Can we apply principles from electronics to understand how the brain computes? This paper explores the pros and cons of analog and digital computation, proposing that the most efficient systems use a hybrid approach. The authors argue that maximum efficiency requires distributing information and processing resources over many wires with optimized signal-to-noise ratios, mirroring the hybrid, distributed architecture of the human brain. By comparing analog and digital methods, the study suggests that neither approach alone is optimal for resource utilization. Instead, a mixed-mode computation combining analog and digital elements offers the greatest efficiency. The key to this efficiency lies in distributing information across numerous pathways while maintaining an optimal balance between signal strength and background noise. Ultimately, this research proposes that the brain likely employs a hybrid computational strategy to achieve remarkable efficiency. This perspective sheds light on how the human brain, despite consuming only 12 watts, can perform complex tasks. The hybrid and distributed nature of its architecture might be key to its power efficiency.
Published in Neural Computation, a journal focusing on computational neuroscience and machine learning, this paper directly aligns with the journal's scope. It explores the intersection of electronics and neurobiology, offering a computational perspective on brain function that is highly relevant to the journal's audience.