Astromorphic Transformers

Bio-inspired neural architectures incorporating astrocyte-neuron interactions for efficient sequence processing

Astromorphic Transformers: Astrocyte-Enhanced Neural Architecture

This research project develops astromorphic transformers, a novel neural network architecture that incorporates the critical role of astrocytes - brain cells that constitute more than 50% of human brain cells - in neuromorphic computing. The work bridges the gap between biological neural networks and artificial intelligence hardware.

Research Overview

Lead Student Researcher, 2023–Present. Developed a neuromorphic algorithmic framework for transformer models with astrocytic memory, enabling biologically inspired sequence learning that mimics actual brain functionality.

Key Research Contributions

🧠 Bioplausible Modeling: Integration of Hebbian and presynaptic plasticities in neuron-astrocyte networks

⚡ Enhanced Performance: Improved accuracy and learning speed in sentiment analysis and image classification tasks

🔬 Novel Architecture: First comprehensive incorporation of astrocytic characteristics in transformer self-attention mechanisms

đź“Š Superior Results: Better perplexity on WikiText-2 dataset compared to conventional models

Technical Innovations

The project leverages astrocytic non-linearities and memory retention to improve long-range dependency processing:

  1. Neuron-Synapse-Astrocyte Interactions: Bioplausible modeling of cellular interactions
  2. Self-Attention Enhancement: Mapping neuron-astrocyte computations to attention mechanisms
  3. Temporal Memory: Astrocytic memory for improved context retention
  4. Energy Efficiency: Bio-inspired optimization for reduced computational complexity

Experimental Validation

Datasets Tested:

  • IMDB (sentiment classification)
  • CIFAR-10 (image classification)
  • WikiText-2 (natural language generation)

Performance Gains:

  • Improved accuracy across all tested datasets
  • Enhanced learning speed and convergence
  • Better generalization and stability

Publications

  • IEEE Transactions on Cognitive and Developmental Systems (2025) - “Delving Deeper Into Astromorphic Transformers”
  • OpenReview (2024) - “RMAAT: A Bio-Inspired Approach for Efficient Long-Context Sequence Processing in Transformers”

Research Team

Principal Investigator: Md Zesun Ahmed Mia
Collaborators: Malyaban Bal, Abhronil Sengupta
Institution: Pennsylvania State University
Funding: Lead Student Researcher role, 2023–Present

Impact and Future Directions

This work advances the field of neuromorphic computing by demonstrating how astrocytic characteristics can enhance computational models, paving the way for more efficient and biologically realistic AI systems.

References