Spontaneous activity of first- and second-order neurons in the frog olfactory system

Rospars, Jean-Pierre; Lánský, Petr; Vaillant, Jean; Duchamp‐Viret, Patricia; Duchamp, André

doi:10.1016/0006-8993(94)90793-5

Cited by 43 publications

(26 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Poisson process as a model of spontaneous activity of a neuron was experimentally observed many times and on very different neuronal structures (e.g., Eggermont et al, 1993;Jones and Jones, 2000;Lin and Chen, 2000;Tateno et al, 2002). In sensory neurons, we showed in frogs (Rospars et al, 1994) and rats (Duchamp-Viret et al, 2005) that the spontaneous activity of olfactory receptor neurons can be described by a Poisson process. Theoretical arguments for the fact that the spontaneous activity is a low rate Poisson process can be found in Laughlin (2001).…”

Section: Spontaneous Activitymentioning

confidence: 97%

The parameters of the stochastic leaky integrate-and-fire neuronal model

Lánský

Šanda

2006

J Comput Neurosci

View full text Add to dashboard Cite

Five parameters of one of the most common neuronal models, the diffusion leaky integrate-and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on the basis of intracellular recording. These parameters can be classified into two categories. Three of them (the membrane time constant, the resting potential and the firing threshold) characterize the neuron itself. The remaining two characterize the neuronal input. The intracellular data were collected during spontaneous firing, which in this case is characterized by a Poisson process of interspike intervals. Two methods for the estimation were applied, the regression method and the maximum-likelihood method. Both methods permit to estimate the input parameters and the membrane time constant in a short time window (a single interspike interval). We found that, at least in our example, the regression method gave more consistent results than the maximum-likelihood method. The estimates of the input parameters show the asymptotical normality, which can be further used for statistical testing, under the condition that the data are collected in different experimental situations. The model neuron, as deduced from the determined parameters, works in a subthreshold regimen. This result was confirmed by both applied methods. The subthreshold regimen for this model is characterized by the Poissonian firing. This is in a complete agreement with the observed interspike interval data.

show abstract

Section: Spontaneous Activitymentioning

confidence: 97%

The parameters of the stochastic leaky integrate-and-fire neuronal model

Lánský

Šanda

2006

J Comput Neurosci

View full text Add to dashboard Cite

show abstract

“…These may be due to the arrival of stimulant (e.g. odorant) molecules at receptor molecules (Rospars et al 1994), as such a process (primitively a birth and death process) is known to be able to be approximated by an Ornstein-Uhlenbeck (OU) process (OUP) (Tuckwell 1987). Such an investigation will be carried out in a later article.…”

Section: Introductionmentioning

confidence: 98%

A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current

Tuckwell

Wan

Rospars

2002

Biological Cybernetics

Self Cite

View full text Add to dashboard Cite

We consider a spatial neuron model in which the membrane potential satisfies a linear cable equation with an input current which is a dynamical random process of the Ornstein-Uhlenbeck (OU) type. This form of current may represent an approximation to that resulting from the random opening and closing of ion channels on a neuron's surface or to randomly occurring synaptic input currents with exponential decay. We compare the results for the case of an OU input with those for a purely white-noise-driven cable model. The statistical properties, including mean, variance and covariance of the voltage response to an OU process input in the absence of a threshold are determined analytically. The mean and the variance are calculated as a function of time for various synaptic input locations and for values of the ratio of the time constant of decay of the input current to the time constant of decay of the membrane voltage in the physiological range for real neurons. The limiting case of a white-noise input current is obtained as the correlation time of the OU process approaches zero. The results obtained with an OU input current can be substantially different from those in the white-noise case. Using simulation of the terms in the series representation for the solution, we estimate the interspike interval distribution for various parameter values, and determine the effects of the introduction of correlation in the synaptic input stochastic process.

show abstract

“…In this study, respiratory activity in MTCs was found to be diverse in both its level and its degree of tuning to the transition from inhalation to exhalation (Fig 1 and [6, 17, 37, 42]). Respiration evokes neural rhythmic activity across species and has long been hypothesized to activate conserved mechanisms of odor processing [44, 45]. Even in the absence of respiration, oscillatory burst firing is observed in the antennal lobe circuit and is internally driven by inhibition [46–49].…”

Section: Discussionmentioning

confidence: 99%

Respiration Gates Sensory Input Responses in the Mitral Cell Layer of the Olfactory Bulb

et al. 2016

View full text Add to dashboard Cite

Respiration plays an essential role in odor processing. Even in the absence of odors, oscillating excitatory and inhibitory activity in the olfactory bulb synchronizes with respiration, commonly resulting in a burst of action potentials in mammalian mitral/tufted cells (MTCs) during the transition from inhalation to exhalation. This excitation is followed by inhibition that quiets MTC activity in both the glomerular and granule cell layers. Odor processing is hypothesized to be modulated by and may even rely on respiration-mediated activity, yet exactly how respiration influences sensory processing by MTCs is still not well understood. By using optogenetics to stimulate discrete sensory inputs in vivo, it was possible to temporally vary the stimulus to occur at unique phases of each respiration. Single unit recordings obtained from the mitral cell layer were used to map spatiotemporal patterns of glomerular evoked responses that were unique to stimulations occurring during periods of inhalation or exhalation. Sensory evoked activity in MTCs was gated to periods outside phasic respiratory mediated firing, causing net shifts in MTC activity across the cycle. In contrast, odor evoked inhibitory responses appear to be permitted throughout the respiratory cycle. Computational models were used to further explore mechanisms of inhibition that can be activated by respiratory activity and influence MTC responses. In silico results indicate that both periglomerular and granule cell inhibition can be activated by respiration to internally gate sensory responses in the olfactory bulb. Both the respiration rate and strength of lateral connectivity influenced inhibitory mechanisms that gate sensory evoked responses.

show abstract

Spontaneous activity of first- and second-order neurons in the frog olfactory system

Cited by 43 publications

References 52 publications

The parameters of the stochastic leaky integrate-and-fire neuronal model

The parameters of the stochastic leaky integrate-and-fire neuronal model

A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current

Respiration Gates Sensory Input Responses in the Mitral Cell Layer of the Olfactory Bulb

Contact Info

Product

Resources

About