Lifeboat Foundation

A fundamental question in neuroscience is what are the constraints that shape the structural and functional organization of the brain. By bringing biological cost constraints into the optimization process of artificial neural networks, Achterberg, Akarca and colleagues uncover the joint principle underlying a large set of neuroscientific findings.

The human mind is by far one of the most amazing natural phenomena known to man. It embodies our perception of reality, and is in that respect the ultimate observer. The past century produced monumental discoveries regarding the nature of nerve cells, the anatomical connections between nerve cells, the electrophysiological properties of nerve cells, and the molecular biology of nervous tissue. What remains to be uncovered is that essential something – the fundamental dynamic mechanism by which all these well understood biophysical elements combine to form a mental state. In this chapter, we further develop the concept of an intraneuronal matrix as the basis for autonomous, self–organized neural computing, bearing in mind that at this stage such models are speculative. The intraneuronal matrix – composed of microtubules, actin filaments, and cross–linking, adaptor, and scaffolding proteins – is envisioned to be an intraneuronal computational network, which operates in conjunction with traditional neural membrane computational mechanisms to provide vastly enhanced computational power to individual neurons as well as to larger neural networks. Both classical and quantum mechanical physical principles may contribute to the ability of these matrices of cytoskeletal proteins to perform computations that regulate synaptic efficacy and neural response. A scientifically plausible route for controlling synaptic efficacy is through the regulation of neural transport of synaptic proteins and of mRNA. Operations within the matrix of cytoskeletal proteins that have applications to learning, memory, perception, and consciousness, and conceptual models implementing classical and quantum mechanical physics are discussed. Nanoneuroscience methods are emerging that are capable of testing aspects of these conceptual models, both theoretically and experimentally. Incorporating intra–neuronal biophysical operations into existing theoretical frameworks of single neuron and neural network function stands to enhance existing models of neurocognition.

Summary: Researchers developed an experimental computing system, resembling a biological brain, that successfully identified handwritten numbers with a 93.4% accuracy rate.

This breakthrough was achieved using a novel training algorithm providing continuous real-time feedback, outperforming traditional batch data processing methods which yielded 91.4% accuracy.

The system’s design features a self-organizing network of nanowires on electrodes, with memory and processing capabilities interwoven, unlike conventional computers with separate modules.

An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.

The algorithm outperformed a conventional machine-learning approach in which training was performed after a batch of data had been processed, producing 91.4% accuracy. The researchers also showed that memory of past inputs stored in the system itself enhanced learning. In contrast, other computing approaches store memory within software or hardware separate from a device’s processor.

For 15 years, researchers at the California NanoSystems Institute at UCLA, or CNSI, have been developing a new platform technology for computation. The technology is a brain-inspired system composed of a tangled-up network of wires containing silver, laid on a bed of electrodes. The system receives input and produces output via pulses of electricity. The individual wires are so small that their diameter is measured on the nanoscale, in billionths of a meter.

If you have any form of Arachnophobia, do not read this article. You’ve been warned. Now if you’re like me and have a mad respect for Mother Nature, I posit you this query. Did you know that spiders can fly? And not by the way you may think.

Good news for your nightmares: Spiders can fly. Despite not having wings, new research shows that spiders have the ability to propel themselves using the Earth’s electric field, with little to no help from wind or webs. Because humans can’t feel these electric currents, their role in biology can often go ignored. But if electrostatic is what is helping spiders fly more than two miles high in the air, let’s pay attention.

In a study published in Current Biology on Thursday, Drs. Erica L. Morley and Daniel Robert of the University of Bristol found that when spiders are placed in a chamber with no wind but a small electric field, they were still able to to fly, despite the prevailing idea that a spider’s flight was reliant on wind currents.

Continue reading “Spiders Can Fly and They Don’t Even Need Wings, Study Claims” »

In October, a paper titled “Assembly theory explains and quantifies selection and evolution” appeared in the journal Nature. The authors—a team led by Lee Cronin at the University of Glasgow and Sara Walker at Arizona State University—claim their theory is an “interface between physics and biology” which explains how complex biological forms can evolve.

The paper provoked strong responses. On the one hand were headlines like “Bold New ” Theory of Everything’ Could Unite Physics And Evolution”

On the other were reactions from scientists. One evolutionary biologist tweeted after multiple reads I still have absolutely no idea what [this paper] is doing. Another said I read the paper and I feel more confused […] I think reading that paper has made me forget my own name.

Researchers at Auburn University have achieved a groundbreaking discovery, illuminating the process by which brain cells efficiently replace older proteins. This process is essential for maintaining effective neural communication and optimal cognitive function.

The findings were published on November 6 in the prestigious journal, Frontiers in Cell Development and Biology. The study, entitled “Recently Recycled Synaptic Vesicles Use Multi-Cytoskeletal Transport and Differential Presynaptic Capture Probability to Establish a Retrograde Net Flux During ISVE in Central Neurons,” explains the transportation and recycling of older proteins in brain cells.

This video explores what life would be like if we became a Type I Civilization. Watch this next video about the Technological Singularity: https://youtu.be/yHEnKwSUzAE.
🎁 5 Free ChatGPT Prompts To Become a Superhuman: https://bit.ly/3Oka9FM
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SOURCES:
• https://www.futuretimeline.net.
• The Singularity Is Near: When Humans Transcend Biology (Ray Kurzweil): https://amzn.to/3ftOhXI
• The Future of Humanity (Michio Kaku): https://amzn.to/3Gz8ffA

Continue reading “What If We Became A Type I Civilization? 15 Predictions” »

Each iteration of ChatGPT has demonstrated remarkable step function capabilities. But what’s next? Ilya Sutskever, Co-Founder & Chief Scientist at OpenAI, joins Sarah Guo and Elad Gil to discuss the origins of OpenAI as a capped profit company, early emergent behaviors of GPT models, the token scarcity issue, next frontiers of AI research, his argument for working on AI safety now, and the premise of Superalignment. Plus, how do we define digital life?

Ilya Sutskever is Co-founder and Chief Scientist of OpenAI. He leads research at OpenAI and is one of the architects behind the GPT models. He co-leads OpenAI’s new “Superalignment” project, which tries to solve the alignment of superintelligences in 4 years. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D in Computer Science from the University of Toronto.

Continue reading “No Priors Ep. 39” »