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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

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Related Experiment Video

Updated: Jul 5, 2026

Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms
10:04

Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms

Published on: April 7, 2011

A (somewhat) new solution to the variable binding problem.

Leon Barrett1, Jerome Feldman, Liam Mac Dermed

  • 1Electrical Engineering and Computer Science Department, University of California, Berkeley, CA 94720, USA. leon@barrettnexus.com

Neural Computation
|April 29, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces short signatures as a novel mechanism for the brain to solve the binding problem, enhancing computational models beyond temporal synchrony. These advancements enable better unification and handling of multiple logical terms.

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Last Updated: Jul 5, 2026

Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms
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Published on: April 7, 2011

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The human brain solves the binding problem for unconscious inference by grouping object properties.
  • Existing temporal binding models, like SHRUTI, use temporal synchrony for this process.

Purpose of the Study:

  • To propose alternative mechanisms to temporal synchrony for solving the binding problem.
  • To extend and improve connectionist models by introducing short signatures.
  • To model human abilities like unification and handling multiple instantiations of logical terms.

Main Methods:

  • Developing a novel computational model using short signatures instead of temporal synchrony.
  • Simulating the model with neuron-like computations to verify feasibility.
  • Exploring biologically plausible alternatives for neural binding.

Main Results:

  • The proposed short signature mechanism offers a viable alternative to temporal synchrony.
  • The extended model successfully incorporates unification and multiple logical term instantiation.
  • Simulations demonstrate the feasibility of the new binding mechanism.

Conclusions:

  • Short signatures provide a biologically plausible and computationally effective solution to the binding problem.
  • This approach enhances neural network models, improving their ability to represent complex information.
  • The findings offer new insights into neural computation and cognitive processes.