The toxic metal may have rewired early human brains--and sealed the Neanderthals' fate. Lead exposure may have negatively affected Neanderthal abilities for language and speech development. Breakthroughs, discoveries, and DIY tips sent every weekday. Today, lead exposure directly correlates to a post-industrialized world. However, new evidence indicates that exposure to the poisonous element is not necessarily a new issue.

Simulacra Naturae is a data-driven media installation that explores collective care through the entanglement of biological computation, material ecologies, and generative systems. The work translates pre-recorded neural activity from brain organoids, lab-grown three-dimensional clusters of neurons, into a multi-sensory environment composed of generative visuals, spatial audio, living plants, and fabricated clay artifacts. These biosignals, streamed through a real-time system, modulate emergent agent behaviors inspired by natural systems such as termite colonies and slime molds. Rather than using biosignals as direct control inputs, Simulacra Naturae treats organoid activity as a co-creative force, allowing neural rhythms to guide the growth, form, and atmosphere of a generative ecosystem. The installation features computationally fabricated clay prints embedded with solenoids, adding physical sound resonances to the generative surround composition. The spatial environment, filled with live tropical plants and a floor-level projection layer featuring real-time generative AI visuals, invites participants into a sensory field shaped by nonhuman cognition. By grounding abstract data in living materials and embodied experience, Simulacra Naturae reimagines visualization as a practice of care, one that decentralizes human agency and opens new spaces for ethics, empathy, and ecological attunement within hybrid computational systems.

We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled interactions and quantitative analysis. Use cases include applications to tracking attitude formation or behavior change, where control or standardization over the conversational flow is desired. We show how our system can be easily adjusted through an online administrative panel to create new interviews, making the tool accessible without coding. Two case studies are presented as example applications, one regarding the Expressive Interviewing system for COVID-19 and the other a semi-structured interview to survey public opinion on emerging neurotechnology. Our code is open-source, allowing others to build off of our work and develop extensions for additional functionality.

Music is a complex auditory stimulus capable of eliciting significant changes in brain activity, influencing cognitive processes such as memory, attention, and emotional regulation. However, the underlying mechanisms of music-induced cognitive processes remain largely unknown. Organoid intelligence and deep learning models show promise for simulating and analyzing these neural responses to classical music, an area significantly unexplored in computational neuroscience. Hence, we present the PyOrganoid library, an innovative tool that facilitates the simulation of organoid learning models, integrating sophisticated machine learning techniques with biologically inspired organoid simulations. Our study features the development of the Pianoid model, a "deep organoid learning" model that utilizes a Bidirectional LSTM network to predict EEG responses based on audio features from classical music recordings. This model demonstrates the feasibility of using computational methods to replicate complex neural processes, providing valuable insights into music perception and cognition. Likewise, our findings emphasize the utility of synthetic models in neuroscience research and highlight the PyOrganoid library's potential as a versatile tool for advancing studies in neuroscience and artificial intelligence.

Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis is time-consuming, and existing datasets lack specific details for brain organoid studies. We introduce BOrg, a dataset designed to study mitotic events in the embryonic development of the brain using confocal microscopy images of brain organoids. BOrg utilizes an efficient annotation pipeline with sparse point annotations and techniques that minimize expert effort, overcoming limitations of standard deep learning approaches on sparse data. We adapt and benchmark state-of-the-art object detection and cell counting models on BOrg for detecting and analyzing mitotic cells across prophase, metaphase, anaphase, and telophase stages. Our results demonstrate these adapted models significantly improve mitosis analysis efficiency and accuracy for brain organoid research compared to existing methods. BOrg facilitates the development of automated tools to quantify statistics like mitosis rates, aiding mechanistic studies of neurodevelopmental processes and disorders. Data and code are available at https://github.com/awaisrauf/borg.

In a story ripped from the opening scenes of a sci-fi horror movie, scientists have bridged a critical gap between the biological and electronic. The study, published in Nature Electronics (summarized in Nature), details a "hybrid biocomputer" combining lab-grown human brain tissue with conventional circuits and AI. Dubbed Brainoware, the system learned to identify voices with 78 percent accuracy. It could one day lead to silicon microchips fused with neurons. Brainoware combines brain organoids -- stem-cell-derived clusters of human cells morphed into neuron-filled "mini-brains" -- with conventional electronic circuits.

Scientists have unveiled a hybrid computer made of electronics and human brain-like tissues called'Brainoware.' It's part of a growing field called biological computing. The new technology features a brain'organoid' made of human stem cells which sit atop a circuit board that feeds the organoid information and reads its responses. This biological-electronic hybrid was able to identify people's by voice and make predictions about a complex math problem. The researchers claim the discovery represents a significant step toward hybrid computers, which merge man and machine to perform complex computing problems using a fraction of the power needed by conventional computers.

Balls of human brain cells linked to a computer have been used to perform a very basic form of speech recognition. The hope is that such systems will use far less energy for AI tasks than silicon chips. "This is just proof-of-concept to show we can do the job," says Feng Guo at Indiana University Bloomington. "We do have a long way to go." Brain organoids are lumps of nerve cells that form when stem cells are grown in certain conditions. "They are like mini-brains," says Guo.

With Brainoware, Guo aimed to use actual brain cells to send and receive information. When the researchers applied electrical stimulation to the hybrid system they'd built, Brainoware responded to those signals, and changes occurred in its neural networks. According to the researchers, this result suggests that the hybrid system did process information, and could perhaps even perform computing tasks without supervision. Guo and his colleagues then attempted to see if Brainoware could perform any useful tasks. In one test, they used Brainoware to try to solve mathematical equations.

Despite AI's impressive track record, its computational power pales in comparison with that of the human brain. Scientists unveil a revolutionary path to drive computing forward: organoid intelligence (OI), where lab-grown brain organoids serve as biological hardware. "This new field of biocomputing promises unprecedented advances in computing speed, processing power, data efficiency, and storage capabilities – all with lower energy needs," say the authors in an article published in Frontiers in Science. Artificial intelligence (AI) has long been inspired by the human brain. This approach proved highly successful: AI boasts impressive achievements – from diagnosing medical conditions to composing poetry.