Tag Archives: Xie Chen

In vitro biological neural networks (BNNs): review paper

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The race to merge the biological with machines continues apace as this press release makes clear, From a March 9, 2023 Beijing Institute of Technology Press Co. press release on EurekAlert, Note: A link has been removed,

A review paper by scientists at the Beijing Institute of Technology summarized recent efforts and future potentials in the use of in vitro biological neural networks (BNNs) for the realization of biological intelligence, with a focus on those related to robot intelligence.

The review paper, published on Jan. 10 in the journal Cyborg and Bionic Systems, provided an overview of 1) the underpinnings of intelligence presented in in vitro BNNs, such as memory and learning; 2) how these BNNs can be embodied with robots through bidirectional connection, forming so-called BNN-based neuro-robotic systems; 3) preliminary intelligent behaviors achieved by these neuro-robotic systems; and 4) current trends and future challenges in the research area of BNN-based neuro-robotic systems.

“our human brain is a complex biological neural network (BNN) composed of billions of neurons, which gives rise to our consciousness and intelligence. However, studying the brain as a whole is extremely challenging due to its intricate nature. By culturing a part of the neurons from the brain in a Petri dish, simpler BNNs, such as mini-brains, can be formed, allowing for easier observation and investigation of the network. These mini-brains may provide valuable insights into the enigmatic origins of consciousness and intelligence.” explained study author Zhiqiang Yu, an assistant researcher at the Beijing Institute of Technology.

“Interestingly, mini-brains are not only structurally similar to human brains, but they can also learn and memorize information in a similar way.” said Yu. In particular, these in vitro BNNs share the same basic structure as in vivo BNNs, where neurons are connected through synapses, and they exhibit short-term memory through fading and hidden memory processes. Additionally, these mini-brains can perform supervised learning and be trained to respond to specific stimuli signals. Recently, researchers have demonstrated that in vitro BNNs can even accomplish unsupervised learning tasks, such as separating mixed signals. “This fascinating ability may have something to do with the famous free energy principle. That is, these BNNs have a tendency to minimize their uncertainty about the outer world,” said Yu.

These abilities of in vitro BNNs are quite intriguing. However, only having such a ‘mini-brain’ on your hand is not enough for the rise of consciousness and intelligence. Our brain relies on our body to perceive, comprehend, and adapt to the outside world, and similarly, these mini-brains require a body to interact with their environment. A robot is an ideal candidate for this purpose, leading to a burgeoning interdisciplinary field at the intersection of neuroscience and robotics: BNN-based neuro-robotic systems.

“A stable bidirectional connection is a prerequisite for these systems.” said study authors, “In this review, we summarize the mainstream means of constructing such a bidirectional connection, which can be broadly classified into two categories based on the direction of connection: from robots to BNNs and from BNNs to robots.” The former involves transmitting sensor signals from the robot to BNNs, utilizing electrical, optical, and chemical stimulation methods, while the latter records the neural activities of BNNs and decode these activities into commands to control the robot, using extracellular, calcium, and intracellular recording techniques.

“Embodied by robots, in vitro BNNs exhibit a wide range of fascinating intelligent behaviors,” according to Yu. “These behaviors include supervised and unsupervised learning, memorization, mobile object tracking, active obstacle avoidance, and even learning to play games such as ‘Pong’.”

The intelligent behaviors displayed by these BNN-based neuro-robotic systems can be divided into two categories based on their dependence on either computing capacity or network plasticity, as explained by Yu. “In computing capacity-dependent behaviors, learning is unnecessary, and the BNN is regarded as an information processor that generates specific neural activities in response to stimuli. However, for the latter, learning is a crucial process, as the BNN adapts to stimuli and these changes are integral to the behaviors or tasks performed by the robot,” added Yu.

To facilitate easy comparison of the recording and stimulation techniques, encoding and decoding rules, training policies, and robot tasks, representative studies from these two categories have been compiled into two tables. Additionally, to provide readers with a historical overview of BNN-based neuro-robotic systems, several noteworthy studies have been selected and arranged chronologically.

The study authors also discussed current trends and main challenges in the field. According to Yu, “Four challenges are keen to be addressed and are being intensely investigated. How to fabricate BNNs in 3D, thereby making in vitro BNNs close to their in vivo counterparts, is the most urgent one of them”

Perhaps the most challenging aspect is how to train these robot-embodied BNNs. The study authors noted that BNNs are composed only of neurons and lack the participation of various neuromodulators, which makes it difficult to transplant various animal training methods to BNNs. Additionally, BNNs have their own limitations. While a monkey can be trained to ride a bicycle, it is much more challenging to accomplish tasks that require higher-level thought processes, such as playing Go.

