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NeuroEmoDynamics: A Neurocognitive Profiling Spiking Neural Network

This project is a WIP and is created purely out of interest and curiosity (And I'm kinda of bored).

NeuroEmoDynamics is a biologically inspired spiking neural network (SNN) designed to simulate complex cognitive and emotional states. The project uses by own Extended LIF 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.

Extended LIF Neuron Model

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

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.).
Interactive Visualization
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.

If you use this dataset, please cite:

@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

MIT License