Dynamical Mechanisms of Odor Processing in Olfactory Bulb Mitral Cells

2006 ◽  
Vol 96 (2) ◽  
pp. 555-568 ◽  
Author(s):  
Daniel B. Rubin ◽  
Thomas A. Cleland
Keyword(s):  
Olfactory Bulb ◽  
Large Scale ◽  
Computational Models ◽  
Potassium Conductance ◽  
Membrane Dynamics ◽  
Inhibitory Input ◽  
Mitral Cells ◽  
Hypoxic Conditions ◽  
Subthreshold Oscillations ◽  
Dynamical Properties

In the olfactory system, the contribution of dynamical properties such as neuronal oscillations and spike synchronization to the representation of odor stimuli is a matter of substantial debate. While relatively simple computational models have sufficed to guide current research in large-scale network dynamics, less attention has been paid to modeling the membrane dynamics in bulbar neurons that may be equally essential to sensory processing. We here present a reduced, conductance-based compartmental model of olfactory bulb mitral cells that exhibits the complex dynamical properties observed in these neurons. Specifically, model neurons exhibit intrinsic subthreshold oscillations with voltage-dependent frequencies that shape the timing of stimulus-evoked action potentials. These oscillations rely on a persistent sodium conductance, an inactivating potassium conductance, and a calcium-dependent potassium conductance and are reset via inhibitory input such as that delivered by periglomerular cell shunt inhibition. Mitral cells fire bursts, or clusters, of spikes when continuously stimulated. Burst properties depend critically on multiple currents, but a progressive deinactivation of IA over the course of a burst is an important regulator of burst termination. Each of these complex properties exhibits appropriate dynamics and pharmacology as determined by electrophysiological studies. Additionally, we propose that a second, inconsistently observed form of infrathreshold bistability in mitral cells may derive from the activation of ATP-activated potassium currents responding to hypoxic conditions. We discuss the integration of these cellular properties in the larger context of olfactory bulb network operations.

scholarly journals Connectivity and dynamics in the olfactory bulb

2021 ◽  
Author(s):  
David EC Kersen ◽  
Gaia Tavoni ◽  
Vijay Balasubramanian
Keyword(s):  
Olfactory Bulb ◽  
Lateral Inhibition ◽  
Large Scale ◽  
Computational Models ◽  
Granule Cells ◽  
Cell Activity ◽  
Gamma Oscillations ◽  
Scale Model ◽  
Mitral Cells ◽  
Cortical Feedback

Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the probability of synapse between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. It in turn predicts testable relations between network structure, lateral inhibition, and odor pattern decorrelation; between the density of granule cell activity and LFP oscillation frequency; how cortical feedback to granule cells affects mitral cell activity; and how cortical feedback to mitral cells is modulated by the network embedding. Additionally, the methodology we describe here provides a tractable tool for other researchers.

Inhibition of Backpropagating Action Potentials in Mitral Cell Secondary Dendrites

2002 ◽  
Vol 88 (1) ◽  
pp. 64-85 ◽  
Author(s):  
Graeme Lowe
Keyword(s):  
Olfactory Bulb ◽  
Granule Cell ◽  
Action Potentials ◽  
Flash Photolysis ◽  
Glutamate Release ◽  
Mitral Cell ◽  
Repetitive Firing ◽  
Inhibitory Input ◽  
Mitral Cells ◽  
Distal Dendrite

