Electrical responses of three classes of granule cells of the olfactory bulb to synaptic inputs in different dendritic locations

Simões-de-Souza, Fábio M.; Antunes, Gabriela; Roque, Antônio C.

doi:10.3389/fncom.2014.00128

Cited by 4 publications

(5 citation statements)

References 48 publications

(71 reference statements)

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“…We selected parameters to model class II behavior of MCs [ 67 ] and to establish realistic f − I curves [ 9 ] ( Fig 2C ). Following conductance based models [ 5 , 7 ], we took GCs to be integrators [ 42 , 43 ]. Other work suggests that some GCs may display resonator properties, including subthreshold membrane potential oscillations [ 68 , 69 ], but the very low oscillation frequency may make them irrelevant to excitability classification [ 43 ], especially since some GCs appear to be entirely non-resonant [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

“…Computational studies are necessary for understanding how this odor information is reshaped, since we lack experimental methods for interrogating this network’s structure-function dependency. However, the sheer number of neurons, encompassing tens of thousands of excitatory cells and millions of inhibitory cells [ 1 ]; intricate network architecture [ 2 – 4 ]; and complex spiking dynamics [ 5 – 9 ] make detailed biophysical simulation impractical at large scale. Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [ 10 – 22 ] and often study smaller networks on the order of hundreds or even tens of neurons [ 10 , 11 , 13 , 14 , 19 , 22 – 24 ], allowing for highly complex, conductance-based neuronal frameworks [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Connectivity and dynamics in the olfactory bulb

2022

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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 connection probability 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. The model in turn predicts testable relationships between network structure and several functional properties, including lateral inhibition, odor pattern decorrelation, and LFP oscillation frequency. We use the model to explore the influence of cortex on the olfactory bulb, demonstrating possible mechanisms by which cortical feedback to mitral cells or granule cells can influence bulbar activity, as well as how neurogenesis can improve bulbar decorrelation without requiring cell death. Our methodology provides a tractable tool for other researchers.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Connectivity and dynamics in the olfactory bulb

2022

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“…2C). Following conductance based models [5, 7], we took GCs to be integrators [42, 43]. Other work suggests that some GCs may display resonator properties, including subthreshold membrane potential oscillations [63, 64], but the very low oscillation frequency may make them irrelevant to excitability classification [43], especially since some GCs appear to be entirely non-resonant [63].…”

Section: Resultsmentioning

confidence: 99%

“…Computational studies are necessary for understanding how this odor information is reshaped, since we lack experimental methods for interrogating this network's structure-function dependency. However, the sheer number of neurons, encompassing tens of thousands of excitatory cells and millions of inhibitory cells [1]; intricate network architecture [2][3][4]; and complex spiking dynamics [5][6][7][8][9] make detailed biophysical simulation impractical at large scale. Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [10][11][12][13][14][15][16][17][18][19][20][21][22] and often study smaller networks on the order of hundreds or even tens of neurons [10,11,13,14,19,[22][23][24], allowing for highly complex, conductance-based neuronal frameworks [7].…”

Section: Introductionmentioning

confidence: 99%

Connectivity and dynamics in the olfactory bulb

Kersen

Tavoni

Balasubramanian

2021

Preprint

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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.

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“…A variety of methods have been used to study the spatiotemporal patterning of olfactory system, such as biosensors and computational models (Arruda et al , 2013; Gilra and Bhalla, 2015; Kaplan and Lansner, 2014; Migliore and Shepherd, 2008; Simoes-de-Souza et al , 2014; Ye et al , 2010; Ying et al , 2012; Yu et al , 2013). Our laboratory has made certain achievements in cell-based sensors (Ling et al , 2010; Tian et al , 2015) and the olfactory modeling and simulation (Ye et al , 2010; Ying et al , 2012).…”

Section: Introductionmentioning

confidence: 99%

A review of olfactory cell-based sensors: coupling microelectronics to olfactory cells

Qin

Chen

et al. 2016

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Purpose Olfaction plays a very important role in daily life. The olfactory system has the ability to recognize, discriminate and identify thousands of odorant compounds with extremely high sensitivity and specificity. The research on olfactory system has very important values in exploring the mechanisms of information processing in the other sensory nervous systems and brain. Recently, with the development of molecular biological and microelectronics technology research, the study of olfactory cell-based sensors has made great progress. The purpose of this paper is to provide details of recent developments in olfactory cell-based sensors. Design/methodology/approach Following an introduction, this paper first discusses some olfactory cell-based biosensors, which focus on the light-addressable potentiometric sensors and the microelectrode arrays. Second, surface modification, microfabrication and microfluidic technology which can improve the efficiency of cell immobilization will be summarized. The research trends of olfactory cell-based sensor in future will be proposed. Findings This paper shows that the biosensors’ performance is expected to be greatly improved due to the fast development of nanotechnology, optical technology and microelectronics. More and more emerging intelligent olfactory sensors will have a promising prospect in many application fields, including food quality and safety assessment, environmental monitor and human diseases detection. Originality/value This paper provides a detailed and timely review of the rapidly growing research in the olfactory cell-based sensors.

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Electrical responses of three classes of granule cells of the olfactory bulb to synaptic inputs in different dendritic locations

Cited by 4 publications

References 48 publications

Connectivity and dynamics in the olfactory bulb

Connectivity and dynamics in the olfactory bulb

Connectivity and dynamics in the olfactory bulb

A review of olfactory cell-based sensors: coupling microelectronics to olfactory cells

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