In vitro neurons learn and exhibit sentience when embodied in a simulated game-world

A multiscale brain emulation-based artificial intelligence framework for dynamic environments

Achieving general artificial intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales, encompassing multi-compartment neuron architectures, diverse synaptic connection modalities, neural microcircuits, cortical columns, and brain regions, as well as biochemical processes including facilitation, feedforward inhibition, short-term potentiation, and short-term depression, all grounded in solid neuroscience. Building upon these highly integrated brain-like mechanisms, I have developed a sensorimotor model that simulates human saccadic eye movements during object observation. The model's algorithmic efficacy was validated through testing with the observation of handwritten digit images.

Virtual white matter: a novel system for cross-dish neural interaction and modulation

Objective. Biological neural networks (BNNs) are characterized by complex interregional connectivity, allowing for seamless communication between different brain regions.In vitromodels traditionally consist of single-dish neural cultures that cannot recapitulate the dynamics of interregional interactions and little effort has been made to interconnect multiple BNNs to process information through a hybrid interconnection of the biological and digital systems.Approach. We introduce virtual white matter (VWM), a novel platform enabling real-time functional digital connectivity between neural cultures in separate microelectrode array dishes. By detecting neural activity in one dish and providing precisely timed electrical stimulation to another, VWM recreates bidirectional inter-regional neural communication. The study introduces the conceptual framework, technical implementation, and proof-of-concept validation of the VWM system.Main Results.VWM represents a significant advancementin vitromodeling and data processing by enabling controlled interactions between heterogeneous neural cultures, such as different brain regions or cell types. The platform successfully enables the investigation of dynamic network behaviors and integration with both biological and artificial neural networks.Significance. VWM will push forward biocomputing, wetware computing, and organic intelligence by establishing a reliable form of interconnection between these systems. Furthermore, VWM has the potential to be applied in fields like therapeutic interventions that use directed neural plasticity to promote recovery from brain injury or disease responses. VWM enables complexin vitromodels to be built with the same neural connectivity as in the human brain. VWM is versatile, placing it at the core of a transformational tool for experimental neuroscience, biocomputing, and translational research to bridge biological and digital systems.

Emerging brain organoids: 3D models to decipher, identify and revolutionize brain

Brain organoids are an emerging in vitro 3D brain model that is integrated from pluripotent stem cells. This model mimics the human brain's developmental process and disease-related phenotypes to a certain extent while advancing the development of human brain-based biological intelligence. However, many limitations of brain organoid culture (e.g., lacking a functional vascular system, etc.) prevent in vitro-cultured organoids from truly replicating the human brain in terms of cell type and structure. To improve brain organoids' scalability, efficiency, and stability, this paper discusses important contributions of material biology and microprocessing technology in solving the related limitations of brain organoids and applying the latest imaging technology to make real-time imaging of brain organoids possible. In addition, the related applications of brain organoids, especially the development of organoid intelligence combined with artificial intelligence, are analyzed, which will help accelerate the rational design and guidance of brain organoids.

Engineered biological neuronal networks as basic logic operators

We present an in vitro neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microelectrode arrays (HD-MEAs) enable precise interaction through extracellular voltage stimulation and spiking activity recording. The architecture of our system allows for creating non-linear functions with two inputs and one output. Additionally, we analyze various encoding schemes, comparing the limitations of rate coding with the potential advantages of spike-timing-based coding strategies. This work contributes to the advancement of hybrid intelligence and biocomputing by offering insights into neural information encoding and decoding with the potential to create fully biological computational systems.

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

Repetitive training enhances the pattern recognition capability of cultured neural networks

Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.

