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.
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The brain's hardware limitations, like slow and stochastic neurons, may actually be advantages. These properties seem perfectly suited for probabilistic inference algorithms that rely on sampling—a task that requires explicit, computationally-intensive random number generation in digital systems. Hardware and algorithm are likely co-designed.
Digital computers have separate units for processing (CPU) and memory (RAM). In biological computation, this distinction dissolves. The strength and pattern of connections between neurons *is* the memory, and the electrical firing (spiking) across these same connections *is* the processing.
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.
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.
New artificial neurons operate at the same low voltage as human ones (~0.1 volts). This breakthrough eliminates the need for external power sources for prosthetics and brain interfaces, paving the way for seamless, self-powered integration of technology with the human body.
While today's computers cannot achieve AGI, it is not theoretically impossible. Creating a generally intelligent system will require a new physical substrate—likely biological or chemical—that can replicate the brain's enormous, dynamic configurational space, which silicon architecture cannot.
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.
There's no universal bioreactor setting for 3D tissue models. Each tissue type has unique biological needs. For instance, neural cells require minimal shear stress and low oxygen, whereas liver cells need rigorous perfusion flow to maintain metabolic competence, mandating highly tailored process design for each model.
