Why does an ant, with a brain smaller than a grain of sand, find the shortest path better than a human engineer?
Richard Feynman didn’t learn about ants from a textbook. He learned by sitting on his bathroom floor with a sugar cube and a stopwatch. What he discovered wasn’t just biology—it was a biological supercomputer solving the \.
As traditional computer chips reach their physical limits and artificial intelligence demands more energy than ever, University of Missouri researchers are rethinking how computers work by taking cues from the human brain. The timing is critical. Energy use from AI data centers is projected to double by the end of the decade, raising urgent questions about sustainability.
The solution may lie in neuromorphic computing, an approach that reimagines computer hardware to process information more like biological neural networks rather than conventional chips.
“One of the brain’s greatest advantages is its efficiency,” Suchi Guha, a professor of physics in Mizzou’s College of Arts and Science, said. “It performs incredibly complex tasks using about 20 watts of power—roughly the same as an old light bulb. By comparison, today’s computer architecture is extremely energy-intensive.”
Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma-and dendrite-targeting inhibition and realistic connectivity constraints. By combining experimentally derived BTSP and Hebbian rules, dendrites compute local error proxies via E/I mismatch, supporting gradient-based deep learning during simultaneous bottom-up and top-down signaling.
What if the human brain could be mapped, simulated… and eventually run like software?
Scientists have already mapped a single cubic millimeter of the human brain, generating a staggering 1.4 petabytes of data. But that’s just the beginning.
In this video, we break down:
The rise of connectomics and full brain mapping
How AI reconstructs neurons from petavoxel-scale data
Why a brain map alone isn’t enough to recreate intelligence
The emergence of digital brain twins
And how models like ZAPBench are predicting brain activity like a weather forecast.
From the complete neural wiring of a fruit fly to simulations like OpenWorm, we are entering an era where biology meets computation.
This isn’t science fiction anymore. It’s engineering.
Artificial Neurons That Talk to the Brain? A Major Breakthrough in Neurotechnology
What if machines could communicate directly with your brain?
Scientists at Northwestern University have developed *printed artificial neurons* that can interact with real brain cells—sending signals that closely mimic natural neural activity. This breakthrough could redefine how we treat neurological disorders and build the next generation of energy-efficient AI systems.
In this video, we explore how these artificial neurons work, how they were tested on real brain tissue, and why this discovery could lead to revolutionary technologies like brain-machine interfaces and neuromorphic computing.
🔬 *What you’ll learn:*
How artificial neurons mimic real brain signals
Why traditional computing struggles with energy efficiency
The role of advanced materials like graphene and MoS₂
How this technology could restore vision, hearing, or movement
What neuromorphic computing means for the future of AI
🚀 *Why this matters:*
In a world where technology and biology converge at an accelerating pace, a new era of self-improvement is emerging — biohacking. This once-niche movement has transformed into a global phenomenon, attracting everyone from Silicon Valley executives to amateur enthusiasts. The promise? To optimize the human mind and body beyond natural limits using a blend of science, lifestyle adjustments, and cutting-edge technology.
But what exactly is biohacking? Is it the future of personal health and evolution, or a slippery slope into risky experimentation? In this article, we’ll delve deep into the world of biohacking — its origins, principles, popular techniques, controversies, and future potential. Whether you’re a skeptic, a curious observer, or a self-improvement junkie, the world of biohacking has something provocative for everyone.
People with high levels of psychopathic tendencies are often incapable of feeling empathy for other people. From a brain science perspective, empathy isn’t a single emotion but a multi-part neural process. It involves brain systems that help us share others’ feelings, understand their perspectives, and even mentally step into their experience.
The bigger picture is, however, still blurry as we lack large-scale studies that map how different features of brain structure link to both empathy and psychopathy, especially in incarcerated populations.
A recent study published in Biological Psychiatry Global Open Science investigated how personality is reflected in the brain by turning to something measurable—the brain’s physical structure.
“As we approach the Convergence Age, the physical, digital, biological, and even cosmic realms are merging into a hyperconnected environment. Our understanding of risk and danger is blurring. Dissolution provides fresh opportunities and problems beyond grasp. It’s not speculation. It’s inevitable due to exponential technology. It’s unlike anything humanity has ever experienced”
As we approach the Convergence Age, the physical, digital, and biological are merging into a hyperconnected environment. Our understanding of risk and danger is blurring.