Human Brain Inspired Artificial Intelligence Neural Networks

It is becoming increasingly evident that Artificial intelligence (AI) development draws inspiration from the architecture and functions of the human brain. This manuscript examines the alignment between key brain regions-such as the brainstem, sensory cortices, basal ganglia, thalamus, limbic system, and prefrontal cortex-and AI paradigms, including generic AI, machine learning, deep learning, and artificial general intelligence (AGI). By mapping these neural and computational architectures, I herein highlight how AI models progressively mimic the brain's complexity, from basic pattern recognition and association to advanced reasoning. Current challenges, such as overcoming learning limitations and achieving comparable neuroplasticity, are addressed alongside emerging innovations like neuromorphic computing. Given the rapid pace of AI advancements in recent years, this work underscores the importance of continuously reassessing our understanding as technology evolves exponentially.

Cyborg organoids integrated with stretchable nanoelectronics can be functionally mapped during development

Organoids are in vitro miniaturized cellular models of organs that offer opportunities for studying organ development, disease mechanisms and drug screening. Understanding the complex processes governing organoid development and function requires methods suitable for the continuous, long-term monitoring of cell activities (for example, electrophysiological and mechanical activity) at single-cell resolution throughout the entire three-dimensional (3D) structure. Cyborg organoid technology addresses this need by seamlessly integrating stretchable mesh nanoelectronics with tissue-like properties, such as tissue-level flexibility, subcellular feature size and mesh-like networks, into 3D organoids through a 2D-to-3D tissue reconfiguration process during organogenesis. This approach enables longitudinal, tissue-wide, single-cell functional mapping, thereby overcoming the limitations of existing techniques including recording duration, spatial coverage, and the ability to maintain stable contact with the tissue during organoid development. This protocol describes the fabrication and characterization of stretchable mesh nanoelectronics, their electrical performance, their integration with organoids and the acquisition of long-term functional organoid activity requiring multimodal data analysis techniques. Cyborg organoid technology represents a transformative tool for investigating organoid development and function, with potential for improving in vitro disease models, drug screening and personalized medicine. The procedure is suitable for users within a multidisciplinary team with expertise in stem cell biology, tissue engineering, nanoelectronics fabrication, electrophysiology and data science.

Active Inference and Intentional Behavior

Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vitro cell cultures and neuronal networks. Such neuronal networks reorganize activity to demonstrate structured behaviors when embodied in structured information landscapes. In this article, we characterize this kind of self-organization through the lens of the free energy principle, that is, as self-evidencing. We do this by first discussing the definitions of reactive and sentient behavior in the setting of active inference, which describes the behavior of agents that model the consequences of their actions. We then introduce a formal account of intentional behavior that describes agents as driven by a preferred end point or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behavior using simulations. First, we simulate the in vitro experiments, in which neuronal cultures modulated activity to improve gameplay in a simplified version of Pong by implementing nested, free energy minimizing processes. The simulations are then used to deconstruct the ensuing predictive behavior, leading to the distinction between merely reactive, sentient, and intentional behavior with the latter formalized in terms of inductive inference. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem) that show how quickly and efficiently adaptive behavior emerges under an inductive form of active inference.

Harnessing Intelligence from Brain Cells In Vitro

Harnessing intelligence from brain cells in vitro requires a multidisciplinary approach integrating wetware, hardware, and software. Wetware comprises the in vitro brain cells themselves, where differentiation from induced pluripotent stem cells offers ethical scalability; hardware typically involves a life support system and a setup to record the activity from and deliver stimulation to the brain cells; and software is required to control the hardware and process the signals coming from and going to the brain cells. This review provides a broad summary of the foundational technologies underpinning these components, along with outlining the importance of technology integration. Of particular importance is that this new technology offers the ability to extend beyond traditional methods that assess primarily the survival and spontaneous activity of neural cultures. Instead, the focus returns to the core function of neural tissue: the neurocomputational ability to process information and respond accordingly. Therefore, this review also covers work that, despite the relatively early state of current technology, has provided novel and meaningful understandings in the field of neuroscience along with opening exciting avenues for future research.

