README.md

---
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 />

## 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.