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In the past decade, lab-grown blobs of human brain tissue began making news headlines, as they ushered in a new era of scientific discovery and raised a slew of ethical questions.
These blobs — scientifically known as brain organoids, but often called “minibrains” in the news — serve as miniature, simplified models of full-size human brains. These organoids can potentially be useful in basic research, drug development and even computer science.
Some years ago, when he was still living in southern California, neuroscientist Christof Koch drank a bottle of Barolo wine while watching The Highlander, and then, at midnight, ran up to the summit of Mount Wilson, the 5,710-foot peak that looms over Los Angeles.
After an hour of “stumbling around with my headlamp and becoming nauseated,” as he later described the incident, he realized the nighttime adventure was probably not a smart idea, and climbed back down, though not before shouting into the darkness the last line of William Ernest Henley’s 1,875 poem “Invictus”: “I am the master of my fate / I am the captain of my soul.”
Mitochondria in brain cells frequently insert their DNA into the nucleus, potentially impacting lifespan, as those with more insertions were found to die earlier. Stress appears to accelerate this process, suggesting a new way mitochondria influence health beyond energy production.
As direct descendants of ancient bacteria, mitochondria have always been a little alien. Now a study shows that mitochondria are possibly even stranger than we thought.
Mitochondria in our brain cells frequently fling their DNA into the nucleus, the study found, where the DNA becomes integrated into the cells’ chromosomes. And these insertions may be causing harm: Among the study’s nearly 1,200 participants, those with more mitochondrial DNA insertions in their brain cells were more likely to die earlier than those with fewer insertions.
Researchers from the University of California, Irvine have discovered the neurons responsible for “item memory,” deepening our understanding of how the brain stores and retrieves the details of “what” happened and offering a new target for treating Alzheimer’s disease.
Memories include three types…
Finding significantly deepens understanding of crucial component of cognitive function.
I argue for a pattern theory of self as a useful way to organize an interdisciplinary approach to discussions of what constitutes a self. According to the pattern theory, a self is constituted by a number of characteristic features or aspects that may include minimal embodied, minimal experiential, affective, intersubjective, psychological/cognitive, narrative, extended, and situated aspects. A pattern theory of self helps to clarify various interpretations of self as compatible or commensurable instead of thinking them in opposition, and it helps to show how various aspects of self may be related across certain dimensions. I also suggest that a pattern theory of self can help to adjudicate (or at least map the differences) between the idea that the self correlates to self-referential processing in the cortical midline structures of the brain and other narrower or wider conceptions of self.
Keywords: self, pattern theory, cortical midline structures, first-person perspective.
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Neuralink says it has successfully implanted another brain chip in a human patient.
According to a study update shared by the company, the patient, identified by his first name, Alex, has been improving his ability to play video games and has started learning how to use design software to create 3D objects.
The company said the procedure “went well,” and Alex’s recovery “has been smooth.”
A Yale-led study reveals that two types of neurodevelopmental abnormalities emerging early in brain development are linked to autism, with these differences influenced by brain size.
By using brain organoids derived from autistic children’s stem cells, researchers uncovered distinct neural growth patterns, potentially guiding personalized treatments and diagnoses.
Early Brain Development and Autism.
LLMs don’t just memorize word pairs or sequences—they learn to encode abstract representations of language. These models are trained on immense amounts of text data, allowing them to infer relationships between words, phrases, and concepts in ways that extend beyond mere surface-level patterns. This is why LLMs can handle diverse contexts, respond to novel prompts, and even generate creative outputs.
In this sense, LLMs are performing a kind of machine inference. They compress linguistic information into abstract representations that allow them to generalize across contexts—similar to how the hippocampus compresses sensory and experiential data into abstract rules or principles that guide human thought.
But can LLMs really achieve the same level of inference as the human brain? Here, the gap becomes more apparent. While LLMs are impressive at predicting the next word in a sequence and generating text that often appears to be the product of thoughtful inference, their ability to truly understand or infer abstract concepts is still limited. LLMs operate on correlations and patterns rather than understanding the underlying causality or relational depth that drives human inference.