Distinct baseline toxicity of volatile organic compounds (VOCs) in gaseous and liquid phases: Mixture effects and potential molecular mechanisms

Volatile organic compounds (VOCs) are significant pollutants found in various environments, posing health risks. Traditionally, the gaseous VOCs are adsorbed and eluted in liquid phases, and then subjected to toxicity testing, which deviates from the actual exposure scenarios of gaseous VOCs. How the physical states of VOCs (gaseous or liquid) affect their toxicity has not been well understood. This study examined the baseline toxicity of VOCs in both gaseous and liquid phases using a self-assembled passive colonization hydrogel (SAPCH) with luminous bacteria (Vibrio fischeri). The findings revealed that gaseous VOCs exhibited higher baseline toxicity than their liquid counterparts, attributed to the higher free energy and electronic activity of gaseous VOC molecules. Furthermore, the study elucidated that the differences in electronic transitions and energy gaps significantly impact the combined toxicity of VOC mixtures in different phases. Understanding these differences is crucial for assessing the real-world impact of VOCs on health and the environment.

Beyond consciousness: Ethical, legal, and social issues in human brain organoid research and application

This study aims to provide a comprehensive review of the ethical, legal and social issues in human brain organoid research, with a view to different types of research and applications: in vitro research, transplantation into non-human animals, and biocomputing. Despite the academic and societal attention on the possibility that human brain organoids may be conscious, we have identified diverse issues in human brain organoid research and applications. To guide the complex terrain of human brain organoid research and applications, a multidisciplinary approach that integrates ethical, legal, and social perspectives is essential.

As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.

Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models

We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison.

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics, and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory type of its constituents, and predict neuronal spiking activity by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator, and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.

Bio-inspired deep neural local acuity and focus learning for visual image recognition

In the field of computer vision and image recognition, enabling the computer to discern target features while filtering out irrelevant ones poses a challenge. Drawing insights from studies in biological vision, we find that there is a local visual acuity mechanism and a visual focus mechanism in the initial conversion and processing of visual information, ensuring that the visual system can give ear to salient features of the target in the early visual observation phase. Inspired by this, we build a novel image recognition network to focus on the target features while ignoring other irrelevant features in the image, and further focus on the focus features based on the target features. Meanwhile, in order to comply with the output characteristics when similar features exist in different categories, we design a softer image label operation for similar features in different categories, which solves the correlation of labels between categories. Relevant experimental findings underscore the efficacy of our proposed method, revealing discernible advantages in comparison to alternative approaches. Visualization results further attest to the method's capability to selectively focus on pertinent target features within the image, sidelining extraneous information.

How to Integrate Neuroethics into a Neuroscience Course - And Drive Student Engagement with Core Concepts

Students are thinking about ethical, moral, and societal implications of science-as individuals and communities- regardless of whether these topics are part of formal curricula. Ethical questions can arise from broad neuroscientific questions (What is consciousness?), emerging topics (e.g., synthetic biological intelligence), neurotechnologies (e.g., human brain organoids), and respective intersections (Could brain organoids be intelligent or conscious?). As a field of scholarship, the ethics of brain science, or 'neuroethics', can help students to situate what they are learning in the classroom within a broader socio-philosophical context that advances critical and ethical reasoning toward future neuroscience research or technologies. I will argue that neuroethics can also enhance student situational interest and cognitive engagement with core neuroscientific concepts that align with core learning objectives. Yet faculty face challenges when incorporating neuroethics topics into courses, which may include, but are not limited to i) lack of disciplinary expertise, ii) time or resource constraints within courses, or iii) the perceived lack of value in formally including ethics instructional content in courses focused on core concepts in neuroscience education. This Opinion article aims to demonstrate how these challenges can be overcome. I describe how the Value Reappraisal Model can be used as a process theory to guide integration of neuroethics into neuroscience curricula. My autoethnographic account of developing and teaching a new course provides a case study for faculty who are interested in creating curricular opportunities for students to engage with ethical issues by fostering deeper learning and appreciation of core concepts in neuroscience.

