Lifeboat Foundation

Social insects like bees demonstrate a remarkable range of behaviors, from working together to build structurally complex nests (complete with built-in climate control) to the pragmatic division of labor within their communities. Biologists have traditionally viewed these behaviors as pre-programmed responses that evolved over generations in response to external factors. But two papers last week reported results indicating that social learning might also play a role.

The first, published in the journal PLoS Biology, demonstrated that bumblebees could learn to solve simple puzzles by watching more experienced peers. The second, published in the journal Science, reported evidence for similar social learning in how honeybees learn to perform their trademark “waggle dance” to tell other bees in their colony where to find food or other resources. Taken together, both studies add to a growing body of evidence of a kind of “culture” among social insects like bees.

“Culture can be broadly defined as behaviors that are acquired through social learning and are maintained in a population over time, and essentially serves as a ‘second form of inheritance,’ but most studies have been conducted on species with relatively large brains: primates, cetaceans, and passerine birds,” said co-author Alice Bridges, a graduate student at Queen Mary University of London who works in the lab of co-author Lars Chittka. “I wanted to study bumblebees in particular because they are perfect models for social learning experiments. They have previously been shown to be able to learn really complex, novel, non-natural behaviors such as string-pulling both individually and socially.”

Progress in biomimetics allows for the fabrication of man-made materials and surfaces with properties similar to biological ones. These advancements enable the development of a new generation of building materials for architecture that have remarkable properties typically unachievable with a traditional approach.

Scientists have created eggs using the cells of male mice for the first time, leading to the birth of seven mice with two fathers, according to research Wednesday hailed as “revolutionary”.

The technique pioneered in the proof-of-concept experiment is a long way from potentially being used in humans, with obstacles including a low success rate, adaptation concerns and wide-ranging ethical considerations.

But the breakthrough raises the prospect of a raft of new reproductive possibilities, including that gay male couples —or even a single man—could have a biological child without needing a female egg.

OpenAI has released a new version of ChatGPT, claiming that the new language learning model is capable of passing – and even excelling in – a variety of academic exams.

ChatGPT-4, which will be available on Bing as well as the OpenAI website, is more reliable and more creative than its predecessor, according to OpenAI. The team tested the model on a number of exams designed for humans, from the bar exam to biology, using publicly available papers. While no additional training was given to the model ahead of the tests, it was able to perform well on most subjects, performing in the estimated 90th percentile for the bar exam and the 86th-100th in art history.

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Kim et al. used directed evolution methods to identify a high-fidelity SpCas9 variant, Sniper2L, which exhibits high general activity but maintains high specificity at a large number of target sites.

Shortform link:
https://shortform.com/artem.

My name is Artem, I’m a computational neuroscience student and researcher.

Continue reading “Logarithmic nature of the brain 💡” »

After debunking many myths around male and female brains, Gina Rippon’s research interests now include gender gaps in science and why they persist, even in allegedly gender-equal societies.

In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc.

This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence.

In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots.

Researchers have developed a new model inspired by recent biological discoveries that shows enhanced memory performance. This was achieved by modifying a classical neural network.

Computer models play a crucial role in investigating the brain’s process of making and retaining memories and other intricate information. However, constructing such models is a delicate task. The intricate interplay of electrical and biochemical signals, as well as the web of connections between neurons and other cell types, creates the infrastructure for memories to be formed. Despite this, encoding the complex biology of the brain into a computer model for further study has proven to be a difficult task due to the limited understanding of the underlying biology of the brain.

Researchers at the Okinawa Institute of Science and Technology (OIST) have made improvements to a widely utilized computer model of memory, known as a Hopfield network, by incorporating insights from biology. The alteration has resulted in a network that not only better mirrors the way neurons and other cells are connected in the brain, but also has the capacity to store significantly more memories.