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

3.1K
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...
3.1K
Propagation of Action Potentials01:23

Propagation of Action Potentials

10.5K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
10.5K
Operational Amplifiers01:17

Operational Amplifiers

2.1K
The operational amplifier, often referred to as an op-amp, is a multifaceted building block of a circuit. This electronic component functions like a voltage-controlled voltage source and can also be used to create a voltage- or current-controlled current source. The design of an operational amplifier enables it to execute mathematical operations when external components like resistors and capacitors are linked to its terminals. An op-amp has the capacity to sum signals, amplify a signal,...
2.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

451
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
451
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.0K
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....
4.0K
Integration of Synaptic Events01:28

Integration of Synaptic Events

5.2K
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...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Modulating backbone flexibility in hydroxamate siderophores for improved iron chelation and peptide nucleic acid delivery into bacteria.

The Biochemical journal·2026
Same author

DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset.

Scientific data·2026
Same author

Graph Attention Networks for Detecting Epilepsy From EEG Signals Using Accessible Hardware in Low-Resource Settings.

IEEE open journal of engineering in medicine and biology·2026
Same author

Influence of Post-Processing on S-Phase Formation During Plasma Nitriding of Additively Manufactured Inconel 939.

Materials (Basel, Switzerland)·2026
Same author

Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.

Patterns (New York, N.Y.)·2025
Same author

Evaluation of an event-driven 3FI ASIC for spectroscopic X-ray detection with synchrotron radiation.

Journal of synchrotron radiation·2025

Related Experiment Video

Updated: Mar 7, 2026

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

10.9K

Operational manifolds in spiking neural networks.

Szymon Mazurek1,2,3, Jakub Caputa1, Piotr Maj4,5

  • 1Department of Computer Science, Electronics and Telecommunications, AGH University of Krakow, Krakow, Poland.

Frontiers in Neuroscience
|March 6, 2026
PubMed
Summary

Spiking Neural Networks (SNNs) achieve energy efficiency by optimizing neuron hyperparameters and state handling. Understanding the operational manifold guides selection for stable, accurate, and robust neuromorphic systems.

Keywords:
inference-time state handlingleaky integrate-and-fire parametersneuromorphic computingneuron hyperparametersoperational manifoldrobustness and stabilityspiking neural networks

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
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

7.6K

Related Experiment Videos

Last Updated: Mar 7, 2026

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

10.9K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
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

7.6K

Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking Neural Networks (SNNs) offer potential energy savings over traditional deep networks.
  • Performance and stability of SNNs are sensitive to neuron hyperparameters and inference strategies.
  • Leaky Integrate-and-Fire (LIF) models are common SNN neuron types.

Purpose of the Study:

  • To investigate the interplay between LIF neuron hyperparameters and inference policies.
  • To define and map an 'operational manifold' for balanced SNN activity and performance.
  • To identify accuracy-energy trade-offs and assess robustness under varying conditions.

Main Methods:

  • Systematic grid sweeps of membrane time constant (τm) and firing threshold (Vth) to map the operational manifold.
  • Synaptic Operation (SOP) cost estimation for quantifying inference energy efficiency.
  • Comparison of 'reset' and 'carry' membrane potential policies for state handling.
  • Analysis of robustness via input perturbations and examination of spike-train correlations.

Main Results:

  • The operational manifold represents a stable operating region for SNNs, balancing activity and performance.
  • Accuracy-energy frontiers were identified within the manifold using composite scores.
  • 'Reset' policy improved accuracy on static data, while 'carry' introduced interference in streaming data.
  • Leaving the operational manifold correlated with increased spike-train synchrony and correlations.

Conclusions:

  • Practical guidelines for selecting SNN hyperparameters and inference policies for energy efficiency and stability.
  • Correlation statistics of spike trains serve as effective, label-free indicators of SNN health and noise exposure.
  • The findings support the development of robust and efficient neuromorphic systems.