NeuroEmoDynamics: A Neurocognitive Profiling Quantum Spiking Neural Network (QSNN)

This project is created purely out of interest and curiosity.

The newest version is much more stable and predicts emotions more accurately than the previous one, which was more of a proof of concept. The new version is also much more modular, allowing for easier experimentation with different configurations and profiles.

Hint: 4 Epochs is enough to get a good result, but you can train it for longer if you want (Maybe add some dropouts etc.).

NeuroEmoDynamics is a biologically inspired quantum spiking neural network (QSNN) designed to simulate complex cognitive and emotional states. The project uses by own QLIF-Neurons, by using feedback and cross-connections among key brain regions, including the prefrontal cortex, amygdala, hippocampus, thalamus, and striatum, while integrating text-based emotion analysis.

Features

Biologically Inspired Architecture

• Prefrontal Cortex (PFC): Receives sensory inputs and modulates downstream regions.
• Downstream Regions:
  • Separate LIF layers for the amygdala, hippocampus, and thalamus.
  • Feedback mechanisms to regulate overall processing.
• Cross-Connectivity:
  • Integrates information across amygdala, hippocampus, and thalamus to simulate interdependent emotional and cognitive processes.
• Striatum Integration:
  • A final layer that aggregates signals for state estimation and emotional decision-making.

QLIF-Neuron Model

• Uses an enhanced Quantum Leaky Integrate-and-Fire (QLIF) model with:
  • Neuromodulation mechanisms (serotonin, dopamine, norepinephrine) that regulate different regions.
  • Adaptive thresholds and noise integration.
  • Cross-neural interaction to simulate brain dynamics.
  • See QLIF-Neurons for more information.

Text-Based Emotion Analysis

• A separate text-processing branch using:
  • Embedding layer and LSTM-based encoder.
  • Fusion mechanism to integrate linguistic input with cognitive-emotional processing.
  • Gating mechanisms to allow text to override sensory/emotional biases (e.g., "I feel happy even though I am depressed").

Synthetic Data Generation

• Generates synthetic sensory input and reward signals based on different psychological profiles:
  • Healthy
  • Depressed
  • Anxious
  • Impulsive
  • Resilient

Visualization Tools

• Run the model_neuron_plot.py script with a trained model, then open interactive_viz.html to visualize (Limited to 200 neurons per region for performance reasons.).

Installation

# Clone the repository
git clone https://github.com/YourRepo/NeuroEmoDynamics.git
cd NeuroEmoDynamics

# Install dependencies
pip install -r requirements.txt

How It Works

1. Profile-Based Neuromodulation

• Each psychological profile (depressed, anxious, etc.) is assigned a neuromodulatory signature.
• These modulate serotonin, dopamine, and norepinephrine levels, influencing:
  • Emotional response
  • Cognitive flexibility
  • Attention regulation

2. Text Processing & Emotional Override

• The text encoder extracts linguistic features.
• Gating layers determine if textual information can override the emotional profile.
• Example effects:
  • A depressed profile reading "I feel strong" may shift towards joy.
  • An anxious profile reading "Everything is fine" may reduce fear responses.

3. Spiking Network & Decision Making

• PFC processes sensory input and modulates emotional states.
• Feedback from the amygdala, hippocampus, and thalamus refines responses.
• Striatum integrates all signals to produce final outputs (emotion classification).

Roadmap

• Improve text-based emotion influence.
• Optimize the LIF neuron feedback mechanisms.
• Extend visualization tools to include real-time simulation.
• Experiment with reinforcement learning for adaptive emotion modulation.

References

This project uses the dataset emotion from Hugging Face.

@inproceedings{saravia-etal-2018-carer,
  title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
  author = "Saravia, Elvis  and
    Liu, Hsien-Chi Toby  and
    Huang, Yen-Hao  and
    Wu, Junlin  and
    Chen, Yi-Shin",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  month = oct # "-" # nov,
  year = "2018",
  address = "Brussels, Belgium",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/D18-1404",
  doi = "10.18653/v1/D18-1404",
  pages = "3687--3697"
}

License

AGPL-3.0 License