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

Neuroplasticity01:01

Neuroplasticity

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.
Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...

You might also read

Related Articles

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

Sort by
Same author

Bio-based PEDOT: nanocellulose hybrids as efficient hole-transport layers for photoelectrochemical devices.

Nanoscale·2026
Same author

Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality.

ACS applied materials & interfaces·2026
Same author

Towards a better understanding of real-life hearing impairments via electrophysiology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants.

IEEE transactions on biomedical circuits and systems·2025
Same author

Temporal recurrence as a general mechanism to explain neural responses in the auditory system.

Communications biology·2025
Same author

Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Related Experiment Video

Updated: Jun 3, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.

Carlos Zamarreño-Ramos1, Luis A Camuñas-Mesa, Jose A Pérez-Carrasco

  • 1Mixed Signal Design, Instituto de Microelectrónica de Sevilla (IMSE-CNM-CSIC) Sevilla, Spain.

Frontiers in Neuroscience
|March 29, 2011
PubMed
Summary

Neuromorphic engineers link memristor nanotechnology to spike-time-dependent-plasticity (STDP) for artificial visual cortex emulation. This framework enables asynchronous learning systems that can extract orientations from visual data.

Keywords:
STDPlearningmemristornanotechnologyneural networkspikessynapsesvisual cortex

More Related Videos

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Area of Science:

  • Neuroscience and nanotechnology intersection
  • Neuromorphic engineering applications
  • Artificial intelligence hardware development

Background:

  • Biological synapses exhibit spike-time-dependent-plasticity (STDP), a crucial learning rule.
  • Memristor devices offer potential for emulating synaptic behavior.
  • Current neuromorphic architectures require advancements for efficient learning.

Purpose of the Study:

  • To establish a link between memristor nanotechnology and STDP.
  • To develop a framework for asynchronous STDP learning neuromorphic architectures.
  • To emulate functionalities of the biological visual cortex using memristors.

Main Methods:

  • Utilizing voltage or flux-driven memristors with a behavioral macro-model.
  • Designing fully asynchronous circuit architectures with bidirectional neuron communication.
  • Investigating the impact of neuron action potential spike shapes on STDP.
  • Interconnecting neurons and memristors for large-scale spiking learning systems.
  • Extending architectures to three-terminal transistors with memristive properties.

Main Results:

  • Demonstrated emulation of a V1 visual cortex layer capable of orientation extraction.
  • Successfully learned to process visual data from a CMOS spiking retina.
  • Showcased how changing spike shapes can tune STDP learning rules.
  • Developed a framework for multiplicative STDP learning systems.

Conclusions:

  • A viable framework for asynchronous STDP learning neuromorphic architectures using memristive devices has been presented.
  • The study highlights the potential of memristors in creating artificial neural systems that mimic biological learning.
  • Future work should address memristor device non-idealities and interconnect limitations for practical implementation.