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Related Concept Videos

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...
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...
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...

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Related Experiment Video

Updated: Jun 25, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Python for information theoretic analysis of neural data.

Robin A A Ince1, Rasmus S Petersen, Daniel C Swan

  • 1Faculty of Life Sciences, University of Manchester Manchester, UK.

Frontiers in Neuroinformatics
|February 27, 2009
PubMed
Summary
This summary is machine-generated.

Information theory provides quantitative insights into neural systems by treating them as communication channels. Using Python enhances the speed and applicability of these analyses for neuroscience research.

Keywords:
Pythonbiase-scienceentropyinformation theorymaximum entropyneural coding

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

Last Updated: Jun 25, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

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

Area of Science:

  • Neuroscience
  • Information Theory
  • Computational Neuroscience

Background:

  • Information theory is crucial for modern quantitative neuroscience.
  • It enables treating neural systems as stochastic communication channels.
  • Provides quantitative insights into neural sensory coding functions.

Purpose of the Study:

  • Describe experiences using Python for information theoretic analysis in neuroscience.
  • Outline algorithmic, statistical, and numerical challenges in computing information theoretic quantities from neural data.
  • Address problems from limited sampling bias and maximum entropy distribution calculations.

Main Methods:

  • Utilizing Python for information theoretic analysis of neural data.
  • Addressing challenges in computation, including sampling bias and maximum entropy distributions.
  • Developing and applying algorithms for analyzing complex neural systems.

Main Results:

  • Python significantly improves the speed and applicability of information theoretic algorithms.
  • Enables analysis of larger datasets with more variables.
  • Facilitates integration with collaborative databases and computational resources.

Conclusions:

  • Python-based information theoretic analysis offers a powerful approach for quantitative neuroscience.
  • Overcomes limitations of traditional methods, especially for non-linear systems.
  • Enhances the scope and efficiency of neural data analysis.