Intensity versus identity coding in an olfactory system

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

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

Cortical control of arm movements: a dynamical systems perspective

Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.

Coordinated population activity underlying texture discrimination in rat barrel cortex

Rodents can robustly distinguish fine differences in texture using their whiskers, a capacity that depends on neuronal activity in primary somatosensory "barrel" cortex. Here we explore how texture was collectively encoded by populations of three to seven neuronal clusters simultaneously recorded from barrel cortex while a rat performed a discrimination task. Each cluster corresponded to the single-unit or multiunit activity recorded at an individual electrode. To learn how the firing of different clusters combines to represent texture, we computed population activity vectors across moving time windows and extracted the signal available in the optimal linear combination of clusters. We quantified this signal using receiver operating characteristic analysis and compared it to that available in single clusters. Texture encoding was heterogeneous across neuronal clusters, and only a minority of clusters carried signals strong enough to support stimulus discrimination on their own. However, jointly recorded groups of clusters were always able to support texture discrimination at a statistically significant level, even in sessions where no individual cluster represented the stimulus. The discriminative capacity of neuronal activity was degraded when error trials were included in the data, compared to only correct trials, suggesting a link between the neuronal activity and the animal's performance. These analyses indicate that small groups of barrel cortex neurons can robustly represent texture identity through synergistic interactions, and suggest that neurons downstream to barrel cortex could extract texture identity on single trials through simple linear combination of barrel cortex responses.

Olfactory cortical neurons read out a relative time code in the olfactory bulb

Odor stimulation evokes complex spatiotemporal activity in the olfactory bulb, suggesting that the identity of activated neurons as well as the timing of their activity convey information about odors. However, whether and how downstream neurons decipher these temporal patterns remains debated. We addressed this question by measuring the spiking activity of downstream neurons while optogenetically stimulating two foci in the olfactory bulb with varying relative timing in mice. We found that the overall spike rates of piriform cortex neurons were sensitive to the relative timing of activation. Posterior piriform cortex neurons showed higher sensitivity to relative input times than neurons in the anterior piriform cortex. In contrast, olfactory bulb neurons rarely showed such sensitivity. Thus, the brain can transform a relative time code in the periphery into a firing-rate-based representation in central brain areas, providing evidence for the relevance of relative time-based code in the olfactory bulb.

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.

Neural dynamics of attentional cross-modality control

Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing.

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.

Coding of odors by temporal binding within a model network of the locust antennal lobe

The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin-Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements.

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

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

Organization of antennal lobe-associated neurons in adult Drosophila melanogaster brain

The primary olfactory centers of both vertebrates and insects are characterized by glomerular structure. Each glomerulus receives sensory input from a specific type of olfactory sensory neurons, creating a topographic map of the odor quality. The primary olfactory center is also innervated by various types of neurons such as local neurons, output projection neurons (PNs), and centrifugal neurons from higher brain regions. Although recent studies have revealed how olfactory sensory input is conveyed to each glomerulus, it still remains unclear how the information is integrated and conveyed to other brain areas. By using the GAL4 enhancer-trap system, we conducted a systematic mapping of the neurons associated with the primary olfactory center of Drosophila, the antennal lobe (AL). We identified in total 29 types of neurons, among which 13 are newly identified in the present study. Analyses of arborizations of these neurons in the AL revealed how glomeruli are linked with each other, how different PNs link these glomeruli with multiple secondary sites, and how these secondary sites are organized by the projections of the AL-associated neurons.

Parallel processing via a dual olfactory pathway in the honeybee

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

Concentration-invariant odor representation in the olfactory system by presynaptic inhibition

The present study investigates a network model for implementing concentration-invariant representation for odors in the olfactory system. The network consists of olfactory receptor neurons, projection neurons, and inhibitory local neurons. Receptor neurons send excitatory inputs to projection neurons, which are modulated by the inhibitory inputs from local neurons. The modulation occurs at the presynaptic site from a receptor neuron to a projection one, leading to the operation of divisive normalization. The responses of local interneurons are determined by the total activities of olfactory receptor neurons. We find that with a proper parameter condition, the responses of projection neurons become effectively independent of the odor concentration. Simulation results confirm our theoretical analysis.

Multi-unit recording methods to characterize neural activity in the locust (Schistocerca americana) olfactory circuits

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons). The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits. First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings. We provide details of our experimental setup and present representative recording traces for each of these techniques.

