responses remains unknown. Here, we discover that olfactory marker protein (OMP) is a major cAMP buffer that maintains the sensitivity of ORNs. Upon the application of sensory stimuli, OMP directly captured and swiftly reduced freely available cAMP, which transiently uncoupled downstream CNG channel activity and prevented persistent depolarization. Under repetitive stimulation, OMP^-/- ORNs were immediately silenced after burst firing due to sustained depolarization and inactivated firing machinery. Consequently, OMP^-/- mice showed serious impairment in odour-source searching tasks. Therefore, cAMP buffering by OMP maintains the resilient firing of ORNs.

Coding of odors in the anterior olfactory nucleus

Odorant molecules stimulate olfactory receptor neurons, and axons of these neurons project into the main olfactory bulb where they synapse onto mitral and tufted cells. These project to the primary olfactory cortex including the anterior olfactory nucleus (AON), the piriform cortex, amygdala, and the entorhinal cortex. The properties of mitral cells have been investigated extensively, but how odor information is processed in subsequent brain regions is less well known. In the present study, we recorded the electrical activity of AON neurons in anesthetized rats. Most AON cells fired in bursts of 2-10 spikes separated by very short intervals (<20 ms), in a period linked to the respiratory rhythm. Simultaneous recordings from adjacent neurons revealed that the rhythms of adjacent cells, while locked to the same underlying rhythm, showed marked differences in phase. We studied the responses of AON cells to brief high-frequency stimulation of the lateral olfactory tract, mimicking brief activation of mitral cells by odor. In different cells, such stimuli evoked transient or sustained bursts during stimulation or, more commonly, post-stimulation bursts after inhibition during stimulation. This suggests that, in AON cells, phase shifts occur as a result of post-inhibitory rebound firing, following inhibition by mitral cell input, and we discuss how this supports processing of odor information in the olfactory pathway. Cells were tested for their responsiveness to a social odor (the bedding of a strange male) among other simple and complex odors tested. In total, 11 cells responded strongly and repeatedly to bedding odor, and these responses were diverse, including excitation (transient or sustained), inhibition, and activation after odor presentation, indicating that AON neurons respond not only to the type of complex odor but also to temporal features of odor application.

In Vivo Electrophysiological Recordings of Olfactory Receptor Neuron Units and Electro-olfactograms in Anesthetized Rats

In vivo recordings of single olfactory receptor neurons and electro-olfactograms (EOG, field potentials from the olfactory mucosa) provide insights into the olfactory processing properties of the olfactory peripheral stage. Because the olfactory receptor neurons are very small electrical generators, it is not easy to unitarily record them in amphibians, reptilians, and fishes. In mammals such recordings are even more difficult to obtain: primarily due to the anatomical configuration in complex turbinates of the olfactory mucosa and its propensity to hemorrhage during surgery; secondarily due to the fact that olfactory receptor neurons are held in closely packed clusters in the olfactory mucosa and are difficult to isolate, from the electrophysiological recording point of view. Here we describe the material and methods we used in vivo, in rats-occasionally, also tested in mice-to get simultaneously receptor neuron single and electro-olfactogram recordings, from septal region or the endoturbinate II, in freely breathing or tracheotomized anesthetized animals. Recording EOG in parallel with receptor neuron units provide, by reflecting the population response to the olfactory stimulus, the continuous assurance of the good physiological state and reactivity of the olfactory epithelium. This configuration will ensure that when a single ORN does not respond to a stimulus it resulted from its qualitative selectivity and not from the olfactory mucosa damaged status.

Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing

The olfactory system, like other sensory systems, can detect specific stimuli of interest amidst complex, varying backgrounds. To gain insight into the neural mechanisms underlying this ability, we imaged responses of mouse olfactory bulb glomeruli to mixtures. We used this data to build a model of mixture responses that incorporated nonlinear interactions and trial-to-trial variability and explored potential decoding mechanisms that can mimic mouse performance when given glomerular responses as input. We find that a linear decoder with sparse weights could match mouse performance using just a small subset of the glomeruli (∼15). However, when such a decoder is trained only with single odors, it generalizes poorly to mixture stimuli due to nonlinear mixture responses. We show that mice similarly fail to generalize, suggesting that they learn this segregation task discriminatively by adjusting task-specific decision boundaries without taking advantage of a demixed representation of odors.

