pyopu: Organoid Processing Unit Framework

pyopu is a Python framework for simulating and executing combinatorial optimization tasks on biological wetware (Brain Organoids) using the Holographic Tensorial Paradigm.

Features

• Agnostic Math Engine: Compile NP-Hard problems into arbitrary high-order arrays using dynamic np.einsum.
• Holographic NeoCode Paradigm: Define problems with algebraic logic and visually inspect Hamiltonian graphs.
• Interchangeable Backends: Run problems on virtual MEAs (Microelectrode Arrays) or advanced Dual-Opsin Optogenetic interfaces.
• Observer Telemetry: Extract spikes, opsin kinetics, and energy dynamics cleanly without cluttering the engine.

Defining Problems with NeoCode

pyopu uses the new NeoCode Compiler to translate logical problem definitions directly into dynamic high-order tensor arrays.

from pyopu.neocode import NeoCode, NeoTerm

prog = NeoCode(num_neobits=5)
prog.add_term(NeoTerm(weight=-20, logic=["x1", "~x3", "x4"]))
prog.draw()

The draw() method outputs a visual representation of the problem topology and algebraic Hamiltonian equation.

Quickstart

Check the examples/ directory for full implementations of the Max-Cut and 3-SAT problems using the new NeoCode abstraction.