Network architecture underlying maximal separation of neuronal representations

One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe-mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon cells (KCs), and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a straightforward way to construct ecologically meaningful representations from this code.

Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks

Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships between time-varying inputs and outputs with complex temporal dependencies. Recently developed algorithms have been successful at training RNNs to perform a wide variety of tasks, but the resulting networks have been treated as black boxes: their mechanism of operation remains unknown. Here we explore the hypothesis that fixed points, both stable and unstable, and the linearized dynamics around them, can reveal crucial aspects of how RNNs implement their computations. Further, we explore the utility of linearization in areas of phase space that are not true fixed points but merely points of very slow movement. We present a simple optimization technique that is applied to trained RNNs to find the fixed and slow points of their dynamics. Linearization around these slow regions can be used to explore, or reverse-engineer, the behavior of the RNN. We describe the technique, illustrate it using simple examples, and finally showcase it on three high-dimensional RNN examples: a 3-bit flip-flop device, an input-dependent sine wave generator, and a two-point moving average. In all cases, the mechanisms of trained networks could be inferred from the sets of fixed and slow points and the linearized dynamics around them.

Ensemble response in mushroom body output neurons of the honey bee outpaces spatiotemporal odor processing two synapses earlier in the antennal lobe

Neural representations of odors are subject to computations that involve sequentially convergent and divergent anatomical connections across different areas of the brains in both mammals and insects. Furthermore, in both mammals and insects higher order brain areas are connected via feedback connections. In order to understand the transformations and interactions that this connectivity make possible, an ideal experiment would compare neural responses across different, sequential processing levels. Here we present results of recordings from a first order olfactory neuropile – the antennal lobe (AL) – and a higher order multimodal integration and learning center – the mushroom body (MB) – in the honey bee brain. We recorded projection neurons (PN) of the AL and extrinsic neurons (EN) of the MB, which provide the outputs from the two neuropils. Recordings at each level were made in different animals in some experiments and simultaneously in the same animal in others. We presented two odors and their mixture to compare odor response dynamics as well as classification speed and accuracy at each neural processing level. Surprisingly, the EN ensemble significantly starts separating odor stimuli rapidly and before the PN ensemble has reached significant separation. Furthermore the EN ensemble at the MB output reaches a maximum separation of odors between 84–120 ms after odor onset, which is 26 to 133 ms faster than the maximum separation at the AL output ensemble two synapses earlier in processing. It is likely that a subset of very fast PNs, which respond before the ENs, may initiate the rapid EN ensemble response. We suggest therefore that the timing of the EN ensemble activity would allow retroactive integration of its signal into the ongoing computation of the AL via centrifugal feedback.

The impact of adult neurogenesis on olfactory bulb circuits and computations

Modern neuroscience has demonstrated how the adult brain has the ability to profoundly remodel its neurons in response to changes in external stimuli or internal states. However, adult brain plasticity, although possible throughout life, remains restricted mostly to subcellular levels rather than affecting the entire cell. New neurons are continuously generated in only a few areas of the adult brain-the olfactory bulb and the dentate gyrus-where they integrate into already functioning circuitry. In these regions, adult neurogenesis adds another dimension of plasticity that either complements or is redundant to the classical molecular and cellular mechanisms of plasticity. This review extracts clues regarding the contribution of adult-born neurons to the different circuits of the olfactory bulb and specifically how new neurons participate in existing computations and enable new computational functions.

Coding odorant concentration through activation timing between the medial and lateral olfactory bulb

In mammals, each olfactory bulb (OB) contains a pair of mirror-symmetric glomerular maps organized to reflect odorant receptor identity. The functional implication of maintaining these symmetric medial-lateral maps within each OB remains unclear. Here, using in vivo multielectrode recordings to simultaneously detect odorant-induced activity across the entire OB, we reveal a timing difference in the odorant-evoked onset latencies between the medial and lateral halves. Interestingly, the latencies in the medial and lateral OB decreased at different rates as odorant concentration increased, causing the timing difference between them to also diminish. As a result, output neurons in the medial and lateral OB fired with greater synchrony at higher odorant concentrations. Thus, we propose that temporal differences in activity between the medial and lateral OB can dynamically code odorant concentration, which is subsequently decoded in the olfactory cortex through the integration of synchronous action potentials.

