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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.2K
Entropy within the Cell01:22

Entropy within the Cell

10.6K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
10.6K
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

6.4K
The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
6.4K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.4K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.4K
PI3K/mTOR/AKT Signaling Pathway01:22

PI3K/mTOR/AKT Signaling Pathway

3.5K
The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
3.5K
The Supercomplexes in the Crista Membrane01:41

The Supercomplexes in the Crista Membrane

2.5K
The mitochondrial cristae membrane is the primary site for the oxidative phosphorylation (OXPHOS) process of energy conversion mediated through respiratory complexes I to V. These complexes have been widely studied for decades, and it has been proven that they form supramolecular structures called respiratory supercomplexes (SC). These higher-order complexes may be crucial in maintaining the biochemical structure and improving the physiological activity of the individual complexes while...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia.

bioRxiv : the preprint server for biology·2026
Same author

Mechanistic corticostriatal circuit model predicts learning-dependent fMRI dynamics and individual reward bias in humans.

bioRxiv : the preprint server for biology·2026
Same author

Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of aged APP knock-in mice.

Cell reports·2026
Same author

The representational geometry of emotional states in basolateral amygdala.

Nature neuroscience·2026
Same author

Anterior cingulate neurons display subregion-specific interaction with frontal eye fields revealed by anti-/orthodromic stimulation and resting-state imaging.

Journal of neurophysiology·2026
Same author

Long-term editing of brain circuits using an engineered electrical synapse.

Nature·2026
Same journal

Dynamic coordination and segregation mechanisms in higher cortex for parallel task processing.

Neuron·2026
Same journal

Higher-order thalamic bursts are drivers of attention control.

Neuron·2026
Same journal

Composing trajectories for rapid inference of navigational goals.

Neuron·2026
Same journal

Peri-head distance coding in the mouse brainstem.

Neuron·2026
Same journal

A two-timepoint framework for sensitive and specific single-cell activity screening.

Neuron·2026
Same journal

From first impressions to bonds: The neural dynamics of social relationships.

Neuron·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

Rapid Development of Cell State Identification Circuits with Poly-Transfection
09:21

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

1.5K

Mixed selectivity: Cellular computations for complexity.

Kay M Tye1, Earl K Miller2, Felix H Taschbach3

  • 1Salk Institute for Biological Studies, La Jolla, CA, USA; Howard Hughes Medical Institute, La Jolla, CA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, San Diego, CA, USA.

Neuron
|May 10, 2024
PubMed
Summary
This summary is machine-generated.

This study explains how neurons achieve mixed selectivity, responding to multiple variables. Nonlinear mixed selectivity in neural circuits provides flexibility, with gating mechanisms dynamically managing information transmission.

Keywords:
braincircuitscodingcognitioncomputationsgatingmixed selectivityneuromodulationneuronoscillations

More Related Videos

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.7K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K

Related Experiment Videos

Last Updated: Jun 26, 2025

Rapid Development of Cell State Identification Circuits with Poly-Transfection
09:21

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

1.5K
Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.7K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Neural Circuits

Background:

  • Mixed selectivity in neurons, where individual neurons respond to multiple stimuli, has been explored computationally.
  • Understanding the biological mechanisms underlying mixed selectivity is crucial for explaining neural computation and flexibility.

Purpose of the Study:

  • To provide a biologically grounded, implementational-level mechanistic explanation for mixed selectivity in neural circuits.
  • To define and differentiate between pure, linear, and nonlinear mixed selectivity.
  • To explore how these properties arise in simple neural circuits and their implications for information processing.

Main Methods:

  • Defining and categorizing mixed selectivity based on response properties (linear vs. nonlinear).
  • Analyzing how simple neural circuits can generate these distinct forms of mixed selectivity.
  • Investigating the role of high-dimensional representations enabled by nonlinear mixed selectivity.

Main Results:

  • Neurons exhibiting mixed selectivity respond to multiple statistically independent variables.
  • Linear mixed selectivity involves responses as a weighted sum of variables; nonlinear mixed selectivity does not.
  • Nonlinear mixed selectivity creates high-dimensional neural representations, offering flexibility to downstream circuits.

Conclusions:

  • Nonlinear mixed selectivity provides significant flexibility to neural computations.
  • Gating mechanisms, such as oscillations and neuromodulation, are essential for managing the complexity of mixed selectivity and dynamically selecting information for readout circuits.