Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

Jolivet, Renaud; Gerstner, Wulfram

doi:10.1016/j.jphysparis.2005.09.010

Cited by 13 publications

(6 citation statements)

References 34 publications

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“…To account for both types of errors, we defined a stringent coincidence window (δ = 84 µs) and calculated the proportion of coincident spikes in both the recorded and predicted spike trains. We used the gamma factor , a normalized coincidence measure that has been used in a number of studies [30]–[32]. We optimized the model parameters to maximize on a given recording, and the model performance was then tested on different recordings in the same cell.…”

Section: Resultsmentioning

confidence: 99%

“…This similarity is quantified using the gamma factor () [30], [31], a normalized measure of coincidence between spike trains within a temporal window : is the mean firing rate of the experimental recording, is the number of coincidences between the predicted and recorded spike trains computed within a time window , and denote the number of spikes in the recorded and predicted spike train, respectively. is the expected number of coincidences generated by a Poisson process with rate .…”

Section: Methodsmentioning

confidence: 99%

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Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

2014

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Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

2014

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“…These models have been found to approximate very well the response properties of cortical neurons (Rauch et al, 2003;Giugliano et al, 2004;Jolivet et al, 2006;La Camera et al, 2006) and more biophysically detailed model neurons (FourcaudTrocmé et al, 2003;Jolivet and Gerstner, 2004;Brette and Gerstner, 2005). IF models generally include a fixed absolute refractory period T arp after spike emission.…”

Section: Mpfc Pyramidal Cells Respond As Integrate-and-fire Neuronsmentioning

confidence: 99%

The Impact of Input Fluctuations on the Frequency–Current Relationships of Layer 5 Pyramidal Neurons in the Rat Medial Prefrontal Cortex

Arsiero

Lüscher

Lundstrom³

et al. 2007

J. Neurosci.

107

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The role of irregular cortical firing in neuronal computation is still debated, and it is unclear how signals carried by fluctuating synaptic potentials are decoded by downstream neurons. We examined in vitro frequency versus current ( f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. Studies in the somatosensory cortex show that L5 neurons become insensitive to input fluctuations as input mean increases and that their f-I response becomes linear. In contrast, our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level.

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“…Third, cortical neurons in vitro produce responses to white noise currents that share properties of in vivo activity, and Gaussian white noise provides a basic model for the distribution of total current inputs at the soma under conditions of intense synaptic bombardment (Mainen and Sejnowski, 1995;Destexhe et al, 2001;Rauch et al, 2003). Finally, modeling work has shown that, under many circumstances, conductancedriven stimuli produce similar responses to white noise current stimuli: in models that capture key properties of experimentally recorded neurons, the response properties of model neurons driven by conductance stimuli map onto the response properties of equivalent neurons driven by current stimuli (Rauch et al, 2003;Jolivet and Gerstner, 2004;La Camera et al, 2004;Richardson, 2004;Jolivet et al, 2006).…”

Section: Stimulus Designmentioning

confidence: 99%

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

Díaz-Quesada¹,

Maravall²

2008

J. Neurosci.

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Barrel cortex neuronal responses adapt to changes in the statistics of complex whisker stimuli. This form of adaptation involves an adjustment in the input-output tuning functions of the neurons, such that their gain rescales depending on the range of the current stimulus distribution. Similar phenomena have been observed in other sensory systems, suggesting that adaptive adjustment of responses to ongoing stimulus statistics is an important principle of sensory function. In other systems, adaptation and gain rescaling can depend on intrinsic properties; however, in barrel cortex, whether intrinsic mechanisms can contribute to adaptation to stimulus statistics is unknown. To examine this, we performed whole-cell patch-clamp recordings of pyramidal cells in acute slices while injecting stochastic current stimuli. We induced changes in statistical context by switching across stimulus distributions. The firing rates of neurons adapted in response to changes in stimulus statistics. Adaptation depended on the form of the changes in stimulus distribution: in vivo-like adaptation occurred only for rectified stimuli that maintained neurons in a persistent state of net depolarization. Under these conditions, neurons rescaled the gain of their input-output functions according to the scale of the stimulus distribution, as observed in vivo. This stimulus-specific adaptation was caused by intrinsic properties and correlated strongly with the amplitude of calciumdependent slow afterhyperpolarizations. Our results suggest that widely expressed intrinsic mechanisms participate in barrel cortex adaptation but that their recruitment is highly stimulus specific.

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Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

Cited by 13 publications

References 34 publications

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

The Impact of Input Fluctuations on the Frequency–Current Relationships of Layer 5 Pyramidal Neurons in the Rat Medial Prefrontal Cortex

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

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