Evolving a neural olfactorimotor system in virtual and real olfactory environments

To provide a platform to enable the study of simulated olfactory circuitry in context, we have integrated a simulated neural olfactorimotor system with a virtual world which simulates both computational fluid dynamics as well as a robotic agent capable of exploring the simulated plumes. A number of the elements which we developed for this purpose have not, to our knowledge, been previously assembled into an integrated system, including: control of a simulated agent by a neural olfactorimotor system; continuous interaction between the simulated robot and the virtual plume; the inclusion of multiple distinct odorant plumes and background odor; the systematic use of artificial evolution driven by olfactorimotor performance (e.g., time to locate a plume source) to specify parameter values; the incorporation of the realities of an imperfect physical robot using a hybrid model where a physical robot encounters a simulated plume. We close by describing ongoing work toward engineering a high dimensional, reversible, low power electronic olfactory sensor which will allow olfactorimotor neural circuitry evolved in the virtual world to control an autonomous olfactory robot in the physical world. The platform described here is intended to better test theories of olfactory circuit function, as well as provide robust odor source localization in realistic environments.

Population-wide distributions of neural activity during perceptual decision-making

Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding.

Keeping their distance? Odor response patterns along the concentration range

We investigate the interplay of odor identity and concentration coding in the antennal lobe (AL) of the honeybee Apis mellifera. In this primary olfactory center of the honeybee brain, odors are encoded by the spatio-temporal response patterns of olfactory glomeruli. With rising odor concentration, further glomerular responses are recruited into the patterns, which affects distances between the patterns. Based on calcium-imaging recordings, we found that such pattern broadening renders distances between glomerular response patterns closer to chemical distances between the corresponding odor molecules. Our results offer an explanation for the honeybee's improved odor discrimination performance at higher odor concentrations.

Activity in Neurons of a Putative Protocerebral Circuit Representing Information about a 10 Component Plant Odor Blend in Heliothis virescens

The olfactory pathway in the insect brain is anatomically well described from the antennal lobe (AL) to the mushroom bodies and the lateral protocerebrum (LP) in several species. Less is known about the further connections of the olfactory network in protocerebrum and how information about relevant plant odorants and mixtures are represented in this network, resulting in output information mediated by descending neurons. In the present study we have recorded intracellularly followed by dye injections from neurons in the LP and superior protocerebrum (SP) of the moth, Heliothis virescens. As relevant stimuli, we have used selected primary plant odorants and mixtures of them. The results provide the morphology and physiological responses of neurons involved in a putative circuit connecting the mushroom body lobes, the SP, and the LP, as well as input to SP and LP by one multiglomerular AL neuron and output from the LP by one descending neuron. All neurons responded to a particular mixture of ten primary plant odorants, some of them also to single odorants of the mixture. Altogether, the physiological data indicate integration in protocerebral neurons of information from several of the receptor neuron types functionally described in this species.

Functional analysis of a higher olfactory center, the lateral horn

The lateral horn (LH) of the insect brain is thought to play several important roles in olfaction, including maintaining the sparseness of responses to odors by means of feedforward inhibition, and encoding preferences for innately meaningful odors. Yet relatively little is known of the structure and function of LH neurons (LHNs), making it difficult to evaluate these ideas. Here we surveyed >250 LHNs in locusts using intracellular recordings to characterize their responses to sensory stimuli, dye-fills to characterize their morphologies, and immunostaining to characterize their neurotransmitters. We found a great diversity of LHNs, suggesting this area may play multiple roles. Yet, surprisingly, we found no evidence to support a role for these neurons in the feedforward inhibition proposed to mediate olfactory response sparsening; instead, it appears that another mechanism, feedback inhibition from the giant GABAergic neuron, serves this function. Further, all LHNs we observed responded to all odors we tested, making it unlikely these LHNs serve as labeled lines mediating specific behavioral responses to specific odors. Our results rather point to three other possible roles of LHNs: extracting general stimulus features such as odor intensity; mediating bilateral integration of sensory information; and integrating multimodal sensory stimuli.

