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Neurons: The Axon

Neurons: The Axon

Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment.

The Role of Ion Channels in Neuronal Computation

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.

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Related Experiment Video

Updated: Jun 3, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Fitting neuron models to spike trains.

Cyrille Rossant¹, Dan F M Goodman, Bertrand Fontaine

¹Laboratoire Psychologie de la Perception, CNRS, Université Paris Descartes Paris, France.

Frontiers in Neuroscience

|March 19, 2011

Summary

This summary is machine-generated.

Researchers developed a flexible Python-based tool using the Brian simulator to fit complex neuron models to experimental data. This computational neuroscience method accurately predicts neural responses and simplifies complex models.

Keywords:

optimization parallel computing python simulation spiking models

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Area of Science:

Computational neuroscience
Systems neuroscience
Computational modeling of neural circuits

Background:

Accurate computational models of individual neurons are crucial for understanding neural circuit function.
Spiking neuron models can predict precisely timed spike trains from cortical neurons if properly fitted.
Existing fitting techniques may lack the efficiency and flexibility needed for testing diverse models.

Purpose of the Study:

To present a generic, efficient, and flexible solution for defining and fitting arbitrary neuron models to electrophysiological recordings.
To leverage vectorization and parallel computing for enhanced fitting performance.
To demonstrate the application of this method on real neural data and model reduction.

Main Methods:

Utilized the Brian simulator, a Python-based neural network simulator.
Implemented vectorization and parallel computing for efficient model fitting.
Applied the fitting technique to neural recordings from the barrel cortex and auditory brainstem.

Main Results:

Confirmed that simple adaptive spiking models can accurately predict cortical neuron responses.
Demonstrated the successful fitting of arbitrary neuron models to electrophysiological data.
Showcased the reduction of a complex multicompartmental model to a simple effective spiking model.

Conclusions:

The developed Brian simulator-based approach provides an efficient and flexible platform for fitting neuron models.
This method accurately predicts neural responses and facilitates model simplification in computational neuroscience.
The tool is applicable to various neural systems and model complexities.