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

Chemical Synapses01:26

Chemical Synapses

4.8K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
4.8K
Chemical Synapses01:26

Chemical Synapses

12.1K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
12.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

436
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...
436
Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

2.3K
Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
2.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

11.1K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
11.1K
Chemical Reactions01:19

Chemical Reactions

96.6K
A chemical reaction is a process by which the bonds in the atoms of substances are rearranged to generate new substances. Matter cannot be created or destroyed in a chemical reaction—the same type and number of atoms that make up the reactants are still present in the products. Merely, the rearrangement of chemical bonds produces new compounds.
Chemical Reactions Rearrange Atoms into New Substances
A chemical reaction takes starting materials—the reactants—and changes them...
96.6K

You might also read

Related Articles

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

Sort by
Same author

PID-controller enhanced artificial β-cells.

PloS one·2026
Same author

Association of mucosal neutrophil inflammation and cytokine responses with natural and experimental pneumococcal carriage in a randomised vaccine trial using experimental human pneumococcal carriage.

Clinical immunology (Orlando, Fla.)·2025
Same author

Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population.

Nature medicine·2024
Same author

The effects of denosumab on osteoclast precursors in postmenopausal women: a possible explanation for the overshoot phenomenon after discontinuation.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2024
Same author

Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications.

Sensors (Basel, Switzerland)·2024
Same author

Experimental pneumococcal carriage in people living with HIV in Malawi: the first controlled human infection model in a key at-risk population.

Wellcome open research·2024

Related Experiment Video

Updated: Feb 25, 2026

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

3.0K

Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor.

Drew Blount1, Peter Banda2, Christof Teuscher3

  • 1Wild Me.

Artificial Life
|August 9, 2017
PubMed
Summary

Scientists developed a novel chemical neural network that learns using a reaction-based backpropagation analogue. This modular system enables adaptive learning in simulated chemical environments, paving the way for wet machine learning.

Keywords:
Chemical reaction networkcellular compartment learningerror backpropagationfeedforwardlinearly inseparable function

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

10.9K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.9K

Related Experiment Videos

Last Updated: Feb 25, 2026

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

3.0K
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
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.9K

Area of Science:

  • Biochemistry
  • Computational Neuroscience
  • Chemical Engineering

Background:

  • Natural biochemical systems exhibit complex information processing capabilities.
  • Existing artificial neural networks often require complex electronic hardware.
  • There is a need for adaptive learning systems that can operate in diverse environments.

Purpose of the Study:

  • To design and simulate a chemically implemented feedforward neural network.
  • To develop a novel chemical-reaction-based analogue of backpropagation for learning.
  • To explore the potential for general-purpose, adaptive learning in chemico.

Main Methods:

  • Simulated a chemical system with compartmentalized neurons separated by semipermeable membranes.
  • Implemented a feedforward neural network architecture.
  • Utilized a novel chemical-reaction-based learning rule analogous to backpropagation.

Main Results:

  • Successfully designed and simulated a chemically implemented feedforward neural network.
  • Demonstrated a novel chemical-reaction-based learning mechanism.
  • Showcased a modular design allowing for varied network topologies.

Conclusions:

  • The study presents a significant step towards realizing 'wet machine learning'.
  • The developed system offers a pathway for adaptive learning in embodied dynamical systems.
  • Chemically implemented neural networks hold promise for future bio-inspired computing.