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

Electrical Synapses01:28

Electrical Synapses

8.3K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
8.3K
Synaptic Signaling01:09

Synaptic Signaling

5.5K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
5.5K
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
Neuronal Communication01:28

Neuronal Communication

818
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...
818
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.5K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
1.5K
The Synapse02:47

The Synapse

124.6K
Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
124.6K

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Bayesian modeling of multiregion brain cell count data.

eLife·2025
Same author

Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing.

Scientific reports·2025
Same author

Competition effects regulating the composition of the microRNA pool.

Journal of the Royal Society, Interface·2025
Same author

GlyT2-Positive Interneurons Regulate Timing and Variability of Information Transfer in a Cerebellar-Behavioral Loop.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Relating Human Error-Based Learning to Modern Deep RL Algorithms.

Neural computation·2024
Same author

Beyond the limitations of any imaginable mechanism: Large language models and psycholinguistics.

The Behavioral and brain sciences·2023

Related Experiment Video

Updated: Jun 17, 2025

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

10.3K

Signatures of Bayesian inference emerge from energy-efficient synapses.

James Malkin1, Cian O'Donnell1,2, Conor J Houghton1

  • 1Faculty of Engineering, University of Bristol, Bristol, United Kingdom.

Elife
|August 6, 2024
PubMed
Summary

Neural circuit performance is limited by unreliable synapses. This study reveals a trade-off between synaptic reliability costs and artificial neural network performance, linking energy efficiency to Bayesian inference principles.

Keywords:
Bayesian inferencecomputational neuroscienceenergy efficiencyneurosciencenonesynaptic plasticity

More Related Videos

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.5K
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

8.1K

Related Experiment Videos

Last Updated: Jun 17, 2025

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

10.3K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.5K
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

8.1K

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Biophysics

Background:

  • Biological synaptic transmission is inherently unreliable, potentially limiting neural circuit function.
  • Mechanisms enhancing synaptic reliability, such as increased vesicle release probability, incur significant energy costs.
  • Understanding this energy-reliability trade-off is crucial for both biological and artificial neural systems.

Purpose of the Study:

  • To investigate the energetic costs associated with biological synaptic reliability mechanisms.
  • To explore the performance-reliability cost trade-off in artificial neural networks (ANNs) with stochastic synapses.
  • To identify potential links between synaptic efficiency, network performance, and Bayesian inference.

Main Methods:

  • Examined four biophysical mechanisms for increasing synaptic reliability and their associated energetic costs.
  • Embedded these energetic costs into ANNs with trainable stochastic synapses.
  • Trained the ANNs on standard image classification tasks to evaluate performance and reliability trade-offs.

Main Results:

  • ANNs demonstrated a clear trade-off between computational performance and the energetic cost of synaptic reliability.
  • Optimized networks predicted that synapses with lower variability exhibit higher input firing rates and lower learning rates.
  • These findings align with existing experimental data and emerge even when synapse statistics are inferred via Bayesian inference.

Conclusions:

  • A formal, theoretical link was established between the performance-reliability cost trade-off and Bayesian inference.
  • This suggests that evolution may have favored energy-efficient neural schemes that incidentally implement Bayesian inference.
  • Alternatively, energy-efficient synapses might exhibit Bayesian inference signatures without explicit Bayesian computation.