Bone marrow cell transplantation restores olfaction in the degenerated olfactory bulb

Bone marrow contains heterogeneous cell types including end-lineage cells, committed tissue progenitors, and multipotent stem/progenitor cells. The immense plasticity of bone marrow cells allows them to populate diverse tissues such as the encephalon, and give rise to a variety of cell types. This unique plasticity makes bone marrow-derived cells good candidates for cell therapy aiming at restoring impaired brain circuits. In the present study, bone marrow cells were transplanted into P20 mice that exhibit selective olfactory degeneration in adulthood between P60 and P150. These animals, the so-called Purkinje Cell Degeneration (PCD) mutant mice, suffer from a progressive and specific loss of a subpopulation of principal neurons of the olfactory bulb, the mitral cells (MCs), sparing the other principal neurons, the tufted cells. As such, PCD mice constitute an interesting model to evaluate the specific role of MCs in olfaction and to test the restorative function of transplanted bone marrow-derived cells. Using precision olfactometry, we revealed that mutant mice lacking MCs exhibited a deficit in odorant detection and discrimination. Remarkably, the transplantation of wild-type bone marrow-derived cells into irradiated PCD mutant mice generated a large population of microglial cells in the olfactory bulb and reduced the degenerative process. The alleviation of MC loss in transplanted mice was accompanied by functional recovery witnessed by significantly improved olfactory detection and enhanced odor discrimination. Together, these data suggest that: (1) bone marrow-derived cells represent an effective neuroprotective tool to restore degenerative brain circuits, and (2) MCs are necessary to encode odor concentration and odor identity in the mouse olfactory bulb.

Bitter taste stimuli induce differential neural codes in mouse brain

A growing literature suggests taste stimuli commonly classified as “bitter” induce heterogeneous neural and perceptual responses. Here, the central processing of bitter stimuli was studied in mice with genetically controlled bitter taste profiles. Using these mice removed genetic heterogeneity as a factor influencing gustatory neural codes for bitter stimuli. Electrophysiological activity (spikes) was recorded from single neurons in the nucleus tractus solitarius during oral delivery of taste solutions (26 total), including concentration series of the bitter tastants quinine, denatonium benzoate, cycloheximide, and sucrose octaacetate (SOA), presented to the whole mouth for 5 s. Seventy-nine neurons were sampled; in many cases multiple cells (2 to 5) were recorded from a mouse. Results showed bitter stimuli induced variable gustatory activity. For example, although some neurons responded robustly to quinine and cycloheximide, others displayed concentration-dependent activity (p<0.05) to quinine but not cycloheximide. Differential activity to bitter stimuli was observed across multiple neurons recorded from one animal in several mice. Across all cells, quinine and denatonium induced correlated spatial responses that differed (p<0.05) from those to cycloheximide and SOA. Modeling spatiotemporal neural ensemble activity revealed responses to quinine/denatonium and cycloheximide/SOA diverged during only an early, at least 1 s wide period of the taste response. Our findings highlight how temporal features of sensory processing contribute differences among bitter taste codes and build on data suggesting heterogeneity among “bitter” stimuli, data that challenge a strict monoguesia model for the bitter quality.

Sodium concentration coding gives way to evaluative coding in cortex and amygdala

Typically, stimulus batteries used to characterize sensory neural coding span physical parameter spaces (e.g., concentration: from low to high). For awake animals, however, psychological variables (e.g., pleasantness/palatability) with complicated relationships to the physical often dominate neural responses. Here we pit physical and psychological axes against one another, presenting awake rats with a stimulus set including 4 NaCl concentrations (0.01, 0.1, 0.3, and 1.0 m) plus palatable (0.3 m sucrose) and aversive (0.001 m quinine) benchmarks, while recording the activity of neurons in two sites vital for NaCl taste processing, gustatory cortex (GC) and central amygdala (CeA). Since NaCl palatability (i.e., preference) follows a non-monotonic, "inverted-U-shaped" curve while concentration increases monotonically, this stimulus battery allowed us to test whether GC and CeA responses better reflect external or internal variables. As predicted, GC single-neuron and population responses reflected both parameters in separate response epochs: sodium concentration-related information appeared with the earliest taste-specific responses, giving way to palatability-related information, in an overlapping subset of neurons, several hundred milliseconds later. CeA single-neuron and population responses, meanwhile, contained only a brief period of concentration specificity, occurring just before palatability-related information emerged (simultaneously with, or slightly later than, in GC). Thus, cortex and amygdala both prominently reflect NaCl palatability late in their responses; CeA neurons largely respond to either palatable or aversive stimuli, while GC responses tend to reflect the entire palatability spectrum in a graded fashion.

