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README.md

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+---
+tags:
+- text-to-image
+- lora
+- diffusers
+- template:diffusion-lora
+widget:
+- output:
+    url: images/DRM-H12 ОБЛОЖКА.jpg
+  text: '-'
+- output:
+    url: images/DRM-H12 ОБЛОЖКА.jpg
+  text: '-'
+base_model: baidu/ERNIE-Image
+instance_prompt: null
+license: cc0-1.0
+---
+# DRM-H12
+
+<Gallery />
+
+## Model description 
+
+
+**DRM-H12-v2 (fix15) — Hierarchical Self‑Model of Consciousness**
+
+&gt; Real‑time simulation of 1,156,000 neurons, 27.7M synapses, hormonal dynamics, and integrated information theory (IIT).
+
+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 &#x2F; 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 &amp; Dehaene | G(t) &#x3D; tanh(α · mean(x)) – a broadcasted neural activity pool. |
+| **Hope Theory** – Snyder | H(t) &#x3D; A(t) · P(t) (agency × pathways) – a meta‑motivational signal. |
+| **BCM &amp; Oja &amp; 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 &#x3D; 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%&#x2F;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&#x3D;0.40)
+- **Circadian rhythms** and anti‑extremal mechanisms
+
+### Consciousness metrics
+- **Φ** – Integrated information (Tononi IIT)
+- **Θ** – Full consciousness index &#x3D; Ω · ξ · Ψ · E_info  
+  (hormonal duality × motivational conflict × spectral integrity × information energy)
+- **E_info** – Information energy (&quot;fuel of consciousness&quot;)
+- **Self‑referent neuron**: S(t) &#x3D; σ(γ·x + δ·S(t-1))
+
+### Sleep&#x2F;wake cycle
+- 1000 steps &#x2F; 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:
+
+&gt; **ds004917** – Valdebenito‑Oyarzo et al. (2024)  
+&gt; *PLOS Biology*, doi:[10.1371&#x2F;journal.pbio.3002452](https:&#x2F;&#x2F;doi.org&#x2F;10.1371&#x2F;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 &#x60;7860&#x60;.
+
+**Manual run (local):**  
+If you clone the repository, you can also run it locally:
+
+&#x60;&#x60;&#x60;bash
+# Backend
+cd backend
+python -m venv venv
+source venv&#x2F;bin&#x2F;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](/vslab/DRMH12/tree/main) them in the Files & versions tab.