Predicting spike timing of neocortical pyramidal neurons by simple threshold models

  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},
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… 

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

The results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Spike-timing prediction in cortical neurons with active dendrites

It is concluded that a simple two-compartment model can predict spike times of pyramidal cells stimulated in the soma and dendrites simultaneously and that regenerating activity in the apical dendritic is required to properly account for the dynamics of layer 5 pyramsidal cells under in-vivo-like conditions.

The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro.

It was found that neurons track unexpectedly fast transients, as their response amplitude has no attenuation up to 200 Hz, higher than the limits set by passive membrane properties and average firing rate and is not affected by the rate of change of the input.

Computational principles of single neuron adaptation

The spiking model introduced in this thesis was not designed to study a particular aspect of single-neuron computation but achieves good performances in predicting the spiking activity of different neuronal types and the proposed method for parameter estimation is efficient and only requires a limited amount of data.

Reliability of spike and burst firing in thalamocortical relay cells

The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice

Made-to-Order Spiking Neuron Model Equipped with a Multi-Timescale Adaptive Threshold

A simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents is devised.

Spike and burst coding in thalamocortical relay cells

The model was used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT) and the cyclic nucleotide modulated h current (Ih).

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model

The SIE is compared with a minimal model constructed to fit the recorded data to show that even with a simple model that is far from perfect in predicting the precise timing of the output spikes of the real neuron, the SIE can still be accurately predicted.

Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times

This paper augments the multi-timescale adaptive threshold model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model.

Bayesian Population Decoding of Spiking Neurons

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire



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

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

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.

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

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
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

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.

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

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

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.