Olfactory Neural System Research Articles

Neural mechanism mimetic selective electronic nose based on programmed M13 bacteriophage

The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).

Research on an olfactory neural system model and its applications based on deep learning

The idea of constructing the biological neural system model as realistic as possible can not only provide a new artificial neural network (ANN), but also offer an effective object to study biological neural systems. As a very meaningful attempt about the idea, a bionic model of olfactory neural system, KIII model, is introduced in this paper. There are the unique characteristics of KIII model different from those of general ANNs. The KIII model realistically simulates the structure of the real olfactory neural system and the process of odor molecules gradually transformed by the core components of the olfactory system including olfactory receptor, olfactory bulb and olfactory cortex. The neuron model of the KIII model is constructed and optimized based on neurophysiological experimental data and accurately reflects the response of olfactory neurons to odor stimulation. In particular, the noise introduced to KIII model can further improve the performance of the model. In addition, the KIII model is analyzed based on the idea of deep learning. The qualitative analysis shows that there are obvious similarities between the KIII model and the deep learning model. Furthermore, with the epileptic electroencephalograph (EEG) recognition task, two groups of experiments are designed to comprehensively analyze the performance of the KIII model. In the first group of experiments, a typical pattern recognition experiment with feature extraction stage is shown. The features of epileptic EEG were extracted based on Empirical Mode Decomposition (EMD), and the KIII model was used as a classifier. The experimental results show that the KIII model only needs a small number of iterations to memorize different modes and has a high recognition rate, over 91%. In the second group of experiments, a direct recognition experiment without feature extraction stage is shown. The original epileptic EEG signals as KIII model inputs directly were recognized. The experimental results show that there is still an excellent performance in the KIII model, over 96%, and the recognition result is similar to the characteristics of the deep learning model. The theoretical analysis and experimental results prove that KIII model with the idea of deep learning is an excellent bionic model of olfactory neural system and gets a good balance between high bionics and good performance, which is a good reference for related research.

Transcriptome analysis of genes involved in the response of a pollinator fig wasp to volatile organic compounds from its host figs

The mutualism of figs and their pollinating fig wasps is widely regarded as a model for coevolved mutualism. A high degree of host specificity is ensured by female wasps only being attracted by their specific fig tree species through the volatile organic compounds (VOCs) released by the figs when they are ready to be pollinated. However, very little is known about the molecular mechanisms underlying the production of VOCs and how pollinators respond to these VOCs. Here we present transcriptome sequencing data from VOC-treated fig wasps and control fig wasps. Using Illumina paired-end sequencing, approximately 6.47 Gbp and 6.48 Gbp high quality reads were generated for fig wasps that had been exposed or not to VOCs of their host fig. After read trimming, the de novo assembly of both types of reads produced 58,192 unigenes with an average length of 817 bp. Then functional annotation and GO enrichment analysis was performed by aligning all-unigenes with public protein databases including NR, SwissProt, and KEGG. Differentially expressed genes (DEGs) were investigated using the RPKM method. Overall, 16 up-regulated genes and 13 down-regulated genes were identified. We further performed GO enrichment and metabolic pathway enrichment analyses. One gene involved in the synoptic vesicle cycle and two genes coding for odorant binding proteins (OBP) are likely to have potential impacts on the response of fig wasps to the VOCs emitted by their host figs. This is the first transcriptome sequencing of a fig wasp in the presence of VOCs of its host figs using the next-generation sequencing technology. Our studies suggest that the expression of some genes in the olfactory neural system of the fig wasps is affected by the VOCs released from the figs. This suggests the presence of a dynamic molecular system of detection and hence response to host plant VOCs. As such our findings provide indications for further mechanistic studies on the fig-fig wasp interactions.

A wearable wireless system for olfactory neural recording in freely moving rats based on Wi-Fi technology

A battery-operated wearable wireless olfactory neural recording system (WONRS) based on Wi-Fi technology is presented and characterized for recording individual olfactory neural activities from freely moving rats. The WONRS that constructed of an amplifier headstage, backpack board and user interface software is capable of transmitting up to 54M bits per second (Mbps) of raw streaming data using IEEE 802.11g protocol, and can examine and store 8 channels of captured olfactory neuronal firing data synchronously. Since the packets are transmitted over the wireless local area network (WLAN), the WONRS provides a capability of being operated over a very large distance, which enables experimenters to be able to operate the device within almost any laboratory or open field context. A major advantage of the WONRS is the high bandwidth and long transmission range afforded by Wi-Fi technology. Using the WONRS, continuous olfactory neural recordings from an awake and behaving rat were performed. Also, WONRS measured the responses of M/T cells under carvone as odorant stimulation over multiple days. This biosensing system could continuously record the response signal of olfactory neuron and, furthermore, had good stability and long working life.

