Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons

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

Coding and learning of chemosensor array patterns in a neurodynamic model of the olfactory system

Arrays of broadly-selective chemical sensors, also known as electronic noses, have been developed during the past two decades as a low-cost and high-throughput alternative to analytical instruments for the measurement of odorant chemicals. Signal processing in these gas-sensor arrays has been traditionally performed by means of statistical and neural pattern recognition techniques. The objective of this dissertation is to develop new computational models to process gas sensor array signals inspired by coding and learning mechanisms of the biological olfactory system. We have used a neurodynamic model of the olfactory system, the KIII, to develop and demonstrate four odor processing computational functions: robust recovery of overlapping patterns, contrast enhancement, background suppression, and novelty detection. First, a coding mechanism based on the synchrony of neural oscillations is used to extract information from the associative memory of the KIII model. This temporal code allows the KIII to recall overlapping patterns in a robust manner. Second, a new learning rule that combines Hebbian and anti-Hebbian terms is proposed. This learning rule is shown to achieve contrast enhancement on gas-sensor array patterns. Third, a new local learning mechanism based on habituation is proposed to perform odor background suppression. Combining the Hebbian/anti-Hebbian rule and the local habituation mechanism, the KIII is able to suppress the response to continuously presented odors, facilitating the detection of the new ones. Finally, a new learning mechanism based on anti-Hebbian learning is proposed to perform novelty detection. This learning mechanism allows the KIII to detect the introduction of new odors even in the presence of strong backgrounds. The four computational models are characterized with synthetic data and validated on gas sensor array patterns obtained from an e-nose prototype developed for this purpose

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

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.Comment: 26 pages, 24 figure