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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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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.
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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
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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...
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Neurulation is the embryological process which forms the precursors of the central nervous system and occurs after gastrulation has established the three primary cell layers of the embryo: ectoderm, mesoderm, and endoderm. In humans, the majority of this system is formed via primary neurulation, in which the central portion of the ectoderm—originally appearing as a flat sheet of cells—folds upwards and inwards, sealing off to form a hollow neural tube. As development proceeds, the...
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Propagation of Action Potentials01:23

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Related Experiment Video

Updated: Aug 13, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Neural Information Squeezer for Causal Emergence.

Jiang Zhang1,2, Kaiwei Liu1

  • 1School of Systems Sciences, Beijing Normal University, Beijing 100875, China.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Neural Information Squeezer, a machine learning framework that automatically identifies causal emergence from time series data by finding optimal coarse-graining strategies. It enables precise control over information channels for analyzing macro-level dynamics.

Keywords:
causal emergencecoarse-graininginvertible neural network

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Area of Science:

  • Complex Systems
  • Machine Learning
  • Causality

Background:

  • Causal emergence, where macro-level causality exceeds micro-level, is known in dynamical systems.
  • Identifying emergent causality is challenging due to the difficulty in finding optimal coarse-graining strategies.

Purpose of the Study:

  • To propose a general machine learning framework, the Neural Information Squeezer (NIS).
  • To automatically extract coarse-graining strategies and macro-level dynamics from time series data.
  • To directly identify causal emergence.

Main Methods:

  • Utilizing an invertible neural network to decompose coarse-graining into information conversion and discarding.
  • Implementing a framework to control information channel width and derive analytical properties.
  • Applying the framework to extract coarse-graining functions and dynamics across different system levels.

Main Results:

  • The Neural Information Squeezer framework successfully extracts effective coarse-graining strategies and macro-level dynamics.
  • The method allows for precise control over information channels.
  • Causal emergence was identified from data in several example systems.

Conclusions:

  • The Neural Information Squeezer provides a powerful, automated approach to identifying causal emergence from data.
  • This framework simplifies the extraction of macro-level dynamics and coarse-graining strategies.
  • It offers analytical insights into information processing within dynamical systems.