Sodium concentration coding gives way to evaluative coding in cortex and amygdala

Typically, stimulus batteries used to characterize sensory neural coding span physical parameter spaces (e.g., concentration: from low to high). For awake animals, however, psychological variables (e.g., pleasantness/palatability) with complicated relationships to the physical often dominate neural responses. Here we pit physical and psychological axes against one another, presenting awake rats with a stimulus set including 4 NaCl concentrations (0.01, 0.1, 0.3, and 1.0 m) plus palatable (0.3 m sucrose) and aversive (0.001 m quinine) benchmarks, while recording the activity of neurons in two sites vital for NaCl taste processing, gustatory cortex (GC) and central amygdala (CeA). Since NaCl palatability (i.e., preference) follows a non-monotonic, "inverted-U-shaped" curve while concentration increases monotonically, this stimulus battery allowed us to test whether GC and CeA responses better reflect external or internal variables. As predicted, GC single-neuron and population responses reflected both parameters in separate response epochs: sodium concentration-related information appeared with the earliest taste-specific responses, giving way to palatability-related information, in an overlapping subset of neurons, several hundred milliseconds later. CeA single-neuron and population responses, meanwhile, contained only a brief period of concentration specificity, occurring just before palatability-related information emerged (simultaneously with, or slightly later than, in GC). Thus, cortex and amygdala both prominently reflect NaCl palatability late in their responses; CeA neurons largely respond to either palatable or aversive stimuli, while GC responses tend to reflect the entire palatability spectrum in a graded fashion.

Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization

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

Heterogeneity in dendritic morphology of moth antennal lobe projection neurons

A population of projection neurons (PNs) in the antennal lobe (AL) integrates sensory information from the antenna and is essential for processing odor information in the insect brain. We examined the anatomy of this neuronal population in the brain of the silkmoth Bombyx mori. Using intracellular dye injection, we labeled a total of 246 PNs and systematically analyzed their morphological features, including the soma position, antennocerebral tract, and number of innervating glomeruli. For example, we analyzed PNs that had somata in the different cell clusters, innervated overlapping but different groups of glomeruli, and ran through different pathways. We also identified glomeruli innervated by PNs using a previously established procedure that first classifies glomeruli into regional groups and then identifies individual glomeruli. We analyzed uniglomerular PNs (75.6% of the total) and found heterogeneity in the dendritic morphology of the PNs that was dependent on the regions and/or the innervating glomeruli. For example, most PNs innervating the macroglomerular complex did not have extraglomerular processes, whereas most PNs innervating ordinary glomeruli did. Moreover, PNs innervating the toroid glomerulus showed heterogeneity in their dendritic morphology. These PNs had dendritic arborization in different areas within the glomerulus. We found that, in some cases, the innervation pattern of the PN dendrite correlated with individual variation in the glomerular organization. These results indicate that PNs are not homogeneous populations, and in some cases morphological heterogeneity in PNs correlated with change in glomerular organization in the silkmoth AL.

Differential coding by two olfactory subsystems in the honeybee brain

Sensory systems use parallel processing to extract and process different features of environmental stimuli. Parallel processing has been studied in the auditory, visual, and somatosensory systems, but equivalent research in the olfactory modality is scarce. The honeybee Apis mellifera is an interesting model for such research as its relatively simple brain contains a dual olfactory system, with a clear neural dichotomy from the periphery to higher-order centers, based on two main neuronal tracts [medial (m) and lateral (l) antenno-protocerebral tract (APT)]. The function of this dual system is as yet unknown, and attributes like odor quality and odor quantity might be separately encoded in these subsystems. We have thus studied olfactory coding at the input of both subsystems, using in vivo calcium imaging. As one of the subsystems (m-APT) has never been imaged before, a novel imaging preparation was developed to this end, and responses to a panel of aliphatic odorants at different concentrations were compared in both subsystems. Our data show a global redundancy of olfactory coding at the input of both subsystems but unravel some specificities for encoding chemical group and carbon chain length of odor molecules.

