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Neuroplasticity

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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.

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...

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Related Experiment Video

Updated: Sep 12, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep

Moein Khajehnejad^1,2, Forough Habibollahi¹, Alon Loeffler¹

¹Cortical Labs, Melbourne, Australia.

Cyborg and Bionic Systems (Washington, D.C.)

|August 5, 2025

Summary

This summary is machine-generated.

Live neural cultures in DishBrain show remarkable learning efficiency, outperforming deep reinforcement learning (RL) algorithms in a game simulation. This highlights the superior sample efficiency of biological neural networks compared to artificial intelligence.

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Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

Area of Science:

Neuroscience
Artificial Intelligence
Computational Biology

Background:

Investigating in vitro neural systems provides insights into complex network dynamics.
Live neural cultures integrated with technology offer novel research platforms.
Understanding neural plasticity is key to deciphering learning mechanisms.

Purpose of the Study:

To analyze network dynamics in live neural cultures during gameplay.
To compare the learning efficiency of biological neural systems with deep reinforcement learning (RL) algorithms.
To introduce a framework for comparing biological and artificial neural network performance.

Main Methods:

Utilized DishBrain, integrating live neural cultures with multi-electrode arrays in closed-loop game environments.
Analyzed neural activity by embedding spiking data into lower-dimensional spaces.
Compared performance of neural cultures and RL algorithms (DQN, A2C, PPO) in a Pong simulation.

Main Results:

Distinguished between spontaneous and gameplay-driven neural activity patterns.
Observed dynamic changes in neural connectivity, indicating sample-efficient plasticity.
Biological neural cultures demonstrated superior performance over deep RL algorithms in limited sample conditions.

Conclusions:

In vitro neural systems exhibit high sample efficiency in learning and adaptation.
Biological neural networks offer a valuable benchmark for artificial intelligence development.
DishBrain facilitates real-time monitoring and manipulation of neural network dynamics.