Efficient information transfer by Poisson neurons

Recently, it has been suggested that certain neurons with Poissonian spiking statistics may communicate by discontinuously switching between two levels of firing intensity. Such a situation resembles in many ways the optimal information transmission protocol for the continuous-time Poisson channel known from information theory. In this contribution we employ the classical information-theoretic results to analyze the efficiency of such a transmission from different perspectives, emphasising the neurobiological viewpoint. We address both the ultimate limits, in terms of the information capacity under metabolic cost constraints, and the achievable bounds on performance at rates below capacity with fixed decoding error probability. In doing so we discuss optimal values of experimentally measurable quantities that can be compared with the actual neuronal recordings in a future effort.

Distribution of transition times in a stochastic model of excitable cell: Insights into the cell-intrinsic mechanisms of randomness in neuronal interspike intervals

In this paper, we develop an analytical approach to studying random patterns of activity in excitable cells. Our analytical approach uses a two-state stochastic model of excitable system based on the electrophysiological properties of refractoriness and restitution, which characterize cell recovery after excitation. By applying the notion of probability density flux, we derive the distributions of transition times between states and the distribution of interspike interval (ISI) durations for a constant applied stimulus. The derived ISI distribution is unimodal and, provided that the time spent in the excited state is constant, can be approximated by a Rayleigh peak followed by an exponential tail. We then explore the role of the model parameters in determining the shape of the derived distributions and the ISI coefficient of variation. Finally, we use our analytical results to study simulation results from the stochastic Morris-Lecar neuron and from a three-state extension of the proposed stochastic model, which is capable of reproducing multimodal ISI histograms.

Performance breakdown in optimal stimulus decoding

OBJECTIVE

One of the primary goals of neuroscience is to understand how neurons encode and process information about their environment. The problem is often approached indirectly by examining the degree to which the neuronal response reflects the stimulus feature of interest.

APPROACH

In this context, the methods of signal estimation and detection theory provide the theoretical limits on the decoding accuracy with which the stimulus can be identified. The Cramér-Rao lower bound on the decoding precision is widely used, since it can be evaluated easily once the mathematical model of the stimulus-response relationship is determined. However, little is known about the behavior of different decoding schemes with respect to the bound if the neuronal population size is limited.

MAIN RESULTS

We show that under broad conditions the optimal decoding displays a threshold-like shift in performance in dependence on the population size. The onset of the threshold determines a critical range where a small increment in size, signal-to-noise ratio or observation time yields a dramatic gain in the decoding precision.

SIGNIFICANCE

We demonstrate the existence of such threshold regions in early auditory and olfactory information coding. We discuss the origin of the threshold effect and its impact on the design of effective coding approaches in terms of relevant population size.

Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding

Stimulus encoding by primary sensory brain areas provides a data-rich context for understanding their circuit mechanisms. The vertebrate olfactory bulb is an input area having unusual two-layer dendro-dendritic connections whose roles in odor coding are unclear. To clarify these roles, we built a detailed compartmental model of the rat olfactory bulb that synthesizes a much wider range of experimental observations on bulbar physiology and response dynamics than has hitherto been modeled. We predict that superficial-layer inhibitory interneurons (periglomerular cells) linearize the input-output transformation of the principal neurons (mitral cells), unlike previous models of contrast enhancement. The linearization is required to replicate observed linear summation of mitral odor responses. Further, in our model, action-potentials back-propagate along lateral dendrites of mitral cells and activate deep-layer inhibitory interneurons (granule cells). Using this, we propose sparse, long-range inhibition between mitral cells, mediated by granule cells, to explain how the respiratory phases of odor responses of sister mitral cells can be sometimes decorrelated as observed, despite receiving similar receptor input. We also rule out some alternative mechanisms. In our mechanism, we predict that a few distant mitral cells receiving input from different receptors, inhibit sister mitral cells differentially, by activating disjoint subsets of granule cells. This differential inhibition is strong enough to decorrelate their firing rate phases, and not merely modulate their spike timing. Thus our well-constrained model suggests novel computational roles for the two most numerous classes of interneurons in the bulb.

The effect of interspike interval statistics on the information gain under the rate coding hypothesis

The question, how much information can be theoretically gained from variable neuronal firing rate with respect to constant average firing rate is investigated. We employ the statistical concept of information based on the Kullback-Leibler divergence, and assume rate-modulated renewal processes as a model of spike trains. We show that if the firing rate variation is sufficiently small and slow (with respect to the mean interspike interval), the information gain can be expressed by the Fisher information. Furthermore, under certain assumptions, the smallest possible information gain is provided by gamma-distributed interspike intervals. The methodology is illustrated and discussed on several different statistical models of neuronal activity.

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.

