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

Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.9K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
1.9K
Understanding Memory01:19

Understanding Memory

1.3K
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
1.3K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.6K
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....
3.6K
Neuronal Communication01:28

Neuronal Communication

2.9K
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...
2.9K
Neuroplasticity01:01

Neuroplasticity

1.5K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
1.5K

You might also read

Related Articles

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

Sort by
Same author

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

Cell reports·2026
Same author

Decomposing the modulation of interactions between neuronal populations.

bioRxiv : the preprint server for biology·2026
Same author

The representational geometry of emotional states in basolateral amygdala.

Nature neuroscience·2026
Same author

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe.

Neuron·2026
Same author

Toward a general framework for kinematic coding. reply to comments on "kinematic coding: Measuring information in naturalistic behaviour".

Physics of life reviews·2026
Same author

Contribution of spike timing to the neural code: from fast to slow timescales.

Biological cybernetics·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: Jan 8, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.4K

Neural population activity for memory: Properties, computations, and codes.

David Dupret1, Stefano Fusi2, Stefano Panzeri3

  • 1Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Neuron
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

Understanding brain memory involves analyzing neural population spiking activity. This study explores trade-offs in neural codes for efficient memory function.

Keywords:
codescomputationsmemoryneural population activity

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K
Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
10:19

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

Published on: March 31, 2016

8.5K

Related Experiment Videos

Last Updated: Jan 8, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.4K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K
Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
10:19

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

Published on: March 31, 2016

8.5K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Memory relies on neural population spiking activity patterns across acquisition, retention, and retrieval stages.
  • The precise link between population activity features and memory properties, computations, and codes remains unclear.
  • Existing research often investigates memory stages separately, limiting a holistic understanding of neural coding.

Purpose of the Study:

  • To synthesize recent advances in memory research from a brain network physiology perspective.
  • To map memory properties and computations to population-activity code features.
  • To propose that brain memory circuits balance conflicting demands on population codes.

Main Methods:

  • This perspective synthesizes findings from recent studies on memory and neural population activity.
  • It employs a viewpoint of brain network physiology to analyze memory circuits.
  • The approach focuses on identifying trade-offs within population codes.

Main Results:

  • Recent advances offer insights into the relationship between neural population activity and memory.
  • Brain memory circuits likely implement trade-offs to manage conflicting requirements for population codes.
  • A "safe zone" in population-activity space is proposed for efficient neuronal circuit operation.

Conclusions:

  • Mapping memory computations to population-activity codes requires understanding neural circuit trade-offs.
  • Investigating these trade-offs is crucial for both fundamental and translational memory research.
  • Identifying operational "safe zones" can guide the understanding of efficient neuronal circuit function in memory.