Theoretical analysis of neuronal network's response under different stimulus

Neuromodulation plays a critical role in the normal physiological functions of organisms. With advancements in science and technology, neuromodulation has expanded into various fields. For instance, in the field of engineering, in vitro-cultured neural networks are utilized to perform closed-loop control for achieving complex functionalities. Conducting pioneering theoretical research using mathematical models is particularly essential for enhancing efficiency and reducing costs. This study focuses on examining the relationship between input and output in order to establish a groundwork for more advanced closed-loop regulation applications in engineering. Using a constructed neural network model, Poisson, square wave and direct current (DC) stimulation are applied. The results show that the network's firing rate increases with the frequency or amplitude of these stimulations. And the network's firing rate could reach to a stable state after the stimulation is applied for 0.8s and return to initial states when the stimulus is removed for 1s. To ascertain if the system exhibits a memory effect from the previous stimulus, we conduct independent and continuous stimulation schemes. Comparing the firing rate of neuronal networks under these two stimulation schemes reveals a memory effect of the system on the previous stimulus, which is independent of network properties and stimulus types. Finally, by applying square wave stimulation to the in vitro cultured neural network, we have confirmed that cultured neural network actually can reach to a steady state and have memory effects on the previous stimulus. Our research results have important theoretical significance and reference value for designing the closed-loop regulation strategy of in vitro cultured neuronal networks.

Moral Intuition Regarding the Possibility of Conscious Human Brain Organoids: An Experimental Ethics Study

The moral status of human brain organoids (HBOs) has been debated in view of the future possibility that they may acquire phenomenal consciousness. This study empirically investigates the moral sensitivity in people's intuitive judgments about actions toward conscious HBOs. The results showed that the presence/absence of pain experience in HBOs affected the judgment about the moral permissibility of actions such as creating and destroying the HBOs; however, the presence/absence of visual experience in HBOs also affected the judgment. These findings suggest that people's intuitive judgments about the moral status of HBOs are sensitive to the valence-independent value of phenomenal consciousness. We discuss how these observations can have normative implications; particularly, we argue that they put pressure on the theoretical view that the moral status of conscious HBOs is grounded solely in the valence-dependent value of consciousness. We also discuss how our findings can be informative even when such a theoretical view is finally justified or when the future possibility of conscious HBOs is implausible.

Goal-Directed Learning in Cortical Organoids

Experimental neuroscience techniques are advancing rapidly, with major recent developments in high-density electrophysiology and targeted electrical stimulation. In combination with these techniques, cortical organoids derived from pluripotent stem cells show great promise as in vitro models of brain development and function. Although sensory input is vital to neurodevelopment in vivo , few studies have explored the effect of meaningful input to in vitro neural cultures over time. In this work, we demonstrate the first example of goal-directed learning in brain organoids. We developed a closed-loop electrophysiology framework to embody mouse cortical organoids into a simulated dynamical task (the inverted pendulum problem known as 'Cartpole') and evaluate learning through high-frequency training signals. Longitudinal experiments enabled by this framework illuminate how different methods of selecting training signals enable improvement on the tasks. We found that for most organoids, training signals chosen by artificial reinforcement learning yield better performance on the task than randomly chosen training signals or the absence of a training signal. This systematic approach to studying learning mechanisms in vitro opens new possibilities for therapeutic interventions and biological computation.

RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies

With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort's true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort's performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort's functionality on different types of recording hardware and electrode configurations.

The impact of aging on HIV-1-related neurocognitive impairment

Depending on the population studied, HIV-1-related neurocognitive impairment is estimated to impact up to half the population of people living with HIV (PLWH) despite the availability of combination antiretroviral therapy (cART). Various factors contribute to this neurocognitive impairment, which complicates our understanding of the molecular mechanisms involved. Biological aging has been implicated as one factor possibly impacting the development and progression of HIV-1-related neurocognitive impairment. This is increasingly important as the life expectancy of PLWH with virologic suppression on cART is currently projected to be similar to that of individuals not living with HIV. Based on our increasing understanding of the biological aging process on a cellular level, we aim to dissect possible interactions of aging- and HIV-1 infection-induced effects and their role in neurocognitive decline. Thus, we begin by providing a brief overview of the clinical aspects of HIV-1-related neurocognitive impairment and review the accumulating evidence implicating aging in its development (Part I). We then discuss potential interactions between aging-associated pathways and HIV-1-induced effects at the molecular level (Part II).