Neuronal activity in the bed nucleus of the stria terminalis in a rat model for obsessive-compulsive disorder

In search of a new potential target for deep brain stimulation in patients with obsessive-compulsive disorder (OCD), we evaluated the single-cell activity of neurons in the bed nucleus of the stria terminalis (BST) in urethane-anesthetized rats in an animal model for OCD, the schedule-induced polydipsia (SIP) model, and compared this to the BST activity in control rats and to a third group of rats which were introduced in the model but did not develop the SIP, and thus were considered resistant. We compared the firing rate and firing pattern of BST neurons between these groups, between hemispheres and made a correlation of the firing rate and firing pattern to the position in the BST. The variability of BST neurons in SIP rats was lower and the randomness higher than BST neurons in control rats or resistant rats. The firing rate of BST neurons in SIP rats was significantly higher and the burst index lower than BST neurons in resistant rats but not in control rats. Also, neurons from the right hemisphere in the SIP group had a higher burst index than neurons from the left hemisphere. However, this is opposite in the resistant and control group. Third, we found a higher bursting index with increasing (more ventral) depth of recording. These findings suggest that schedule-induced polydipsia, which models compulsive behavior in humans, induces a change in firing behavior of BST neurons.

Estimating individual firing frequencies in a multiple spike train record

Neuronal activity of several neurons is commonly recorded by a single electrode and then the individual spike trains are separated. If the separation is difficult or fails, then as a minimal result of the experiment, the individual firing rates are of interest. The proposed method solves the problem of their identification. This is possible under the condition that the recorded neurons are independent in their activities. The number of the neurons in the multi-unit record needs to be given (known or assumed) prior the calculation. The proposed method is based on the presence of the refractory period in neuronal firing, however, its precise value is not required. In addition to the determination of the individual firing rates the method can be used for an inference about the refractory period itself.

The source of spontaneous activity in the main olfactory bulb of the rat

Introduction

In vivo, most neurons in the main olfactory bulb exhibit robust spontaneous activity. This paper tests the hypothesis that spontaneous activity in olfactory receptor neurons drives much of the spontaneous activity in mitral and tufted cells via excitatory synapses.

Methods

Single units were recorded in vivo from the main olfactory bulb of a rat before, during, and after application of lidocaine to the olfactory nerve. The effect of lidocaine on the conduction of action potentials from the olfactory epithelium to the olfactory bulb was assessed by electrically stimulating the olfactory nerve rostral to the application site and monitoring the field potential evoked in the bulb.

Results

Lidocaine caused a significant decrease in the amplitude of the olfactory nerve evoked field potential that was recorded in the olfactory bulb. By contrast, the lidocaine block did not significantly alter the spontaneous activity of single units in the bulb, nor did it alter the field potential evoked by electrical stimulation of the lateral olfactory tract. Lidocaine block also did not change the temporal patters of action potential or their synchronization with respiration.

Conclusions

Spontaneous activity in neurons of the main olfactory bulb is not driven mainly by activity in olfactory receptor neurons despite the extensive convergence onto mitral and tufted cells. These results suggest that spontaneous activity of mitral and tufted is either an inherent property of these cells or is driven by centrifugal inputs to the bulb.

Variability measures of positive random variables

During the stationary part of neuronal spiking response, the stimulus can be encoded in the firing rate, but also in the statistical structure of the interspike intervals. We propose and discuss two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information-based dispersion. The measures are compared with the frequently used concept of standard deviation. It is shown, that standard deviation is not well suited to quantify some aspects of dispersion that are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not entirely independent, although each describes the interspike intervals from a different point of view. The new methods are applied to common models of neuronal firing and to both simulated and experimental data.

Firing variability is higher than deduced from the empirical coefficient of variation

A convenient and often used summary measure to quantify the firing variability in neurons is the coefficient of variation (CV), defined as the standard deviation divided by the mean. It is therefore important to find an estimator that gives reliable results from experimental data, that is, the estimator should be unbiased and have low estimation variance. When the CV is evaluated in the standard way (empirical standard deviation of interspike intervals divided by their average), then the estimator is biased, underestimating the true CV, especially if the distribution of the interspike intervals is positively skewed. Moreover, the estimator has a large variance for commonly used distributions. The aim of this letter is to quantify the bias and propose alternative estimation methods. If the distribution is assumed known or can be determined from data, parametric estimators are proposed, which not only remove the bias but also decrease the estimation errors. If no distribution is assumed and the data are very positively skewed, we propose to correct the standard estimator. When defining the corrected estimator, we simply use that it is more stable to work on the log scale for positively skewed distributions. The estimators are evaluated through simulations and applied to experimental data from olfactory receptor neurons in rats.

