| --- |
| tags: |
| - neuroscience |
| - consciousness |
| - transformers |
| - AGI |
| - open-science |
| - simulation |
| - spiking-neural-networks |
| - Realtime |
| - NumPy |
| - SNN |
| - IIT |
| - hormones |
| widget: |
| - output: |
| url: images/DRM-H12 ОБЛОЖКА.jpg |
| text: '-' |
| base_model: baidu/ERNIE-Image |
| instance_prompt: null |
| license: cc0-1.0 |
| language: |
| - ru |
| - en |
| pipeline_tag: robotics |
| --- |
| # DRM-H12 |
|
|
| <Gallery /> |
|
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| ## Model description |
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| **DRM-H12-v2 (fix15) — Hierarchical Self‑Model of Consciousness** |
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| > Real‑time simulation of 1,156,000 neurons, 27.7M synapses, hormonal dynamics, and integrated information theory (IIT). |
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| 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. |
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| 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. |
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| --- |
|
|
| ## 🧠 Theoretical Foundations |
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| The model combines five major theories: |
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| | 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). | |
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| --- |
|
|
| ## 📊 Key Model Features |
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|
| ### 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](https://doi.org/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 |
| |
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| [Download](/vslab/DRMH12/tree/main) them in the Files & versions tab. |