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

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

Spontaneous olfactory receptor neuron activity determines follower cell response properties

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

Feedforward inhibition and synaptic scaling--two sides of the same coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

The inputs a neuron receives from its presynaptic partners strongly fluctuate as a result of either varying sensory information or ongoing intrinsic activity. To represent this wide range of signals effectively, neurons use various mechanisms that regulate the total input they receive. On the one hand, feedforward inhibition adjusts the relative contribution of individual inputs inversely proportional to the total number of active afferents, implementing a form of input normalization. On the other hand, synaptic scaling uniformly rescales the efficacy of incoming synapses to stabilize the neuron's firing rate after learning-induced changes in drive. Given that these mechanisms often act on the same neurons, we ask here if there are any benefits in combining the two. We show that the interaction between the two has important computational consequences, beyond their traditional role in maintaining network homeostasis. When combined with lateral inhibition, synaptic scaling and fast feedforward inhibition allow the circuit to learn efficiently from noisy, ambiguous inputs. For inputs not normalized by feed-forward inhibition, learning is less efficient. Given that feed-forward inhibition and synaptic scaling have been reported in various systems, our results suggest that they could generally facilitate learning in neural circuits. More broadly, our work emphasizes the importance of studying the interaction between different plasticity mechanisms for understanding circuit function.

Distributed representation of chemical features and tunotopic organization of glomeruli in the mouse olfactory bulb

In the mammalian brain, similar features of the sensory stimuli are often represented in proximity in the sensory areas. However, how chemical features are represented in the olfactory bulb has been controversial. Questions have been raised as to whether specific chemical features of the odor molecules are represented by spatially clustered olfactory glomeruli. Using a sensitive probe, we have analyzed the glomerular response to large numbers of odorants at single glomerulus resolution. Contrary to the general view, we find that the representation of chemical features is spatially distributed in the olfactory bulb with no discernible chemotopy. Moreover, odor-evoked pattern of activity does not correlate directly with odor structure in general. Despite the lack of spatial clustering or preference with respect to chemical features, some structurally related odors can be similarly represented by ensembles of spatially distributed glomeruli, providing an explanation of their perceptual similarity. Whereas there is no chemotopic organization, and the glomeruli are tuned to odors from multiple classes, we find that the glomeruli are hierarchically arranged into clusters according to their odor-tuning similarity. This tunotopic arrangement provides a framework to understand the spatial organization of the glomeruli that conforms to the organizational principle found in other sensory systems.

Peripheral and central olfactory tuning in a moth

Animals can be innately attracted to certain odorants. Because these attractants are particularly salient, they might be expected to induce relatively strong responses throughout the olfactory pathway, helping animals detect the most relevant odors but limiting flexibility to respond to other odors. Alternatively, specific neural wiring might link innately preferred odors to appropriate behaviors without a need for intensity biases. How nonpheromonal attractants are processed by the general olfactory system remains largely unknown. In the moth Manduca sexta, we studied this with a set of innately preferred host plant odors and other, neutral odors. Electroantennogram recordings showed that, as a population, olfactory receptor neurons (ORNs) did not respond with greater intensity to host plant odors, and further local field potential recordings showed that no specific amplification of signals induced by host plant odors occurred between the first olfactory center and the second. Moreover, when odorants were mutually diluted to elicit equally intense output from the ORNs, moths were able to learn to associate all tested odorants equally well with food reward. Together, these results suggest that, although nonpheromonal host plant odors activate broadly distributed responses, they may be linked to attractive behaviors mainly through specific wiring in the brain.

Olfactory sensor processing in neural networks: lessons from modeling the fruit fly antennal lobe

The insect olfactory system can be a model for artificial olfactory devices. In particular, Drosophila melanogaster due to its genetic tractability has yielded much information about the design and function of such systems in biology. In this study we investigate possible network topologies to separate representations of odors in the primary olfactory neuropil, the antennal lobe. In particular we compare networks based on stochastic and homogeneous connection weight distributions to connectivities that are based on the input correlations between the glomeruli in the antennal lobe. We show that moderate homogeneous inhibition implements a soft winner-take-all mechanism when paired with realistic input from a large meta-database of odor responses in receptor cells (DoOR database). The sparseness of representations increases with stronger inhibition. Excitation, on the other hand, pushes the representation of odors closer together thus making them harder to distinguish. We further analyze the relationship between different inhibitory network topologies and the properties of the receptor responses to different odors. We show that realistic input from the DoOR database has a relatively high entropy of activation values over all odors and receptors compared to the theoretical maximum. Furthermore, under conditions in which the information in the input is artificially decreased, networks with heterogeneous topologies based on the similarity of glomerular response profiles perform best. These results indicate that in order to arrive at the most beneficial representation for odor discrimination it is important to finely tune the strength of inhibition in combination with taking into account the properties of the available sensors.

