Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Second Uniqueness Theorem01:16

Second Uniqueness Theorem

1.0K
Consider a region consisting of several individual conductors with a definite charge density in the region between these conductors. The second uniqueness theorem states that if the total charge on each conductor and the charge density in the in-between region are known, then the electric field can be uniquely determined.
In contrast, consider that the electric field is non-unique and apply Gauss's law in divergence form in the region between the conductors and the integral form to the...
1.0K
Law of Independent Assortment02:03

Law of Independent Assortment

55.9K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
55.9K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

726
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
726
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

554
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
554
Linear time-invariant Systems01:23

Linear time-invariant Systems

289
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
289
Convolution Properties I01:20

Convolution Properties I

180
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
180

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CBR-db: A Cheminformatic Database for Biochemical Reaction Analysis.

ACS synthetic biology·2026
Same author

Variable temperature processing by plasmodesmata regulates robust bud dormancy release.

Nature communications·2026
Same author

Revealing non-trivial information structures in aneural biological tissues via functional connectivity.

PLoS computational biology·2025
Same author

Assembly theory explains and quantifies selection and evolution.

Nature·2023
Same author

Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules.

International journal of molecular sciences·2022
Same author

Formalising the Pathways to Life Using Assembly Spaces.

Entropy (Basel, Switzerland)·2022

Related Experiment Video

Updated: Jul 19, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K

On the non-uniqueness problem in integrated information theory.

Jake R Hanson1,2,3, Sara I Walker1,2,4,5,3

  • 1School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA.

Neuroscience of Consciousness
|August 10, 2023
PubMed
Summary
This summary is machine-generated.

Integrated Information Theory

Keywords:
computational neuroscienceconsciousnessintegrated information theory (IIT)mathematical theories of consciousnessnon-uniqueness

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K

Related Experiment Videos

Last Updated: Jul 19, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K

Area of Science:

  • Neuroscience
  • Consciousness Studies
  • Mathematical Biology

Background:

  • Integrated Information Theory (IIT) 3.0 is a prominent theory of consciousness.
  • IIT 3.0 quantifies consciousness using a scalar measure, Φ, derived from phenomenological axioms.

Purpose of the Study:

  • To investigate the mathematical rigor of the Φ measure in Integrated Information Theory (IIT) 3.0.
  • To determine if the Φ value is uniquely defined for a given system.

Main Methods:

  • Developed an algorithm to compute all possible Φ values for a system based on IIT 3.0's mathematical definition.
  • Analyzed published Φ values and their selection criteria.

Main Results:

  • Demonstrated that the Φ measure is not a well-defined mathematical concept, yielding non-unique values.
  • Showed that published Φ values are arbitrarily selected from multiple valid alternatives.
  • Identified instances where both high and low Φ values are simultaneously predicted for systems.

Conclusions:

  • The current formulation of IIT 3.0 does not provide a decidable measure of consciousness due to the non-uniqueness of Φ.
  • Reinterpretations of consciousness and non-consciousness in systems are non-decidable with the current IIT 3.0 framework.