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm, artificial intelligence (AI) is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations. Living human organoids (LOs) have a great potential for in vitro reshaping many aspects of in vivo true human organs, including organ development, disease occurrence, and drug responses. To date, AI has driven the revolutionary advances of human organoids in life science, precision medicine and pharmaceutical science in an unprecedented way. Herein, we provide a forward-looking review, the frontiers of LOs, covering the engineered construction strategies and multidisciplinary technologies for developing LOs, highlighting the cutting-edge achievements and the prospective applications of AI in LOs, particularly in biological study, disease occurrence, disease diagnosis and prediction and drug screening in preclinical assay. Moreover, we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies. The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.

Human Brain Organoid Research and Applications: Where and How to Meet Legal Challenges?

An ethical and legal framework is needed to regulate the rapidly developing human brain organoid research field properly. However, considering the legal issues involved in human brain organoid research remains underdeveloped and scattered. This article reviews the legal issues of human brain organoid research, grouping them into the following five broad themes: (1) consciousness, (2) legal status, (3) consent, (4) ownership, and (5) transplantation. The issues in each topic include both the urgent (e.g., appropriate forms of consent) and the speculative (e.g., protection of conscious human brain organoids). Therefore, we have attempted to be as explicit as possible about the timescale within which each issue will be realized and to prioritize each. Examining these issues has revealed legal issues specific to human brain organoid research and issues common to research in other fields. Further discussion of human brain organoid research from a legal perspective is needed in the future, considering discussions in related fields.

Revealing single-neuron and network-activity interaction by combining high-density microelectrode array and optogenetics

The synchronous activity of neuronal networks is considered crucial for brain function. However, the interaction between single-neuron activity and network-wide activity remains poorly understood. This study explored this interaction within cultured networks of rat cortical neurons. Employing a combination of high-density microelectrode array recording and optogenetic stimulation, we established an experimental setup enabling simultaneous recording and stimulation at a precise single-neuron level that can be scaled to the level of the whole network. Leveraging our system, we identified a network burst-dependent response change in single neurons, providing a possible mechanism for the network-burst-dependent loss of information within the network and consequent cognitive impairment during epileptic seizures. Additionally, we directly recorded a leader neuron initiating a spontaneous network burst and characterized its firing properties, indicating that the bursting activity of hub neurons in the brain can initiate network-wide activity. Our study offers valuable insights into brain networks characterized by a combination of bottom-up self-organization and top-down regulation.

A two-faced membrane channel

Contrasting surface properties activate a feedback loop system for a complete oil-and-water separation.

Real-time hardware emulation of neural cultures: A comparative study of in vitro, in silico and in duris silico models

Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human-machine interfacing.

Organoid intelligence for developmental neurotoxicity testing

The increasing prevalence of neurodevelopmental disorders has highlighted the need for improved testing methods to determine developmental neurotoxicity (DNT) hazard for thousands of chemicals. This paper proposes the integration of organoid intelligence (OI); leveraging brain organoids to study neuroplasticity in vitro, into the DNT testing paradigm. OI brings a new approach to measure the impacts of xenobiotics on plasticity mechanisms - a critical biological process that is not adequately covered in current DNT in vitro assays. Finally, the integration of artificial intelligence (AI) techniques will further facilitate the analysis of complex brain organoid data to study these plasticity mechanisms.

