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Related Concept Videos

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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Neurons: The Axon01:21

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.

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Related Experiment Video

Updated: Jun 26, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Brian: a simulator for spiking neural networks in python.

Dan Goodman1, Romain Brette

  • 1Département d'Informatique, Ecole Normale Supérieure Paris, France.

Frontiers in Neuroinformatics
|December 31, 2008
PubMed
Summary
This summary is machine-generated.

Brian is a new Python-based simulator for spiking neural networks. It offers flexibility for developing custom neuron models and analyzing data, making it ideal for computational neuroscience research and education.

Keywords:
Pythoncomputational neuroscienceintegrate and fireneural networkssimulationsoftwarespiking neuronsteaching

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

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Jun 26, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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
  • Neural Network Simulation
  • Scientific Computing

Background:

  • Existing spiking neural network simulators may lack flexibility for novel neuron models.
  • Developing and simulating complex neural networks requires specialized tools.
  • Matlab and C are common but can be less intuitive for rapid prototyping.

Purpose of the Study:

  • Introduce Brian, a new, flexible simulator for spiking neural networks.
  • Enable users to define custom neuron models using differential equations.
  • Provide an accessible tool for research and education in computational neuroscience.

Main Methods:

  • Brian is a Python-based simulator utilizing vectorization for efficiency.
  • Users can define neuron models via ordinary differential equations.
  • Integration with Python scientific libraries facilitates data analysis.

Main Results:

  • Brian supports standard and arbitrary differential equation-based neuron models.
  • Efficient simulations are achieved despite Python's interpreted nature.
  • The simulator is well-suited for non-standard neuron models.

Conclusions:

  • Brian offers an intuitive and flexible platform for spiking neural network simulations.
  • Its design facilitates the exploration of novel computational neuroscience models.
  • Brian serves as a valuable educational tool for teaching computational neuroscience.