Combinatorial protein recognition as an alternative approach to antibody-mimetics

Current approaches to medical diagnostics and drug design are largely based on the ability of monoclonal antibodies or synthetic molecules to bind proteins with high affinity and selectivity. In recent years, however, an alternative approach to protein recognition has emerged, in which proteins are identified using non-specific receptor arrays that are inspired by the olfactory neural system. An ultimate challenge for such systems is realizing a single, high-throughput analytical device that can effectively diagnose a range of medicinally relevant proteins. Such devices might overcome the difficulties associated with designing potent synthetic receptors for proteins and hence, could open up new possibilities in medical diagnostics, pathogen detection, and proteomics. Here we summarize recent developments in this area and also highlight its limitations and the challenges that this exciting interdisciplinary field faces. In particular, the goal of this review is to underscore the basic parameters required for obtaining combinatorial sensors for proteins and more importantly, to elucidate the rational methodologies that can be applied for systematically improving these promising analytical devices.

Two morphological sub-systems within the olfactory organs of a terrestrial snail

In the present work, we have re-visited the problem of the olfactory neural system organization in the terrestrial snail. By staining the tentacle's nerves and their intrinsic tracts in different points of the cerebral ganglia–tentacles system we have found that the relatively small part of the primary sensory neurons from the sensory pad (7–8%) send their axons directly to the cerebral ganglia. The axons terminated in the metacerebral neuropil which suggests these receptors being not chemosensory but rather mechanosensory neurons. Majority of the primary sensory neurons are synaptically switching in the areas outside the cerebral ganglia, i.e. digits, glomeruli, tentacular ganglion. No primary sensory neurons of the olfactory pad were projecting directly to the procerebrum — the putative centre of olfactory information processing. The afferent tract innervating the procerebrum neuropil originated from the interneurons located in the tentacle ganglion and its digits. Our results suggest the presence of two different sub-systems within the snail nose – mechanosensory and chemosensory – with two different projection targets.

Phylogenetic comparison of serotonin-immunoreactive neurons in the insect olfactory neural systems

Phylogenetic comparison of serotonin-immunoreactive neurons in the insect olfactory neural systems

A STUDY ON A BIONIC PATTERN CLASSIFIER BASED ON OLFACTORY NEURAL SYSTEM

This paper presents a simulation of a biological olfactory neural system with a KIII set, which is a high-dimensional chaotic neural network. The KIII set differs from conventional artificial neural networks by use of chaotic attractors for memory locations that are accessed by, chaotic trajectories. It was designed to simulate the patterns of action potentials and EEG waveforms observed in electrophysiological experiments, and has proved its utility as a model for biological intelligence in pattern classification. An application to recognition of handwritten numerals is presented here, in which the classification performance of the KIII network under different noise levels was investigated.

Smaller Nasal Volumes as Stigmata of Aberrant Neurodevelopment in Schizophrenia

Anatomical and functional deficits of the olfactory neural system have been identified in patients with schizophrenia. Since olfactory structures develop in conjunction with both the palate and ventral forebrain, the authors hypothesized that schizophrenia patients might have structural abnormalities of the nasal cavity, which could represent specific markers of embryological dysmorphogenesis underlying schizophrenia. A measurement of nasal volume was acquired by acoustic rhinometry for 40 male schizophrenia patients and 24 healthy male comparison subjects. The patients had smaller posterior nasal volumes than the comparison subjects but did not differ in anterior nasal volumes. This difference persisted after covarying for height and smoking history. The lower observed posterior nasal volume likely reflects a specific developmental craniofacial abnormality. This finding confirms an early disruption in embryological development in males with schizophrenia and may represent a genetic or environmental "first hit" that leaves the individual vulnerable to subsequent pathology.

Study of a bionic pattern classifier based on olfactory neural system

Simulating biological olfactory neural system, K III network, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the K III network works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting numerals are presented here. The classification performance of the K III network at different noise levels was also investigated.