Integrating adult neurogenesis and human brain organoid models to advance epilepsy and associated behavioral research

Epilepsy is a chronic neurological disorder characterized by recurring, unprovoked seizures, asymmetrical electroencephalogram patterns, and other pathological abnormalities. The hippocampus plays a pivotal role in learning, memory consolidation, attentional control, and pattern separation. Impairment of hippocampal network circuitry can induce long-term cognitive and memory dysfunction. In this review, we discuss how aberrant adult neurogenesis and plasticity collectively alter the network balance for information processing within the hippocampal neural network. Subsequently, we explore the potential of human brain organoids integrated into microelectrode array technology as an electrophysiological tool. We also discuss the utilization of a closed-loop platform that connects the brain organoid to a mobile robot in a virtual environment. While in vivo models provide valuable insights into some aspects of epileptogenesis, such as the impact of adult neurogenesis on hippocampal function, brain organoids are indispensable for comprehensively studying epileptogenesis involving genetic mutations that underlie human epilepsy. More importantly, a combinational approach using brain organoids on MEA paves the way for studying impaired plasticity and abnormal information processing within epileptic neural networks. This innovative in vitro approach may provide a new pathway for investigating the behavioral outcomes of aberrant neural networks when integrated with a mobile robot, closing the loop between the neural network in brain organoids and the mobile robot. In this review, we aim to discuss the use of each model to study the behavioral changes in epilepsy and highlight the benefits of both in vivo and in vitro models for understanding the behavioral aspects of epilepsy.

Wirelessly steerable bioelectronic neuromuscular robots adapting neurocardiac junctions

Biological motions of native muscle tissues rely on the nervous system to interface movement with the surrounding environment. The neural innervation of muscles, crucial for regulating movement, is the fundamental infrastructure for swiftly responding to changes in body tissue requirements. This study introduces a bioelectronic neuromuscular robot integrated with the motor nervous system through electrical synapses to evoke cardiac muscle activities and steer robotic motion. Serving as an artificial brain and wirelessly regulating selective neural activation to initiate robot fin motion, a wireless frequency multiplexing bioelectronic device is used to control the robot. Frequency multiplexing bioelectronics enables the control of the robot locomotion speed and direction by modulating the flapping of the robot fins through the wireless motor innervation of cardiac muscles. The robots demonstrated an average locomotion speed of ~0.52 ± 0.22 millimeters per second, fin-flapping frequency up to 2.0 hertz, and turning locomotion path curvature of ~0.11 ± 0.04 radians per millimeter. These systems will contribute to the expansion of biohybrid machines into the brain-to-motor frontier for developing autonomous biohybrid systems capable of advanced adaptive motor control and learning.

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.

Brain organoid maturation and implantation integration based on electrical signals input

INTRODUCTION

Brain organoids are believed to be able to regenerate impaired neural circuits and reinstate brain functionality. The neuronal activity of organoids is considered a crucial factor for restoring host function after implantation. However, the optimal stage of brain organoid post-transplantation has not yet been established. External electrical signal plays a crucial role in the physiology and development of a majority of human tissues. However, whether electrical input modulates the development of brain organoids, making them ideal transplant donors, is elusive.

METHODS

Bioelectricity was input into cortical organoids by electrical stimulation (ES) with a multi-electrode array (MEA) to obtain a better-transplanted candidate with better viability and maturity, realizing structural-functional integration with the host brain.

RESULTS

We found that electrical stimulation facilitated the differentiation and maturation of organoids, displaying well-defined cortical plates and robust functional electrophysiology, which was probably mediated via the pathway of calcium-calmodulin (CaM) dependent protein kinase II (CAMK II)-protein kinase A (PKA)-cyclic-AMP response binding protein (pCREB). The ES-pretreated D40 organoids displayed superior cell viability and higher cell maturity, and were selected to transplant into the damaged primary sensory cortex (S1) of host. The enhanced maturation was exhibited within grafts after transplantation, including synapses and complex functional activities. Moreover, structural-functional integration between grafts and host was observed, conducive to strengthening functional connectivity and restoring the function of the host injury.

CONCLUSION

Our findings supported that electrical stimulation could promote the development of cortical organoids. ES-pretreated organoids were better-transplanted donors for strengthening connectivity between grafts and host. Our work presented a new physical approach to regulating organoids, potentially providing a novel translational strategy for functional recovery after brain injury. In the future, the development of 3D flexible electrodes is anticipated to overcome the drawbacks of 2D planar MEA, promisingly achieving multimodal stimulation and long-term recordings of brain organoids.

