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Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
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Related Experiment Video

Updated: Jul 23, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

An optimized hierarchical attention assisted deep learning model for brain tissue classification.

Divya Sundar V S1, Vijayachamundeeswari V2

  • 1Department of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Chennai 602105, India.

Journal of Neuroscience Methods
|February 25, 2026
PubMed
Summary

This study introduces an optimized deep learning model for precise brain tissue segmentation and classification in MRI scans. The novel approach achieves high accuracy in detecting brain tissue abnormalities, improving diagnostic capabilities.

Keywords:
Brain tissue segmentationCapsule NetworkCoati optimization algorithmFused Accumulation BridgeHierarchical AttentionResidual blocks

Related Experiment Videos

Last Updated: Jul 23, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Medical Imaging Analysis
  • Deep Learning Applications
  • Neuroscience

Background:

  • Accurate brain tissue differentiation in Magnetic Resonance Imaging (MRI) is crucial for medical applications.
  • Existing methods face limitations due to inconsistencies from varied scanner types and acquisition procedures.
  • Complex brain tissue structures necessitate advanced segmentation techniques.

Purpose of the Study:

  • To develop an effective, optimized hierarchical deep learning method for detecting brain tissue anomalies.
  • To enhance the precision of brain tissue classification and segmentation in MRI.
  • To address the limitations of current methods in handling inter-scanner variability.

Main Methods:

  • Pre-processing of MRI images using min-max normalization.
  • A novel hybrid segmentation approach, Residual fused accumulated U-net bridge module (ResFAU-net), combining residual blocks, attention gates, and Fused Accumulation Bridge module.
  • Classification using the Hierarchical Attention-Based Modified Convolutional Cascaded Capsule Network (HAMC3), integrating CNNs and hierarchical attention.

Main Results:

  • The model was evaluated on the BRATS2020 dataset.
  • Performance was validated using metrics including Dice score, Intersection over Union (IoU), accuracy, precision, sensitivity, specificity, and F1-score.
  • The proposed model demonstrated strong performance across various indicators.

Conclusions:

  • The developed hierarchical deep learning model effectively segments and classifies abnormal brain tissue.
  • The model achieved a high IoU score of 96.29% and accuracy of 99.03%.
  • This approach shows significant potential for improving the detection of brain tissue abnormalities.