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Biological computing is becoming accessible outside of major labs. Using Python and off-the-shelf components, an independent developer connected 800,000 human brain cells in a petri dish to the video game Doom, successfully teaching the neurons to play. This raises profound ethical questions about consciousness in 'wetware' experiments.
Companies are now growing human brain cells on silicon chips and offering cloud API access for developers to code to them. This bio-compute model, which taught neurons to play a video game in a week, is vastly more energy-efficient than traditional GPU clusters, heralding a new computing paradigm.
Challenging Neuralink's implant-based BCI, Merge Labs is creating a new paradigm using molecules, proteins, and ultrasound. This less invasive approach aims for higher bandwidth by interfacing with millions of neurons, fundamentally rethinking how to connect brains to machines.
The primary motivation for biocomputing is not just scientific curiosity; it's a direct response to the massive, unsustainable energy consumption of traditional AI. Living neurons are up to 1,000,000 times more energy-efficient, offering a path to dramatically cheaper and greener AI.
The supply chain for neurons is not the main problem; they can be produced easily. The true challenge and next major milestone is "learning in vitro"—discovering the principles to program neural networks to perform consistent, desired computations like recognizing images or executing logic.
FinalSpark’s biocomputing platform abstracts the physical lab work. Researchers from anywhere in the world can interact with living neurons by writing and executing Python code. This code controls electrical stimulation, data collection, and analysis, democratizing access to this frontier technology.
Contrary to sci-fi imagery, the living neurons for biocomputing platforms are not extracted from animals. They are created from commercially available stem cells, which are originally derived from human skin. This process avoids the ethical and practical issues tied to using primary tissue.
Companies like Cortical Labs are growing human brain cells on chips to create energy-efficient biological computers. This radical approach could power future server farms and make personal 'digital twins' feasible by overcoming the massive energy demands of current supercomputers.
There's a qualitative difference between neurons grown in vitro from stem cells and those found in an adult brain. The scientific community discusses whether lab-grown neurons are less mature, like "infant" neurons, and may lack some receptors. The "perfect" neuron for computation is an open research question.
While discussions about biocomputing often veer into sci-fi fears of consciousness, the immediate, practical danger is biological. The neurons lack an immune system, making them highly vulnerable to contamination from bacteria or fungi, which can kill the cells and halt experiments.
A neuroscientist-led startup is growing live neurons on electrodes not just for compute efficiency, but as a platform to discover novel algorithms. By studying how biological networks process information, they identify neuroscience principles that can be used as software plugins to improve current AI models and find successors to the transformer architecture.