Study of a bionic pattern classifier based on olfactory neural system

Simulating biological olfactory neural system, K III network, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the K III network works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting numerals are presented here. The classification performance of the K III network at different noise levels was also investigated.

An olfactory recognition model based on spatio-temporal encoding of odor quality in the olfactory bulb.

In order to study the problem how the olfactory neural system processes the odorant molecular information for constructing the olfactory image of each object, we present a dynamic model of the olfactory bulb constructed on the basis of well-established experimental and theoretical results. The information relevant to a single odor, i.e. its constituent odorant molecules and their mixing ratios, are encoded into a spatio-temporal pattern of neural activity in the olfactory bulb, where the activity pattern corresponds to a limit cycle attractor in the mitral cell network. The spatio-temporal pattern consists of a temporal sequence of spatial firing patterns: each constituent molecule is encoded into a single spatial pattern, and the order of magnitude of the mixing ratio is encoded into the temporal sequence. The formation of a limit cycle attractor under the application of a novel odor is carried out based on the intensity-to-time-delay encoding scheme. The dynamic state of the olfactory bulb, which has learned many odors, becomes a randomly itinerant state in which the current firing state of the bulb itinerates randomly among limit cycle attractors corresponding to the learned odors. The recognition of an odor is generated by the dynamic transition in the network from the randomly itinerant state to a limit cycle attractor state relevant to the odor, where the transition is induced by the short-term synaptic changes made according to the Hebbian rule under the application of the odor stimulus.

Biologically Modeled Noise Stabilizing Neurodynamics for Pattern Recognition

We present a distributed KIII model for the olfactory neural system. Low-level Gaussian noise is introduced to the receptors and anterior olfactory nucleus, which biologically models the peripheral and central sources of noise. The additive noise numerically makes the model stable and robust in respect to repeated input-induced state transitions, while improving the simulations of EEG potentials and multiunit activity from the olfactory system. This hybrid dynamics generates a 1/f aperiodic state, which provides an unpatterned basal state for every module to stay in while there is no significant stimulus. Any external input may guide the system to a certain patterned state. The mechanism is fast, fully parallel, under modulatory control, and flexible in absorbing new patterns from unpredictable environments.

Retrograde transport of nerve growth factor from olfactory bulb to olfactory epithelium.

NGF is produced in the olfactory bulb and transported from the bulb to cholinergic neurons in the horizontal limb of the diagonal band. Although the expression of NGF receptors has been reported in olfactory epithelial cells, no correlation between NGF and olfactory epithelial cells has been found. This study aimed to define whether or not retrograde transport of NGF occurs in the olfactory neural system using the method of radioluminography. 125I-labeled NGF injected into the olfactory bulb was taken up and transported to the olfactory epithelium 18 h after injection. This finding suggests that bulbar NGF may act as a neurotrophic factor in olfactory epithelial cells.

Parameter optimization in models of the olfactory neural system

We model a mammalian neural olfactory system that is characterized anatomically by modifiable synaptic connections, and physiologically by the spatio-temporal interactions among neural ensembles under external inputs. The behavior of each ensemble is represented by a second order ordinary differential equation (ODE) relating the aggregate activation of cells to system parameters and stimuli from anloutside environment. Parameter optimization rules that lead to minimizing the distance between the computed output patterns of the model and the evoked unit and EEG potentials from experimental data are derived by the methods of error propagation and the calculus of variations. In addition, the existence and uniqueness of the equation set are proved to assure the propriety of the parameter adaptation by these methods. Several subsets (KO, KI, KII) of the olfactory neural (KIII) model are simulated in software. Numerical results support the mathematical analyses, and the system parameters are thus optimized. Our experience with realistic models of neural systems has shown that hundreds of equations and thousands of parameters are required, and the performance of the models may be sensitive to minor software changes, such as adapting them to new machines. We propose that a set of reliable parameter optimization algorithms, including specification of performance criteria, should be an integral component of a realistic neural model.

Mechanisms of insect odor-seaching behavior. Olfactory processing in an insect brain and behavior.

In order to mate, male moths must find a female by flying or walking upwind, tracing a stream of dilute pheromones in the air. Recent research attempting to understand how information about the pheromones is processed in the olfactory neural system of the brain and how this information participates in generating and controlling orientation behavior, provides useful insight. The present review concerns with overview of odor orientation behavior and olfactory processing pathways of an insect brain, focusing on state-dependent of flipflopping neural sig- nals to repetitive pheromonal stimulation, which are important in controlling the behavior.