Reshaping of bulbar odor response by nasal flow rate in the rat

Background

The impact of respiratory dynamics on odor response has been poorly studied at the olfactory bulb level. However, it has been shown that sniffing in the behaving rodent is highly dynamic and varies both in frequency and flow rate. Bulbar odor response could vary with these sniffing parameter variations. Consequently, it is necessary to understand how nasal airflow can modify and shape odor response at the olfactory bulb level.

Methodology and Principal Findings

To assess this question, we used a double cannulation and simulated nasal airflow protocol on anesthetized rats to uncouple nasal airflow from animal respiration. Both mitral/tufted cell extracellular unit activity and local field potentials (LFPs) were recorded. We found that airflow changes in the normal range were sufficient to substantially reorganize the response of the olfactory bulb. In particular, cellular odor-evoked activities, LFP oscillations and spike phase-locking to LFPs were strongly modified by nasal flow rate.

Conclusion

Our results indicate the importance of reconsidering the notion of odor coding as odor response at the bulbar level is ceaselessly modified by respiratory dynamics.

Pheromone transduction in moths

Calling female moths attract their mates late at night with intermittent release of a species-specific sex-pheromone blend. Mean frequency of pheromone filaments encodes distance to the calling female. In their zig-zagging upwind search male moths encounter turbulent pheromone blend filaments at highly variable concentrations and frequencies. The male moth antennae are delicately designed to detect and distinguish even traces of these sex pheromones amongst the abundance of other odors. Its olfactory receptor neurons sense even single pheromone molecules and track intermittent pheromone filaments of highly variable frequencies up to about 30 Hz over a wide concentration range. In the hawkmoth Manduca sexta brief, weak pheromone stimuli as encountered during flight are detected via a metabotropic PLCβ-dependent signal transduction cascade which leads to transient changes in intracellular Ca²⁺ concentrations. Strong or long pheromone stimuli, which are possibly perceived in direct contact with the female, activate receptor-guanylyl cyclases causing long-term adaptation. In addition, depending on endogenous rhythms of the moth's physiological state, hormones such as the stress hormone octopamine modulate second messenger levels in sensory neurons. High octopamine levels during the activity phase maximize temporal resolution cAMP-dependently as a prerequisite to mate location. Thus, I suggest that sliding adjustment of odor response threshold and kinetics is based upon relative concentration ratios of intracellular Ca²⁺ and cyclic nucleotide levels which gate different ion channels synergistically. In addition, I propose a new hypothesis for the cyclic nucleotide-dependent ion channel formed by insect olfactory receptor/coreceptor complexes. Instead of being employed for an ionotropic mechanism of odor detection it is proposed to control subthreshold membrane potential oscillation of sensory neurons, as a basis for temporal encoding of odors.

Physiological evidence for two classes of mitral cells in the rat olfactory bulb

The spontaneous activity of mitral cells was recorded in vivo from the main olfactory bulb of freely breathing anesthetized rats. Single units recorded extracellularly from the mitral cell body layer were further identified as mitral cells by antidromic activation of the lateral olfactory tract and the posterior piriform cortex. Hierarchical cluster analysis of their spontaneous activity showed that at least two classes of mitral cells could be distinguished. A post-hoc multivariate analysis of variance indicated significant differences between the two groups based on mean rate, latency, and the coefficient of variation in interspike interval. Univariate tests showed that the groups differed in mean rate, but not in latency, or in the coefficient of variation in interspike interval. Autocorrelation analysis showed that the high frequency group tended to fire in bursts. Functional implications of these putative subclasses of mitral cells are discussed.

The role of Hopf bifurcation dynamics in sensory processes

We emphasize here the role of the Hopf bifurcation in detection of stimuli in sensory processes--we discuss in particular chemosensors. It is shown that the essential nonlinearities inherent in the signal transduction mechanism can take advantage of the noise from the environment the system is subject to, to display a highly amplified response to stimuli in a frequency-selective manner. It is shown that in the absence of any externally applied stimulus, the feedback mechanisms playing a regulatory role in the transduction mechanism can give rise, in the presence of noise, to peaks in the spectral power density, suggesting enhanced spontaneous activity in sensory cells. The power law in this spectrum is determined.

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.

