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Related Concept Videos

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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Action Potential01:14

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Electrical Synapses01:28

Electrical Synapses

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Related Experiment Video

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Published on: March 25, 2014

Spike train probability models for stimulus-driven leaky integrate-and-fire neurons.

Shinsuke Koyama1, Robert E Kass

  • 1Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA. koyama@stat.cmu.edu

Neural Computation
|March 14, 2008
PubMed
Summary

The study compares simplified point process models (TRRP and m-IMI) with leaky integrate-and-fire (LIF) neuron spike trains. The m-IMI and TRRP models fit LIF data well, requiring large samples to detect poor fit, with specific models excelling under different stimulus conditions.

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

  • Computational Neuroscience
  • Statistical Modeling
  • Spike Train Analysis

Background:

  • Mathematical models of neurons, such as the leaky integrate-and-fire (LIF) model, are crucial for understanding neuronal spiking.
  • Statistical point process models, including the time-rescaled renewal process (TRRP) and multiplicative inhomogeneous Markov interval (m-IMI) model, offer data-analytical techniques for spike trains.
  • The ease of application and accuracy of these simplified models in fitting complex neuronal data remain key research questions.

Discussion:

  • This study evaluates the fitting capabilities of the TRRP and m-IMI models against spike trains generated by stimulus-driven LIF neurons.
  • Under constant stimuli, LIF spike trains behave as renewal processes, well-described by TRRP and m-IMI models.
  • With time-varying stimuli, the LIF, m-IMI, and TRRP models exhibit distinct dependencies on stimulus timing and inter-spike intervals, necessitating a quantitative assessment of their fit.

Key Insights:

  • The TRRP and m-IMI models demonstrate a strong ability to fit LIF neuron spike trains, even with time-varying stimuli.
  • Unlike Poisson models, which show lack of fit even in small samples, the TRRP and m-IMI models require significantly larger datasets to reveal statistical evidence of poor fit.
  • Model performance varies with stimulus characteristics: m-IMI excels when stimulus mean changes over time, while TRRP is superior when stimulus variance fluctuates.

Outlook:

  • Further research could explore the application of these models to more complex neuronal dynamics and network activity.
  • Investigating the theoretical underpinnings of the observed differences in model fit could lead to more refined statistical tools for neuroscience.
  • Developing adaptive methods for model selection based on stimulus properties could enhance the analysis of real-world neural data.