Predicting spike timing of neocortical pyramidal neurons by simple threshold models

@article{Jolivet2006PredictingST,
  title={Predicting spike timing of neocortical pyramidal neurons by simple threshold models},
  author={Renaud Blaise Jolivet and Alexander Rauch and Hans-Rudolf L{\"u}scher and Wulfram Gerstner},
  journal={Journal of Computational Neuroscience},
  year={2006},
  volume={21},
  pages={35-49}
}
Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current—is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the… 

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References

SHOWING 1-10 OF 91 REFERENCES

Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell

TLDR
This work matches a simple integrate-and-fire model to the experimentally measured integrative properties of cortical regular spiking cells, leading to an intuitive picture of neuronal integration that unifies the seemingly contradictory and random walk pictures that have been proposed.

Effective minimal threshold models of neuronal activity

TLDR
This work suggests that, at least in the considered settings, the picture of a neuron that combines linear summation with a threshold criterion is not too wrong and provides a justification to the use of IF models in large scale network simulations.

Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents.

TLDR
The integrate-and-fire model with spike-frequency-dependent adaptation/facilitation is an adequate model reduction of cortical cells when the mean spike- frequency response to in vivo-like currents with stationary statistics is considered.

Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

TLDR
It is suggested that the noise inherent in the operation of ion channels enables neurons to act as smart encoders and channel stochasticity should be considered in realistic models of neurons.

Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo.

  • R. AzouzC. Gray
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 2000
TLDR
The basic mechanism responsible for action potential generation also enhances the sensitivity of cortical neurons to coincident synaptic inputs, and voltage-gated Na(+) and K(+) conductances endow cortical neurons with an enhanced sensitivity to rapid depolarizations that arise from synchronous excitatory synaptic inputs.

How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs

TLDR
This study examines the ability of neurons to track temporally varying inputs by investigating how the instantaneous firing rate of a neuron is modulated by a noisy input with a small sinusoidal component with frequency, and proposes a simplified one-variable model, the “exponential integrate-and-fire neuron,” as an approximation of a conductance-based model.

Reliability of spike timing in neocortical neurons.

TLDR
Data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

Novel Integrate-and-re-like Model of Repetitive Firing in Cortical Neurons

TLDR
A simple repetitive ring model is derived from the Hodgkin-Huxley equations, related to the conventional integrate-andre model, but uses a time-varying time constant in place of the usual time constant.

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

TLDR
It is suggested that quantities are represented as rate codes in ensembles of 50–100 neurons, which implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.
...