Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation

Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T.; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J.; Toyama, Keisuke

doi:10.1016/j.neunet.2013.01.006

Cited by 13 publications

(33 citation statements)

References 20 publications

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“…We further investigated the effect of the spike data length on the estimation accuracy. The parameters were estimated under different settings of the data length, which was varied from 50 s to 500 s. These data lengths were comparable to the recording time of the experimental data in our previous study [3]. Figure 5 indicates that increasing the spike data length enhanced the estimation accuracy for both g i and g c .…”

Section: Parameter Estimation For the Test Datamentioning

confidence: 58%

“…The aim of our previous study was to overcome this challenging problem on the basis of the idea that the parameters are estimated for small segments of spike data. The distribution of the estimated parameters for small segments captured the essential features of the experimental conditions very well (PIX to reduce g i and CBX to reduce g c ) [3]. However, the estimation accuracy had not yet been properly evaluated, because the true values for g i and g c were unknown for the experiments.…”

Section: Discussionmentioning

confidence: 99%

“…The simulation model was composed of 3 × 3 neurons, each of which consists of soma, dendrite, and spine compartments [3]. The neurons were connected to each other via gap junctions (g c ).…”

Section: Network Model Simulationmentioning

confidence: 99%

“…First, they exhibited firing dynamics significantly different from each other. Second, they imitate the three types of experimental conditions, i.e., administration of GABA blocker picrotoxin (PIX) and gap-junction blocker carbenoxolone (CBX) and their control (CON), in our previous study [3]. We applied the minimum-error method to estimate g i and g c from the test spike data and evaluated the estimation accuracy as the absolute error between the estimated and true parameter values.…”

Section: Test Data Setsmentioning

confidence: 99%

“…In the previous study [3], we targeted inferior olive (IO) neurons, which play a central role in cerebellar learning. The IO network model was composed of nine neurons receiving excitatory and inhibitory inputs.…”

Section: Introductionmentioning

confidence: 99%

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Validation of the Minimum-Error Method for Estimating Model Parameters from Neural Spike Train Data

Hoang

Tokuda

2015

Journal of Signal Processing

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A minimum-error method was developed in our previous study to estimate model parameters from spike train data experimentally recorded from inferior olive neurons. Our method characterized the neuronal firing dynamics using 67 spatiotemporal features. The closest match between experimental and simulated data in the feature space determined two parameters that control the neural network dynamics of the simulation model. This approach, however, has not been fully verified, because the true parameter values were unknown for the experimental data. In the present study, we attempt to validate the minimum-error method using simulated spike data, for which the true parameter values are known, as the test data. Our performance evaluation on the test data confirmed that the minimum-error method is effective for resolving the inverse problem even when the simulation model is an imperfect representation of the experimental data.

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Section: Parameter Estimation For the Test Datamentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Network Model Simulationmentioning

confidence: 99%

Section: Test Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of the Minimum-Error Method for Estimating Model Parameters from Neural Spike Train Data

Hoang

Tokuda

2015

Journal of Signal Processing

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Electrical Coupling through Gap Junctions between Electrically Excitable Cells

Lefler¹,

Uusisaari²

2015

Electrochemical Processes in Biological Systems

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Cerebellar Inhibitory Output Shapes the Temporal Dynamics of Its Somatosensory Inferior Olivary Input

2014

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The cerebellum is necessary and sufficient for the acquisition and execution of adaptively timed conditioned motor responses following repeated paired presentations of a conditioned stimulus and an unconditioned stimulus. The underlying plasticity depends on the convergence of conditioned and unconditioned stimuli signals relayed to the cerebellum by the pontine nucleus and the inferior olive (IO), respectively. Adaptive timing of conditioned responses relies on the correctly predicted onset of the unconditioned stimulus, usually a noxious somatosensory stimulus. We addressed two questions: First, does the IO relay information regarding the duration of somatosensory stimuli to the cerebellum? Multiple-unit recordings from the IO of anesthetized rats that received periorbital airpuffs of various durations revealed that sustained somatosensory stimuli are invariably transformed into phasic IO outputs. The phasic response was followed by a post-peak depression in IO activity as compared to baseline, providing the cerebellum with a highly synchronous signal, time-locked to the stimulus' onset. Second, we sought to examine the involvement of olivocerebellar interactions in this signal transformation. Cerebello-olivary inhibition was interrupted using temporary pharmacological inactivation of cerebellar output nuclei, resulting in more sustained (i.e., less synchronous) IO responses to sustained somatosensory stimuli, in which the post-peak depression was substituted with elevated activity as compared to baseline. We discuss the possible roles of olivocerebellar negative-feedback loops and baseline cerebello-olivary inhibition levels in shaping the temporal dynamics of the IO's response to somatosensory stimuli and the consequences of this shaping for cerebellar plasticity and its ability to adapt to varying contexts.

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Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation

Cited by 13 publications

References 20 publications

Validation of the Minimum-Error Method for Estimating Model Parameters from Neural Spike Train Data

Validation of the Minimum-Error Method for Estimating Model Parameters from Neural Spike Train Data

Electrical Coupling through Gap Junctions between Electrically Excitable Cells

Cerebellar Inhibitory Output Shapes the Temporal Dynamics of Its Somatosensory Inferior Olivary Input

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