Lifeboat Foundation

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.

The research, by Osaka University, is early but raises the prospect of male couples may someday have their own biological children.

Studying the relationship between the arrangement of water molecules incorporated into layered materials like clays and the arrangement of ions within these materials has been a difficult experiment to conduct.

However, researchers have now succeeded in observing these interactions for the first time by utilizing a technique commonly used for measuring extremely small masses and molecular interactions at the nanoscale.

The nanoscale refers to a length scale that is extremely small, typically on the order of nanometers (nm), which is one billionth of a meter. At this scale, materials and systems exhibit unique properties and behaviors that are different from those observed at larger length scales. The prefix “nano-” is derived from the Greek word “nanos,” which means “dwarf” or “very small.” Nanoscale phenomena are relevant to many fields, including materials science, chemistry, biology, and physics.

Regeneration, Resuscitation & Biothreat Countermeasures — Commander Dr. Jean-Paul Chretien, MD, Ph.D., Program Manager, Biological Technology Office, DARPA

Commander Dr. Jean-Paul Chretien, MD, Ph.D. (https://www.darpa.mil/staff/cdr-jean-paul-chretien) is a Program Manager in the Biological Technology Office at DARPA, where his research interests include disease and injury prevention, operational medicine, and biothreat countermeasures. He is also responsible for running the DARPA Triage Challenge (https://triagechallenge.darpa.mil/).

Continue reading “CDR Dr. Jean-Paul Chretien — DARPA BTO — Regeneration, Resuscitation And Biothreat Countermeasures” »

Here’s another thing I have changed my mind on. Well, sort of. I used to make fun of “vitalism” and trade insults with my favorite archenemy Dale Carrico. Now I must repent or at least add important qualifications.

Vitalism is currently defined by Wikipedia as “the belief that living organisms are fundamentally different from non-living entities because they contain some non-physical element or are governed by different principles than are inanimate things.”

If we eliminate a few words from this definition we are left with a statement that I don’t disagree with:

Summary: Utilizing a classic neural network, researchers have created a new artificial intelligence model based on recent biological findings that shows improved memory performance.

Source: OIST

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals – both biochemical and electrical – and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Summary: Bumblebees are able to learn to solve puzzles by watching more experienced bees complete a task. This new behavioral preference then spreads throughout the entire colony. The bees that learned from others became more adept and began to prefer the learned solution over alternatives.

Source: PLOS

Bumblebees learn to solve a puzzle by watching more experienced bees, and this behavioral preference then spreads through the colony, according to a study published March 7^th in the open access journal PLOS Biology by Alice Dorothy Bridges and colleagues at Queen Mary University of London, UK.

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals—both biochemical and electrical—and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Now, researchers at the Okinawa Institute of Science and Technology (OIST) have altered a commonly used computer model of memory called a Hopfield network in a way that improves performance by taking inspiration from biology. They found that not only does the new network better reflect how neurons and other cells wire up in the brain, it can also hold dramatically more memories.

The complexity added to the network is what makes it more realistic, says Thomas Burns, a Ph.D. student in the group of Professor Tomoki Fukai, who heads OIST’s Neural Coding and Brain Computing Unit. “Why would biology have all this complexity? Memory capacity might be a reason,” Mr. Burns says.