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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.

Long-term Potentiation

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 Potentiation

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...

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Neural memory plasticity for medical anomaly detection.

Tharindu Fernando¹, Simon Denman¹, David Ahmedt-Aristizabal¹

¹Image and Video Research Laboratory, SAIVT, Queensland University of Technology, Australia.

Neural Networks : the Official Journal of the International Neural Network Society

|April 26, 2020

Summary

This study introduces a novel plastic neural memory access mechanism for Neural Memory Networks (NMNs). This enhanced approach improves anomaly detection in medical data by utilizing dynamic and static weights for better knowledge retrieval.

Keywords:

Abnormal EEG identification Anomaly detection MRI tumour type classification Neural Memory Networks Neural plasticity Schizophrenia risk detection

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Area of Science:

Machine Learning
Artificial Intelligence
Computational Neuroscience

Background:

Neural Memory Networks (NMNs) show promise in various AI tasks.
Current NMNs use static attention weights, limiting performance in anomaly detection.
Anomaly detection requires flexible knowledge retrieval due to data variability.

Purpose of the Study:

To propose a plastic neural memory access mechanism for NMNs.
To enhance knowledge retrieval by incorporating dynamic connection weights.
To improve performance in challenging anomaly detection tasks.

Main Methods:

Developed a novel memory access mechanism with plastic (dynamic) and static weights.
Applied the mechanism to memory read, write, and output generation.
Evaluated the model on three medical anomaly detection tasks.

Main Results:

The proposed plastic memory model significantly outperformed the state-of-the-art in all tested anomaly detection tasks.
Demonstrated the effectiveness of neural plasticity in knowledge retrieval.
Showcased the model's ability to generate sparse yet informative memory outputs.

Conclusions:

Plastic neural memory access mechanisms offer a significant advancement over static attention in NMNs.
The proposed model shows high effectiveness and flexibility for medical anomaly detection.
Neural plasticity is crucial for adaptive knowledge retrieval in complex datasets.