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

Neuronal Communication01:28

Neuronal Communication

1.5K
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...
1.5K
Neural Circuits01:25

Neural Circuits

1.7K
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...
1.7K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
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....
3.3K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

3.5K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
3.5K
Synaptic Signaling01:12

Synaptic Signaling

76.3K
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.
76.3K
The Synapse02:47

The Synapse

127.6K
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.
127.6K

You might also read

Related Articles

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

Sort by
Same author

Low-variance Forward Gradients using Direct Feedback Alignment and momentum.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Exploring Trade-Offs in Spiking Neural Networks.

Neural computation·2023
Same author

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Nature communications·2021
Same author

A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction.

Nature communications·2021
Same author

Constraints on Hebbian and STDP learned weights of a spiking neuron.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Interpretation of morphogen gradients by a synthetic bistable circuit.

Nature communications·2020
Same journal

Breaking the Stability-Activity-Selectivity Trilemma in Unspecific Peroxygenase through Computation-Based Cross-Regional Combinatorial Mutagenesis.

ACS synthetic biology·2026
Same journal

Sequential Plasmid Curing and Genome Editing in <i>Escherichia coli</i> Nissle 1917.

ACS synthetic biology·2026
Same journal

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026
Same journal

A Multitask Prediction Framework for CircRNAs, Drugs, and Diseases Based on Multi-View Information Integration and Graph Contrastive Learning.

ACS synthetic biology·2026
Same journal

Engineering Modular Cargo Loading Strategies for Carboxysome-Derived Protein Particles.

ACS synthetic biology·2026
Same journal

Suppression of Salmonella Effectors with CRISPRi Controls the Immune Response to Bacterial Therapies.

ACS synthetic biology·2026
See all related articles

Related Experiment Video

Updated: Sep 21, 2025

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

7.1K

Programming Molecular Systems To Emulate a Learning Spiking Neuron.

Jakub Fil1, Neil Dalchau2, Dominique Chu3

  • 1APT Group, School of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom.

ACS Synthetic Biology
|May 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces the first chemical reaction network (CRN) capable of autonomous Hebbian learning, mimicking neuron adaptation for unsupervised learning in molecular systems. This breakthrough enables systems to learn from stimuli without feedback, paving the way for synthetic biological intelligence.

Keywords:
DNA strand displacementHebbian learningautonomous learningbiochemical intelligencespiking neurons

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

11.6K
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

10.5K

Related Experiment Videos

Last Updated: Sep 21, 2025

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

7.1K
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

11.6K
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

10.5K

Area of Science:

  • Synthetic Biology
  • Computational Neuroscience
  • Chemical Kinetics

Background:

  • Hebbian theory explains neural adaptation and learning through stimulus response.
  • Unsupervised learning methods like Hebbian learning are crucial for autonomous systems.
  • Designing molecular systems with proto-intelligent behaviors is an emerging field.

Purpose of the Study:

  • To propose the first chemical reaction network (CRN) exhibiting autonomous Hebbian learning.
  • To demonstrate how molecular systems can emulate spiking neurons and learn statistical input biases.
  • To explore engineering de novo molecular learning systems using DNA-based approaches.

Main Methods:

  • Development of a minimal, thermodynamically plausible CRN for Hebbian learning.
  • Proposal of an extended CRN utilizing enzyme-driven compartmentalized reactions.
  • Implementation of neuronal dynamics using a DNA strand displacement system.

Main Results:

  • The proposed CRN successfully emulates a spiking neuron.
  • The system demonstrates the ability to learn statistical biases from incoming inputs autonomously.
  • A DNA-based system is shown to realize neuronal dynamics, supporting synthetic intelligence.

Conclusions:

  • This work provides a blueprint for creating autonomous learning in biological settings.
  • The developed CRN is a significant step towards realizing synthetic biological intelligence.
  • The findings open new avenues for bio-inspired computing and adaptive molecular systems.