ORGANICS.AI
BIOCOMPUTING SYSTEM
Building a biocomputing system by combining biological neuronal networks with in-silico artificial intelligence interfaces. Coupling biological neuronal networks, BNN's, with models of spiking neuronal networks, SNN's.
BIOLOGICAL COMPUTING
WHAT IT IS
Nature has created in eons sophisticated complex computing systems, which we call biological computing
The human brain is the example of the most sophisticated computational systems
It is principally an open question if biocomputing is more then just our known computers
On this website we will give answers and hints for these mankind questions.
THE NEED
Biological computation is multiscale in that sense that it uses a multitude of mechanisms of learning from the molecular level to the tissue level.
Von Neumann computation is limited and not multiscale.
Quantum computers almost incorporate von Neumann computation with higher computational speed and producing real random numbers.
Neuromorphic computation is also not multiscale.
->> Biological computation is a different approach to computation, it is mortal computing, which depends on its substrate.
See also (Milinkovic and Aru 2025)
ADVANTAGES
Biological networks offer unprecedented learning algorithms.
It mimics the multidimensional and multiscaling biological learning.
BNNs allow highly effective learning with fewer learning examples, and faster algorithms.
Flexibility and modality are also advantage of BNN’s.
BNNs are a most energy efficient intelligent system, by a factor of 1:100 Mio. in comparison to in-silico AI superintelligence.
ORGANICS.AI - THE IDEA
Communication interface between a BNN-Modul and AI
A biological neuronal network module is the elementary building block of the Organics.AI biocomputing system. The communication between a computer and a biocomputing system is the bottleneck to building efficient solutions.
AI-controlled biocomputing system interfaces
Simulations of spiking neuronal networks (SNN) combined with AI analyses are the main tools for such a communication interface.
It delivers an optimal interface to the BNN, for learning and interpretation.
The BNN is the microchip of Organics.AI.
Microelectrode-Array Technology – MEA – The Building Blocks of BNNs
MICROELECTRODE ARRAYS
MEAs are plated with neuronal cell cultures
They form spontaneously electrically active networks
Action potential of several neurons are detected and saved as spikes
Those spike are processed by the control computer.
NEURONAL NETWORK CULTURES
Form spontaneous neuronal networks
10.000 to 100.000 cells with several thousands synaptic connections per each neuron.
Primary cell cultures or stem-cell derived cell cultures.
Viable for weeks or months.
ADVANTAGES OF THE ORGANICS.AI BIOCOMPUTING SYSTEM
The coupling of BNN and SNN allows the development of unprecedented algorithms for:
- Learning mechanisms
- Improved stimulation and recording paradigms
- Superior solutions for specific applications.
POTENTIAL APPLICATIONS
Organics.AI
Delivers turnkey solutions for biocomputing in
- Drug Discovery
- Feedback – Loop Solutions
- Classification and Pattern Recognition
- Scientific Research
DRUG DISCOVERY
The Organics.AI system would mean a paradigm change in drug discovery
Significant improvement of the classification of activity patterns
Increase of Productivity
New test paradigms are possible. Nanotesting of test compounds.
New Disease Models
For Dementia, Depression, Schizophrenia, Sleep and Cognition
PHYSICAL EMBODIMENT
First Application – Balancing of a Stick – Inverse Pendulum
First Organics.AI application to demonstrate the biocomputing capacities.
PHYSICAL EMBODIMENT
Learning of physical behavior
Organics.AI can learn the system dynamics of
complex systems. (As the three body problem)
Stick balancing
Applications in intuitive physics
Walking of robots. Packing of a shopping cart and so forth.
Brain Computer Interfaces - BCI
Applications in nuclear fusion
Control of plasma generation with short term predictions.
IMAGE RECOGNITION – CLASSIFICATION TASK
First Application – In Pattern Recognition
Demonstration that a biocomputer can also solve classification tasks.
Problem: Number of stimulation electrodes
Solution: HDMEA and Organoids.
MEDICAL RESEARCH
Consciousness research by Tonino and Edelman
Measures of consciousness, such as the perturbational complexity index, PCI, can be estimated with AI functions.
Consciousness science will become a quantitative science
We can precisely measure consciousness levels with ideas from the IIT, integrated information theory, allowing us to optimize them.
Consciousness is one of the blind spots in AGI research but cause difficult ethical questions
The combination of Organics.AI with in-silico AI will create artificial general intelligence