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Neuroplasticity

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.

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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
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Related Experiment Video

Updated: Sep 25, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Efficient dendritic learning as an alternative to synaptic plasticity hypothesis.

Shiri Hodassman¹, Roni Vardi², Yael Tugendhaft¹

¹Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel.

Scientific Reports

|April 28, 2022

Summary

This summary is machine-generated.

Synaptic plasticity enables brain learning and machine learning through neuronal adaptation. This study demonstrates sub-dendritic adaptation for efficient deep learning, highlighting the need for novel dendritic hardware.

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Last Updated: Sep 25, 2025

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Published on: September 20, 2024

Area of Science:

Neuroscience
Machine Learning
Computational Neuroscience

Background:

Synaptic plasticity is fundamental to brain learning and machine learning, involving local neuronal adaptation.
Current experimental limitations hinder pinpointing adaptation sites within complex neuronal structures like dendrites.

Purpose of the Study:

To investigate sub-dendritic adaptation and nonlinear amplification for efficient learning.
To explore the computational potential of dendritic structures in deep learning.

Main Methods:

Implementing backpropagation and Hebbian learning on dendritic trees, inspired by experimental data.
Modeling sub-dendritic adaptation and its nonlinear amplification effects.

Main Results:

Achieved near-perfect success rates in handwritten digit recognition, demonstrating deep learning capabilities within single dendrites/neurons.
Showcased how dendritic amplification exponentially increases input interactions, enhancing learning success.

Conclusions:

Sub-dendritic adaptation and nonlinear amplification offer a powerful mechanism for efficient deep learning.
The computational demands of direct implementation necessitate the development of specialized nonlinear adaptive dendritic hardware for brain-inspired computing.