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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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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.
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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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Towards a Neuronal Gauge Theory.

Biswa Sengupta1, Arturo Tozzi2, Gerald K Cooray3

  • 1Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.

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Summary
This summary is machine-generated.

This study proposes a gauge theory for neuronal dynamics, suggesting biological systems resolve environmental uncertainty. This framework may explain complex phenomena like attention and action-perception links.

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Area of Science:

  • Neuroscience
  • Theoretical Biology
  • Computational Neuroscience

Background:

  • The neurosciences are accumulating vast knowledge and data.
  • A unifying principle for neuronal dynamics across different timescales is lacking.

Purpose of the Study:

  • To propose a general principle for neuronal dynamics using a gauge theory framework.
  • To characterize the brain and other self-organized biological systems mathematically.
  • To explore how this framework can explain complex biological phenomena.

Main Methods:

  • Formulating a gauge theory for neuronal dynamics.
  • Applying the mathematical apparatus of gauge theory to biological systems.
  • Utilizing approximate Bayesian inference as a basis for the theory.

Main Results:

  • Biological systems, including the brain, can be characterized as resolving uncertainty about their external environment.
  • Systems resolve uncertainty by altering internal states or their environmental relationship.
  • The proposed gauge theory offers a novel mathematical approach to understanding biological systems.

Conclusions:

  • A gauge theory based on approximate Bayesian inference provides a potential unifying principle for neuronal dynamics.
  • This framework may offer new insights into phenomena like attention and the action-perception link.
  • The approach suggests biological systems are fundamentally geared towards uncertainty resolution.