Lifeboat Foundation

3D structures of cells and connections reveal new role for an understudied brain cell.

Talk kindly contributed by Michael Levin in SEMF’s 2022 Spacious Spatiality.

https://semf.org.es/spatiality.

Continue reading “Michael Levin | Cell Intelligence in Physiological & Morphological Spaces” »

This educational video about cellular automata was filmed, narrated, and edited by Rudy Rucker in 1990, using some “CA Lab” software he worled on at Autodesk. Renamed “Cellab,” the software and manual are available for free on Rucker’s website.

http://www.rudyrucker.com/oldhomepage/cellab.htm.

Continue reading “Cellular Automata” »

Year 2016 face_with_colon_three

In the future, can humans control computer-operated machines by simply “thinking”?

Year 2018 face_with_colon_three

A group of researchers used optogenetics and brain implants to make mice ignore sex and food.

Video shows a cyborg mouse ignoring sex and food as it obeys humans’ navigation commands.

Augmenting a person’s cognitive ability with BCIs is becoming more science fact that science fiction, with defence personnel likely to benefit.

Researchers discover that to sharpen its control over precise maneuvers, the brain uses comparisons between control signals — not the signals themselves.

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: human language processing, our species’ signature cognitive skill. We find that the most powerful “transformer” models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models’ neural fits (“brain score”) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.