This project implements a biologically inspired computational model of consciousness, integrating multiple neurocognitive theories into a single running system.
It is not a classical ML model but a dynamical neural simulation with synaptic plasticity (STDP, Oja, BCM), structural plasticity, a hormonal system with circadian rhythms, and a hierarchical self‑model (HSM) with predictive processing.

The simulation is written in Python (FastAPI + NumPy + Numba) and visualized via a Next.js / React front‑end** with real‑time charts and control panels.

🧠 Theoretical Foundations

The model combines five major theories:

Theory	Implementation in DRM-H12-v2
Integrated Information Theory (IIT) – Tononi	Φ(t) computed from network partitions, influences consciousness index Θ.
Global Workspace Theory – Baars & Dehaene	G(t) = tanh(α · mean(x)) – a broadcasted neural activity pool.
Hope Theory – Snyder	H(t) = A(t) · P(t) (agency × pathways) – a meta‑motivational signal.
BCM & Oja & STDP	Tri‑factor synaptic plasticity: Hebbian, homeostatic, and spike‑timing dependent.
Predictive processing + Hierarchical Self‑Model (HSM)	Two‑level self (bodily S1, abstract S2) with prediction errors and reflection depth D(t).

📊 Key Model Features

Neural layer

1,156,000 neurons placed on a ring topology (RING_HALF = 5000)
24 synapses per neuron (sparse, float32) → ~27.7 million connections
Spiking activity with adaptive thresholds

Plasticity

STDP (spike‑timing dependent plasticity)
Oja’s rule (weight normalisation)
BCM (Bienenstock‑Cooper‑Munro) with sliding threshold
Structural plasticity – synapse growth and pruning (up to 13%/day)

Hormonal system (10 hormones)

DA (dopamine), 5‑HT (serotonin), NE (norepinephrine), MLT (melatonin), CORT (cortisol), OXT (oxytocin), EPI (epinephrine), LEP (leptin), T (testosterone), GHR (growth hormone)
5 antagonistic pairs with product balance (PAIR_MIDPOINT=0.40)
Circadian rhythms and anti‑extremal mechanisms

Consciousness metrics

Φ – Integrated information (Tononi IIT)
Θ – Full consciousness index = Ω · ξ · Ψ · E_info
(hormonal duality × motivational conflict × spectral integrity × information energy)
E_info – Information energy ("fuel of consciousness")
Self‑referent neuron: S(t) = σ(γ·x + δ·S(t-1))

Sleep/wake cycle

1000 steps / day, 15% sleep (150 steps)
Memory consolidation (weak synapses reset, strong protected)
Neurogenesis boost during sleep (×1.8)

📁 Dataset Acknowledgment – Human Data Used

This simulation was calibrated using real human neurobehavioral data.

We used the Probability Decision‑making Task with ambiguity dataset from OpenNeuro:

> ds004917 – Valdebenito‑Oyarzo et al. (2024)
> PLOS Biology, doi:10.1371/journal.pbio.3002452

The empirical EEG and behavioural responses under ambiguity informed:

the tuning of uncertainty‑related neuromodulators (norepinephrine, dopamine),
the structural plasticity rates,
and the parameters of the predictive self‑model (fix15).

Please cite both the model and the original dataset when reusing or extending this work.

🚀 How to Run This Space

This Space uses Docker to run the full stack (Python backend + static frontend build).
When you open the Space, the simulation will automatically start at the exposed port `7860`.

Manual run (local):
If you clone the repository, you can also run it locally:

```bash

Backend

cd backend python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows pip install -r requirements.txt python main.py

Frontend (in another terminal)

npm install npm run dev

Download model

Download them in the Files & versions tab.

vslab/DRMH12

DRM-H12

Model description