“The mystery of how consciousness and intelligence emerge from the network of cells in our brains still eludes neuroscientists” said Yu. However, with the development of embodying in vitro BNNs with robots, we may observe more intelligent behaviors in them and bring people closer to the truth behind the mystery.

I think that ‘in vitro biological neural networks (BNNs) or mini-brains’ can also be called brain organoids, which seems to be the more popular term in some circles.

Here’s a link to and a citation for the paper,

An Overview of In Vitro Biological Neural Networks for Robot Intelligence by Zhe Chen, Qian Liang, Zihou Wei, Xie Chen, Qing Shi, Zhiqiang Yu, and Tao Sun. Cyborg and Bionic Systems 10 Jan 2023 Vol 4 Article ID: 0001 DOI: 10.34133/cbsystems.0001

This paper is open access.

500 phases of matter take us beyond solid, liquid, and gas

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A Dec. 22, 2012 news item on Nanowerk touts a major reclassification and expansion of the states of matter,

Forget solid, liquid, and gas: there are in fact more than 500 phases of matter. In a major paper in Science, Perimeter [Institute] Faculty member Xiao-Gang Wen reveals a modern reclassification of all of them.

Using modern mathematics, Wen and collaborators reveal a new system which can, at last, successfully classify symmetry-protected phases of matter. Their new classification system will provide insight about these quantum phases of matter, which may in turn increase our ability to design states of matter for use in superconductors or quantum computers.

The Perimeter Institute for Theoretical Physics, where this work was done, is located in Waterloo, Ontario (Canada). More information about Wen’s latest publication can be found in this Dec. 21, 2012 press release on the Institute website (there are also links to more explanations about condensed matter and other related topics),

Condensed matter physics – the branch of physics responsible for discovering and describing most of these phases – has traditionally classified phases by the way their fundamental building blocks – usually atoms – are arranged. The key is something called symmetry.

To understand symmetry, imagine flying through liquid water in an impossibly tiny ship: the atoms would swirl randomly around you and every direction – whether up, down, or sideways – would be the same. The technical term for this is “symmetry” – and liquids are highly symmetric. Crystal ice, another phase of water, is less symmetric. If you flew through ice in the same way, you would see the straight rows of crystalline structures passing as regularly as the girders of an unfinished skyscraper. Certain angles would give you different views. Certain paths would be blocked, others wide open. Ice has many symmetries – every “floor” and every “room” would look the same, for instance – but physicists would say that the high symmetry of liquid water is broken.

Classifying the phases of matter by describing their symmetries and where and how those symmetries break is known as the Landau paradigm. More than simply a way of arranging the phases of matter into a chart, Landau’s theory is a powerful tool which both guides scientists in discovering new phases of matter and helps them grapple with the behaviours of the known phases. Physicists were so pleased with Landau’s theory that for a long time they believed that all phases of matter could be described by symmetries. That’s why it was such an eye-opening experience when they discovered a handful of phases that Landau couldn’t describe.

Beginning in the 1980s, condensed matter researchers, including Xiao-Gang Wen – now a faculty member at Perimeter Institute – investigated new quantum systems where numerous ground states existed with the same symmetry. Wen pointed out that those new states contain a new kind of order: topological order. Topological order is a quantum mechanical phenomenon: it is not related to the symmetry of the ground state, but instead to the global properties of the ground state’s wave function. Therefore, it transcends the Landau paradigm, which is based on classical physics concepts.

Topological order is a more general understanding of quantum phases and the transitions between them. In the new framework, the phases of matter were described not by the patterns of symmetry in the ground state, but by the patterns of a decidedly quantum property – entanglement.

Wen’s new work has been published in latest issue of Science,

Symmetry-Protected Topological Orders in Interacting Bosonic Systems by Xie Chen, Zheng-Cheng Gu, Zheng-Xin Liu, Xiao-Gang Wen in Science 21 December 2012: Vol. 338 no. 6114 pp. 1604-1606 DOI: 10.1126/science.1227224

The article is behind a paywall.

Surprisingly, there aren’t any visualizations of the 500 states similar to chemistry’s periodic table to elements; at least, they aren’t included in the press materials on the Institute’s website.