Olfactory object recognition, segmentation, adaptation, target seeking, and discrimination by the network of the olfactory bulb and cortex: computational model and experimental data

Mammals are poor at individuating the separate components that comprise odor mixtures, but not when components enter environment serially and when there is top-down expectation. Li proposed in 1990 an odor segmentation mechanism using the centrifugal feedback from the olfactory cortex to the olfactory bulb. This feedback suppresses the bulbar responses to the ongoing and already recognized odors so that a subsequent addition of a foreground odor can be singled out for recognition. Additionally, the feedback can depend on context so as to, for example, enhance sensitivity to a target odor or improve discrimination between similar odors. I review experimental data that have since emerged in relation to the computational predictions and implications, and suggest experiments to test the model further

An Olfactory Cocktail Party: Figure-Ground Segregation of Odorants in Rodents

In odorant-rich environments, animals must be able to detect specific odorants of interest against variable backgrounds. However, studies have found that both humans and rodents are poor at analyzing the components of odorant mixtures, suggesting that olfaction is a synthetic sense in which mixtures are perceived holistically. We found that mice could be easily trained to detect target odorants embedded in unpredictable and variable mixtures. To relate the behavioral performance to neural representation, we imaged the responses of olfactory bulb glomeruli to individual odors in mice expressing the $Ca^{2+}$ indicator GCaMP3 in olfactory receptor neurons. The difficulty of segregating the target from the background depended strongly on the extent of overlap between the glomerular responses to target and background odors. Our study indicates that the olfactory system has powerful analytic abilities that are constrained by the limits of combinatorial neural representation of odorants at the level of the olfactory receptors.Molecular and Cellular Biolog

Activity-Induced Remodeling of Olfactory Bulb Microcircuits Revealed by Monosynaptic Tracing

The continued addition of new neurons to mature olfactory circuits represents a remarkable mode of cellular and structural brain plasticity. However, the anatomical configuration of newly established circuits, the types and numbers of neurons that form new synaptic connections, and the effect of sensory experience on synaptic connectivity in the olfactory bulb remain poorly understood. Using in vivo electroporation and monosynaptic tracing, we show that postnatal-born granule cells form synaptic connections with centrifugal inputs and mitral/tufted cells in the mouse olfactory bulb. In addition, newly born granule cells receive extensive input from local inhibitory short axon cells, a poorly understood cell population. The connectivity of short axon cells shows clustered organization, and their synaptic input onto newborn granule cells dramatically and selectively expands with odor stimulation. Our findings suggest that sensory experience promotes the synaptic integration of new neurons into cell type-specific olfactory circuits

Sensor-based machine olfaction with neuromorphic models of the olfactory system

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings

Mechanisms and Function of Neural Synchronization in an Insect Olfactory System

One of the fundamental questions in modem integrative neurobiology relates to the encoding of sensory information by populations of neurons, and to the significance of this activity for perception, learning, memory and behavior. Synchronization of activity across a population of neurons has been observed many times over, but has never been demonstrated to be a necessary component of this coding process. Neural synchronization has been found in many brain areas in animals across several phyla, from molluscs to mammals. Studies in mammals have correlated the degree of neural synchronization with specific behavioral or cognitive states, such as sensorimotor tasks, segmentation and binocular rivalry suggesting a functional link. In the locust olfactory system, oscillatory synchronization is a prominent feature of the odor-evoked neural activity. Stimulation of the antenna by odors evokes synchronized firing in dynamic and odor-specific ensembles of the projection neurons of the antennal lobe, the principal neurons of the first-order olfactory relay in insects. The coherent activity of these projection neurons underlies an odor-evoked oscillatory field potential which can be recorded in the mushroom body, the second-order olfactory relay to which they project. In this dissertation, we investigated two important questions raised by these findings: how are such stimulus-evoked synchronous ensembles generated, and what is their functional significance? To address these questions, we performed electrophysiological experiments and recorded odor responses from neurons of the antennal lobes and mushroom bodies of locusts, in vivo and using natural odor stimulation in an unanesthetized, semi-intact preparation. We demonstrated the critical mechanism involved in neural synchronization of the antennal lobe neurons. The synchronization of the projection neurons relies critically on fast GABA (γ-aminobutyric acid) -mediated inhibition from the local interneurons. Projection neuron synchronization could be selectively blocked by local injection of the GABA receptor antagonist, picrotoxin. Picrotoxin spared the odor-specific, slow modulation of individual projection neuron responses, but desynchronized the firing of the odor-activated projection neuron assemblies. The oscillatory activity of the local intemeurons was also blocked by picrotoxin, which indicates that such activity depends on network synaptic dynamics. We also showed that the mushroom body networks are capable of generating oscillatory behavior of a similar frequency as that of its projection neuron inputs, and that they may thus be "tuned" to accept synchronized, oscillatory inputs of that frequency range. Our understanding of this mechanism, in tum, made possible the functional investigation of neural synchronization by selective disruption of projection neuron synchronization. We studied a population of neurons downstream from the antennal lobe projection neurons, the extrinsic neurons of the β-lobe of the mushroom body (βLNs). These βLNs were chosen for investigation because they were found to be odor-responsive and because their position in the olfactory pathway makes them a suitable "read-out" of population activity in the antennal lobe. We characterized βLN odor responses before and after selective disruption of the synchronization of the projection neuron ensembles with local picrotoxin injection into the antennal lobe. We showed that the tuning of these βLN responses was altered by PN desynchronization by changing existing responses and inducing new responses. This alteration in tuning resulted in a significant loss of odor specificity in individual βLN responses, an effect that never occurred in the responses of individual, desynchronized projection neurons. We thus propose that neural synchronization is indeed important for information processing in the brain: it serves, at least in part, as a temporal substrate for the transmission of information that is contained across co-activated neurons (relational code) early in the pathway.</p

Data driven approaches for investigating molecular heterogeneity of the brain

It has been proposed that one of the clearest organizing principles for most sensory systems is the existence of parallel subcircuits and processing streams that form orderly and systematic mappings from stimulus space to neurons. Although the spatial heterogeneity of the early olfactory circuitry has long been recognized, we know comparatively little about the circuits that propagate sensory signals downstream. Investigating the potential modularity of the bulb’s intrinsic circuits proves to be a difficult task as termination patterns of converging projections, as with the bulb’s inputs, are not feasibly realized. Thus, if such circuit motifs exist, their detection essentially relies on identifying differential gene expression, or “molecular signatures,” that may demarcate functional subregions. With the arrival of comprehensive (whole genome, cellular resolution) datasets in biology and neuroscience, it is now possible for us to carry out large-scale investigations and make particular use of the densely catalogued, whole genome expression maps of the Allen Brain Atlas to carry out systematic investigations of the molecular topography of the olfactory bulb’s intrinsic circuits. To address the challenges associated with high-throughput and high-dimensional datasets, a deep learning approach will form the backbone of our informatic pipeline. In the proposed work, we test the hypothesis that the bulb’s intrinsic circuits are parceled into distinct, parallel modules that can be defined by genome-wide patterns of expression. In pursuit of this aim, our deep learning framework will facilitate the group-registration of the mitral cell layers of ~ 50,000 in-situ olfactory bulb circuits to test this hypothesis

Internal Cholinergic Regulation of Learning and Recall in a Model of Olfactory Processing

In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system. In the model, cholinergic neurons reduce their firing in response to familiar odors—reducing plasticity in the PC, but increase their firing in response to novel odor—increasing PC plasticity. Recordings from HDB neurons in awake behaving rats reflect predictions from the model by showing that a subset of neurons decrease their firing as an odor becomes familiar