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Related Concept Videos

Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

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 results...

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Updated: May 12, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Maximum-entropy-based metrics for quantifying critical dynamics in spiking neuron data.

Felipe Serafim1, Tawan T A Carvalho1,2,3, Mauro Copelli1

  • 1Departamento de Física, Centro de Ciência Exatas e da Natureza, <a href="https://ror.org/047908t24">Universidade Federal de Pernambuco</a>, Recife PE 50670-901, Brazil.

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|September 19, 2024
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Summary
This summary is machine-generated.

Researchers explored brain activity using a maximum entropy approach. This method successfully identified critical behavior in computational models and aligns with experimental data from rat brains, supporting the brain

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Statistical Mechanics

Background:

  • The brain's operational state is hypothesized to be a critical regime.
  • Maximum-entropy models offer a novel method for detecting criticality in neuronal data.

Purpose of the Study:

  • To investigate signatures of criticality using a firing rate-based maximum entropy approach.
  • To compare results from computational models with experimental brain data.

Main Methods:

  • Applied a firing rate-based maximum entropy method to computational model datasets.
  • Compared model-generated criticality signatures with experimental cortical data from urethane-anesthetized rats.

Main Results:

  • The maximum entropy approach consistently identified critical behavior near phase transitions in computational models.
  • Criticality was ruled out in models lacking a phase transition.
  • Findings from models were compatible with experimental data from rat brains.

Conclusions:

  • The maximum entropy approach is a viable tool for identifying brain criticality.
  • Results provide further evidence supporting the hypothesis that brain activity operates in a critical state.