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Related Concept Videos

Neuronal Communication01:28

Neuronal Communication

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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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Neurons as Communicators of the Brain01:22

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Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
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Neuron Structure01:30

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Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
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Neural Circuits01:25

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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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Nervous Tissue: Neuron Types01:19

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Neurons, the fundamental units of the nervous system, can be classified based on both their structural and functional characteristics.
Structurally, neurons are categorized into three main types: multipolar, bipolar, and unipolar (or pseudounipolar). Multipolar neurons, which are the most common type in the brain and spinal cord, as well as all motor neurons, possess multiple dendrites and a single axon.
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The Role of Ion Channels in Neuronal Computation01:19

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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.
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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The neuron as a direct data-driven controller.

Jason J Moore1,2, Alexander Genkin2, Magnus Tournoy2

  • 1Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016.

Proceedings of the National Academy of Sciences of the United States of America
|June 24, 2024
PubMed
Summary
This summary is machine-generated.

Neurons act as optimal feedback controllers, steering their environment toward desired states. This new model explains complex neural behaviors like plasticity shifts and operational variability, moving beyond traditional neuron models.

Keywords:
controldynamicsneuron

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Area of Science:

  • Computational neuroscience
  • Neurobiology
  • Control theory

Background:

  • Existing normative models primarily focus on prediction.
  • Gaps in physiological data necessitate new approaches to modeling neuronal function.
  • Traditional neuron models (e.g., McCulloch-Pitts-Rosenblatt) are limited in scope.

Purpose of the Study:

  • To conceptualize neurons as optimal feedback controllers.
  • To extend normative theories of neuronal function beyond prediction.
  • To develop a biologically informed model of neuronal control.

Main Methods:

  • Utilizing the direct data-driven control (DD-DC) framework.
  • Modeling neurons as controllers that steer their environment toward desired states.
  • Incorporating synaptic feedback for control effectiveness evaluation.

Main Results:

  • The DD-DC neuron model explains spike-timing-dependent plasticity (potentiation-depression shift).
  • The model accounts for the duration and adaptive nature of neuronal filters.
  • It elucidates spike generation imprecision and brain's operational variability/noise.

Conclusions:

  • Neurons function as optimal feedback controllers, not just predictors.
  • The DD-DC framework offers a biologically plausible model for neural computation.
  • This approach provides a fundamental, modern unit for neural network construction.