Parameters of stochastic diffusion processes estimated from observations of first-hitting times: application to the leaky integrate-and-fire neuronal model

A theoretical model has to stand the test against the real world to be of any practical use. The first step is to identify parameters in the model estimated from experimental data. In many applications where renewal point data are available, models of first-hitting times of underlying diffusion processes arise. Despite the seemingly simplicity of the model, the problem of how to estimate parameters of the underlying stochastic process has resisted solution. The few attempts have either been unreliable, difficult to implement, or only valid in subsets of the relevant parameter space. Here we present an estimation method that overcomes these difficulties, is computationally easy and fast to implement, and also works surprisingly well on small data sets. The method is illustrated on simulated and experimental data. Two common neuronal models--the Ornstein-Uhlenbeck and Feller models--are investigated.

Interspike interval statistics in the stochastic Hodgkin-Huxley model: coexistence of gamma frequency bursts and highly irregular firing

When the classical Hodgkin-Huxley equations are simulated with Na- and K-channel noise and constant applied current, the distribution of interspike intervals is bimodal: one part is an exponential tail, as often assumed, while the other is a narrow gaussian peak centered at a short interspike interval value. The gaussian arises from bursts of spikes in the gamma-frequency range, the tail from the interburst intervals, giving overall an extraordinarily high coefficient of variation--up to 2.5 for 180,000 Na channels when I approximately 7 microA/cm(2). Since neurons with a bimodal ISI distribution are common, it may be a useful model for any neuron with class 2 firing. The underlying mechanism is due to a subcritical Hopf bifurcation, together with a switching region in phase-space where a fixed point is very close to a system limit cycle. This mechanism may be present in many different classes of neurons and may contribute to widely observed highly irregular neural spiking.

The parameters of the stochastic leaky integrate-and-fire neuronal model

Five parameters of one of the most common neuronal models, the diffusion leaky integrate-and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on the basis of intracellular recording. These parameters can be classified into two categories. Three of them (the membrane time constant, the resting potential and the firing threshold) characterize the neuron itself. The remaining two characterize the neuronal input. The intracellular data were collected during spontaneous firing, which in this case is characterized by a Poisson process of interspike intervals. Two methods for the estimation were applied, the regression method and the maximum-likelihood method. Both methods permit to estimate the input parameters and the membrane time constant in a short time window (a single interspike interval). We found that, at least in our example, the regression method gave more consistent results than the maximum-likelihood method. The estimates of the input parameters show the asymptotical normality, which can be further used for statistical testing, under the condition that the data are collected in different experimental situations. The model neuron, as deduced from the determined parameters, works in a subthreshold regimen. This result was confirmed by both applied methods. The subthreshold regimen for this model is characterized by the Poissonian firing. This is in a complete agreement with the observed interspike interval data.

Classification of stationary neuronal activity according to its information rate

We propose a measure of the information rate of a single stationary neuronal activity with respect to the state of null information. The measure is based on the Kullback-Leibler distance between two interspike interval distributions. The selected activity is compared with the Poisson model with the same mean firing frequency. We show that the approach is related to the notion of specific information and that the method allows us to judge the relative encoding efficiency. Two classes of neuronal activity models are classified according to their information rate: the renewal process models and the first-order Markov chain models. It has been proven that information can be transmitted changing neither the spike rate nor the coefficient of variation and that the increase in serial correlation does not necessarily increase the information gain. We employ the simple, but powerful, Vasicek's estimator of differential entropy to illustrate an application on the experimental data coming from olfactory sensory neurons of rats.

Similarity of interspike interval distributions and information gain in a stationary neuronal firing

The Kullback-Leibler (KL) information distance is proposed for judging similarity between two different interspike interval (ISI) distributions. The method is applied by a comparison of four common ISI descriptors with an exponential model which is characterized by the highest entropy. Under the condition of equal mean ISI values, the KL distance corresponds to information gain coming from the state described by the exponential distribution to the state described by the chosen ISI model. It has been shown that information can be transmitted changing neither the spike rate nor coefficient of variation (CV). Furthermore the KL distance offer an indication of the exponentiality of the chosen ISI descriptor (or data): the distance is zero if, and only if, the ISIs are distributed exponentially. Finally an application on experimental data coming from the olfactory sensory neurons of rats is shown.

Humans as an animal model for systems-level organization of olfaction

The past 15 years have seen significant advances in the study of olfaction, with particular emphasis on elucidating the molecular building blocks of the sensory process. However, much of the systems-level organization of olfaction remains unexplored. Here, we provide an overview at this level, highlighting results obtained from studying humans, whom we think provide an underutilized, yet critical, animal model for olfaction.