Encoding odorant identity by spiking packets of rate-invariant neurons in awake mice

BACKGROUND

How do neural networks encode sensory information? Following sensory stimulation, neural coding is commonly assumed to be based on neurons changing their firing rate. In contrast, both theoretical works and experiments in several sensory systems showed that neurons could encode information as coordinated cell assemblies by adjusting their spike timing and without changing their firing rate. Nevertheless, in the olfactory system, there is little experimental evidence supporting such model.

METHODOLOGY/PRINCIPAL FINDINGS

To study these issues, we implanted tetrodes in the olfactory bulb of awake mice to record the odorant-evoked activity of mitral/tufted (M/T) cells. We showed that following odorant presentation, most M/T neurons do not significantly change their firing rate over a breathing cycle but rather respond to odorant stimulation by redistributing their firing activity within respiratory cycles. In addition, we showed that sensory information can be encoded by cell assemblies composed of such neurons, thus supporting the idea that coordinated populations of globally rate-invariant neurons could be efficiently used to convey information about the odorant identity. We showed that different coding schemes can convey high amount of odorant information for specific read-out time window. Finally we showed that the optimal readout time window corresponds to the duration of gamma oscillations cycles.

CONCLUSION

We propose that odorant can be encoded by population of cells that exhibit fine temporal tuning of spiking activity while displaying weak or no firing rate change. These cell assemblies may transfer sensory information in spiking packets sequence using the gamma oscillations as a clock. This would allow the system to reach a tradeoff between rapid and accurate odorant discrimination.

Stimulus and network dynamics collide in a ratiometric model of the antennal lobe macroglomerular complex

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.

Sequential mechanisms underlying concentration invariance in biological olfaction

Concentration invariance-the capacity to recognize a given odorant (analyte) across a range of concentrations-is an unusually difficult problem in the olfactory modality. Nevertheless, humans and other animals are able to recognize known odors across substantial concentration ranges, and this concentration invariance is a highly desirable property for artificial systems as well. Several properties of olfactory systems have been proposed to contribute to concentration invariance, but none of these alone can plausibly achieve full concentration invariance. We here propose that the mammalian olfactory system uses at least six computational mechanisms in series to reduce the concentration-dependent variance in odor representations to a level at which different concentrations of odors evoke reasonably similar representations, while preserving variance arising from differences in odor quality. We suggest that the residual variance then is treated like any other source of stimulus variance, and categorized appropriately into "odors" via perceptual learning. We further show that naïve mice respond to different concentrations of an odorant just as if they were differences in quality, suggesting that, prior to odor categorization, the learning-independent compensatory mechanisms are limited in their capacity to achieve concentration invariance.

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

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

Dopaminergic modulation of the striatal microcircuit: receptor-specific configuration of cell assemblies

Selection and inhibition of motor behaviors are related to the coordinated activity and compositional capabilities of striatal cell assemblies. Striatal network activity represents a main step in basal ganglia processing. The dopaminergic system differentially regulates distinct populations of striatal medium spiny neurons (MSNs) through the activation of D(1)- or D(2)-type receptors. Although postsynaptic and presynaptic actions of these receptors are clearly different in MSNs during cell-focused studies, their activation during network activity has shown inconsistent responses. Therefore, using electrophysiological techniques, functional multicell calcium imaging, and neuronal population analysis in rat corticostriatal slices, we describe the effect of selective dopaminergic receptor activation in the striatal network by observing cell assembly configurations. At the microcircuit level, during striatal network activity, the selective activation of either D(1)- or D(2)-type receptors is reflected as overall increases in neuronal synchronization. However, graph theory techniques applied to the transitions between network states revealed receptor-specific configurations of striatal cell assemblies: D(1) receptor activation generated closed trajectories with high recurrence and few alternate routes favoring the selection of specific sequences, whereas D(2) receptor activation created trajectories with low recurrence and more alternate pathways while promoting diverse transitions among neuronal pools. At the single-cell level, the activation of dopaminergic receptors enhanced the negative-slope conductance region (NSCR) in D(1)-type-responsive cells, whereas in neurons expressing D(2)-type receptors, the NSCR was decreased. Consequently, receptor-specific network dynamics most probably result from the interplay of postsynaptic and presynaptic dopaminergic actions.

