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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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Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
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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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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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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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Understanding Emergent Dynamics: Using a Collective Activity Coordinate of a Neural Network to Recognize Time-Varying

John J Hopfield1

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A. hopfield@princeton.edu.

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Summary
This summary is machine-generated.

This study presents a biologically based neural network model that robustly categorizes time-varying patterns like speech. The network

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Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Theoretical neuroscience

Background:

  • Complex behaviors in higher animals arise from neural ensembles.
  • Categorizing time-varying patterns (e.g., speech) is challenging due to variations in timing and duration.

Purpose of the Study:

  • To describe a biologically based neural network model for robustly categorizing time-varying stimulus patterns.
  • To present a novel computational algorithm derived from neural circuit dynamics.

Main Methods:

  • Simulation of a biologically based neural network with feedback connections and ongoing activity.
  • Derivation of a closed equation of motion for a collective variable to describe network computation.
  • Analysis of network dynamics on a reduced dimensionality manifold.

Main Results:

  • The neural network model successfully categorizes time-varying patterns, mimicking natural solutions.
  • A collective variable equation quantitatively explains emergent network computation.
  • Network dynamics, shaped by feedback and ongoing activity, define the implemented algorithm.

Conclusions:

  • Biologically inspired neural networks offer robust solutions for complex pattern recognition.
  • Neural integrators-like circuits can serve as building blocks for advanced computational algorithms.
  • The collective dynamics of neural networks provide a framework for understanding emergent computation.