The mammalian olfactory bulb is a geometrically organized signal-processing array that utilizes lateral inhibitory circuits to transform spatially patterned inputs. A major part of the lateral circuitry consists of extensively radiating secondary dendrites of mitral cells. These dendrites are bidirectional cables: they convey granule cell inhibitory input to the mitral soma, and they conduct backpropagating action potentials that trigger glutamate release at dendrodendritic synapses. This study examined how mitral cell firing is affected by inhibitory inputs at different distances along the secondary dendrite and what happens to backpropagating action potentials when they encounter inhibition. These are key questions for understanding the range and spatial dependence of lateral signaling between mitral cells. Backpropagating action potentials were monitored in vitro by simultaneous somatic and dendritic whole cell recording from individual mitral cells in rat olfactory bulb slices, and inhibition was applied focally to dendrites by laser flash photolysis of caged GABA (2.5-μm spot). Photolysis was calibrated to activate conductances similar in magnitude to GABAA-mediated inhibition from granule cell spines. Under somatic voltage-clamp with CsCl dialysis, uncaging GABA onto the soma, axon initial segment, primary and secondary dendrites evoked bicuculline-sensitive currents (up to −1.4 nA at −60 mV; reversal at ∼0 mV). The currents exhibited a patchy distribution along the axon and dendrites. In current-clamp recordings, repetitive firing driven by somatic current injection was blocked by uncaging GABA on the secondary dendrite ∼140 μm from the soma, and the blocking distance decreased with increasing current. In the secondary dendrites, backpropagated action potentials were measured 93–152 μm from the soma, where they were attenuated by a factor of 0.75 ± 0.07 (mean ± SD) and slightly broadened (1.19 ± 0.10), independent of activity (35–107 Hz). Uncaging GABA on the distal dendrite had little effect on somatic spikes but attenuated backpropagating action potentials by a factor of 0.68 ± 0.15 (0.45–0.60 μJ flash with 1-mM caged GABA); attenuation was localized to a zone of width 16.3 ± 4.2 μm around the point of GABA release. These results reveal the contrasting actions of inhibition at different locations along the dendrite: proximal inhibition blocks firing by shunting somatic current, whereas distal inhibition can impose spatial patterns of dendrodendritic transmission by locally attenuating backpropagating action potentials. The secondary dendrites are designed with a high safety factor for backpropagation, to facilitate reliable transmission of the outgoing spike-coded data stream, in parallel with the integration of inhibitory inputs.

Phasic Stimuli Evoke Precisely Timed Spikes in Intermittently Discharging Mitral Cells

2004 ◽  
Vol 92 (2) ◽  
pp. 743-753 ◽  
Author(s):  
Ramani Balu ◽  
Phillip Larimer ◽  
Ben W. Strowbridge
Keyword(s):  
Olfactory Bulb ◽  
Action Potentials ◽  
Sensory Stimulation ◽  
Spike Timing ◽  
Mitral Cells ◽  
Potential Oscillations ◽  
Sensory Stimuli ◽  
Subthreshold Oscillations ◽  
Simple Step

Mitral cells, the principal cells of the olfactory bulb, respond to sensory stimulation with precisely timed patterns of action potentials. By contrast, the same neurons generate intermittent spike clusters with variable timing in response to simple step depolarizations. We made whole cell recordings from mitral cells in rat olfactory bulb slices to examine the mechanisms by which normal sensory stimuli could generate precisely timed spike clusters. We found that individual mitral cells fired clusters of action potentials at 20-40 Hz, interspersed with periods of subthreshold membrane potential oscillations in response to depolarizing current steps. TTX (1 μM) blocked a sustained depolarizing current and fast subthreshold oscillations in mitral cells. Phasic stimuli that mimic trains of slow excitatory postsynaptic potentials (EPSPs) that occur during sniffing evoked precisely timed spike clusters in repeated trials. The amplitude of the first simulated EPSP in a train gated the generation of spikes on subsequent EPSPs. 4-aminopyridine (4-AP)–sensitive K+ channels are critical to the generation of spike clusters and reproducible spike timing in response to phasic stimuli. Based on these results, we propose that spike clustering is a process that depends on the interaction between a 4-AP–sensitive K+ current and a subthreshold TTX-sensitive Na+ current; interactions between these currents may allow mitral cells to respond selectively to stimuli in the theta frequency range. These intrinsic properties of mitral cells may be important for precisely timing spikes evoked by phasic stimuli that occur in response to odor presentation in vivo.