Behavioral and neurophysiological study of olfactory perception and learning in honeybees

The honeybee Apis mellifera has been a central insect model in the study of olfactory perception and learning for more than a century, starting with pioneer work by Karl von Frisch. Research on olfaction in honeybees has greatly benefited from the advent of a range of behavioral and neurophysiological paradigms in the Lab. Here I review major findings about how the honeybee brain detects, processes, and learns odors, based on behavioral, neuroanatomical, and neurophysiological approaches. I first address the behavioral study of olfactory learning, from experiments on free-flying workers visiting artificial flowers to laboratory-based conditioning protocols on restrained individuals. I explain how the study of olfactory learning has allowed understanding the discrimination and generalization ability of the honeybee olfactory system, its capacity to grant special properties to olfactory mixtures as well as to retain individual component information. Next, based on the impressive amount of anatomical and immunochemical studies of the bee brain, I detail our knowledge of olfactory pathways. I then show how functional recordings of odor-evoked activity in the brain allow following the transformation of the olfactory message from the periphery until higher-order central structures. Data from extra- and intracellular electrophysiological approaches as well as from the most recent optical imaging developments are described. Lastly, I discuss results addressing how odor representation changes as a result of experience. This impressive ensemble of behavioral, neuroanatomical, and neurophysiological data available in the bee make it an attractive model for future research aiming to understand olfactory perception and learning in an integrative fashion.

Relating the sequential dynamics of excitatory neural networks to synaptic cellular automata

We have developed a new approach for the description of sequential dynamics of excitatory neural networks. Our approach is based on the dynamics of synapses possessing the short-term plasticity property. We suggest a model of such synapses in the form of a second-order system of nonlinear ODEs. In the framework of the model two types of responses are realized-the fast and the slow ones. Under some relations between their timescales a cellular automaton (CA) on the graph of connections is constructed. Such a CA has only a finite number of attractors and all of them are periodic orbits. The attractors of the CA determine the regimes of sequential dynamics of the original neural network, i.e., itineraries along the network and the times of successive firing of neurons in the form of bunches of spikes. We illustrate our approach on the example of a Morris-Lecar neural network.

Elemental and configural olfactory coding by antennal lobe neurons of the honeybee (Apis mellifera)

When smelling an odorant mixture, olfactory systems can be analytical (i.e. extract information about the mixture elements) or synthetic (i.e. creating a configural percept of the mixture). Here, we studied elemental and configural mixture coding in olfactory neurons of the honeybee antennal lobe, local neurons in particular. We conducted intracellular recordings and stimulated with monomolecular odorants and their coherent or incoherent binary mixtures to reproduce a temporally dynamic environment. We found that about half of the neurons responded as ‘elemental neurons’, i.e. responses evoked by mixtures reflected the underlying feature information from one of the components. The other half responded as ‘configural neurons’, i.e. responses to mixtures were clearly different from responses to their single components. Elemental neurons divided in late responders (above 60 ms) and early responder neurons (below 60 ms), whereas responses of configural coding neurons concentrated in-between these divisions. Latencies of neurons with configural responses express a tendency to be faster for coherent stimuli which implies employment in different processing circuits.

Neuronal filtering of multiplexed odour representations

Neuronal activity patterns contain information in their temporal structure, indicating that information transfer between neurons may be optimized by temporal filtering. In the zebrafish olfactory bulb, subsets of output neurons (mitral cells) engage in synchronized oscillations during odour responses, but information about odour identity is contained mostly in non-oscillatory firing rate patterns. Using optogenetic manipulations and odour stimulation, we found that firing rate responses of neurons in the posterior zone of the dorsal telencephalon (Dp), a target area homologous to olfactory cortex, were largely insensitive to oscillatory synchrony of mitral cells because passive membrane properties and synaptic currents act as low-pass filters. Nevertheless, synchrony influenced spike timing. Moreover, Dp neurons responded primarily during the decorrelated steady state of mitral cell activity patterns. Temporal filtering therefore tunes Dp neurons to components of mitral cell activity patterns that are particularly informative about precise odour identity. These results demonstrate how temporal filtering can extract specific information from multiplexed neuronal codes.

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

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