Mitral Cells of Olfactory Bulb Are Capable of Encoding Odor Location*

2011 ◽  
Vol 38 (11) ◽  
pp. 1020-1026 ◽  
Author(s):  
Xiang LI ◽  
An-An LI ◽  
Ling GONG ◽  
Qing LIU ◽  
Fu-Qiang XU
Keyword(s):  
Olfactory Bulb ◽  
Mitral Cells

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Group I Metabotropic Glutamate Receptors Are Differentially Expressed by Two Populations of Olfactory Bulb Granule Cells

2007 ◽  
Vol 97 (4) ◽  
pp. 3136-3141 ◽  
Author(s):  
Thomas Heinbockel ◽  
Kathryn A. Hamilton ◽  
Matthew Ennis
Keyword(s):  
Olfactory Bulb ◽  
Glutamate Receptors ◽  
Metabotropic Glutamate Receptors ◽  
Granule Cells ◽  
Mitral Cell ◽  
Mitral Cells ◽  
Patch Clamp Recording ◽  
Metabotropic Glutamate ◽  
Group I ◽  
Bath Application

In the main olfactory bulb, several populations of granule cells (GCs) can be distinguished based on the soma location either superficially, interspersed with mitral cells within the mitral cell layer (MCL), or deeper, within the GC layer (GCL). Little is known about the physiological properties of superficial GCs (sGCs) versus deep GCs (dGCs). Here, we used patch-clamp recording methods to explore the role of Group I metabotropic glutamate receptors (mGluRs) in regulating the activity of GCs in slices from wildtype and mGluR−/− mutant mice. In wildtype mice, bath application of the selective Group I mGluR agonist DHPG depolarized and increased the firing rate of both GC subtypes. In the presence of blockers of fast synaptic transmission (APV, CNQX, gabazine), DHPG directly depolarized both GC subtypes, although the two GC subtypes responded differentially to DHPG in mGluR1−/− and mGluR5−/− mice. DHPG depolarized sGCs in slices from mGluR5−/− mice, although it had no effect on sGCs in slices from mGluR1−/− mice. By contrast, DHPG depolarized dGCs in slices from mGluR1−/− mice but had no effect on dGCs in slices from mGluR5−/− mice. Previous studies showed that mitral cells express mGluR1 but not mGluR5. The present results therefore suggest that sGCs are more similar to mitral cells than dGCs in terms of mGluR expression.

Mitral and Tufted Cells Differ in the Decoding Manner of Odor Maps in the Rat Olfactory Bulb

2004 ◽  
Vol 91 (6) ◽  
pp. 2532-2540 ◽  
Author(s):  
Shin Nagayama ◽  
Yuji K. Takahashi ◽  
Yoshihiro Yoshihara ◽  
Kensaku Mori
Keyword(s):  
Olfactory Bulb ◽  
Spike Activity ◽  
Homologous Series ◽  
Morphological Difference ◽  
Aliphatic Acid ◽  
Mitral Cells ◽  
Firing Rates ◽  
Aliphatic Acids ◽  
Projection Pattern ◽  
Tufted Cells

Mitral and tufted cells in the mammalian olfactory bulb are principal neurons, each type having distinct projection pattern of their dendrites and axons. The morphological difference suggests that mitral and tufted cells are functionally distinct and may process different aspects of olfactory information. To examine this possibility, we recorded odorant-evoked spike responses from mitral and middle tufted cells in the aliphatic acid- and aldehyde-responsive cluster at the dorsomedial part of the rat olfactory bulb. Homologous series of aliphatic acids and aldehydes were used for odorant stimulation. In response to adequate odorants, mitral cells showed spike responses with relatively low firing rates, whereas middle tufted cells responded with higher firing rates. Examination of the molecular receptive range (MRR) indicated that most mitral cells exhibited a robust inhibitory MRR, whereas a majority of middle tufted cells showed no or only a weak inhibitory MRR. In addition, structurally different odorants that activated neighboring clusters inhibited the spike activity of mitral cells, whereas they caused no or only a weak inhibition in the middle tufted cells. Furthermore, responses of mitral cells to an adequate excitatory odorant were greatly inhibited by mixing the odorant with other odorants that activated neighboring glomeruli. In contrast, odorants that activated neighboring glomeruli did not significantly inhibit the responses of middle tufted cells to the adequate excitatory odorant. These results indicate a clear difference between mitral and middle tufted cells in the manner of decoding the glomerular odor maps.

scholarly journals Odor enrichment sculpts the abundance of olfactory bulb mitral cells

2013 ◽  
Vol 541 ◽  
pp. 173-178 ◽  
Author(s):  
Melissa Cavallin Johnson ◽  
K.C. Biju ◽  
Joshua Hoffman ◽  
Debra Ann Fadool
Keyword(s):  
Olfactory Bulb ◽  
Mitral Cells

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

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