Debates on humanization of human-animal brain chimeras - are we putting the cart before the horses?

Research on human-animal chimeras have elicited alarms and prompted debates. Those involving the generation of chimeric brains, in which human brain cells become anatomically and functionally intertwined with their animal counterparts in varying ratios, either via xenografts or embryonic co-development, have been considered the most problematic. The moral issues stem from a potential for "humanization" of the animal brain, as well as speculative changes to the host animals' consciousness or sentience, with consequential alteration in the animal hosts' moral status. However, critical background knowledge appears to be missing to resolve these debates. Firstly, there is no consensus on animal sentience vis-à-vis that of humans, and no established methodology that would allow a wholesome and objective assessment of changes in animal sentience resulting from the introduction of human brain cells. Knowledge in interspecies comparative neuropsychology that could allow effective demarcation of a state of "humanization" is also lacking. Secondly, moral status as a philosophical construct has no scientific and objective points of reference. Either changes in sentience or humanization effects would remain unclear unless there are some neuroscientific research grounding. For a bioethical stance based on moral status of human-animal brain chimera to make meaningful contributions to regulatory policies, it might first need to be adequately informed by, and with its arguments constructed, in a manner that are factually in line with the science. In may be prudent for approved research projects involving the generation of human-animal brain chimera to have a mandatory component of assessing plausible changes in sentience.

Why brain organoids are not conscious yet

Rapid advances in human brain organoid technologies have prompted the question of their consciousness. Although brain organoids resemble many facets of the brain, their shortcomings strongly suggest that they do not fit any of the operational definitions of consciousness. As organoids gain internal processing systems through statistical learning and closed loop algorithms, interact with the external world, and become embodied through fusion with other organ systems, questions of biosynthetic consciousness will arise.

Recent advances and applications of human brain models

Recent advances in human pluripotent stem cell (hPSC) technologies have prompted the emergence of new research fields and applications for human neurons and brain organoids. Brain organoids have gained attention as an in vitro model system that recapitulates the higher structure, cellular diversity and function of the brain to explore brain development, disease modeling, drug screening, and regenerative medicine. This progress has been accelerated by abundant interactions of brain organoid technology with various research fields. A cross-disciplinary approach with human brain organoid technology offers a higher-ordered advance for more accurately understanding the human brain. In this review, we summarize the status of neural induction in two- and three-dimensional culture systems from hPSCs and the modeling of neurodegenerative diseases using brain organoids. We also highlight the latest bioengineered technologies for the assembly of spatially higher-ordered neural tissues and prospects of brain organoid technology toward the understanding of the potential and abilities of the human brain.

Detection of Memory Engrams in Mammalian Neuronal Circuits

It has long been assumed that activity patterns persist in neuronal circuits after they are first experienced, as part of the process of information processing and storage by the brain. However, these "reverberations" of current activity have not been directly observed on a single-neuron level in a mammalian system. Here we demonstrate that specific induced activity patterns are retained in mature cultured hippocampal neuronal networks. Neurons within the network are induced to fire at a single frequency or in a more complex pattern containing two distinct frequencies. After the stimulation was stopped, the subsequent neuronal activity of hundreds of neurons in the network was monitored. In the case of single-frequency stimulation, it was observed that many of the neurons continue to fire at the same frequency that they were stimulated to fire at. Using a recurrent neural network trained to detect specific, more complex patterns, we found that the multiple-frequency stimulation patterns were also retained within the neuronal network. Moreover, it appears that the component frequencies of the more complex patterns are stored in different populations of neurons and neuron subtypes.

A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network

While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly "resonate" to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this, we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.

Microinstrumentation for Brain Organoids

Brain organoids are three-dimensional aggregates of self-organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs); and optical, magnetic, and acoustic techniques, has limitations when applied to three-dimensional (3D) organoids, primarily due to their limits with inherently two-dimensional geometry and interfacing. Hence, there is an urgent need to develop new instrumentation compatible with live cell culture techniques and with scalable 3D formats relevant to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid-machine interfaces. Specifically, this article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast-scan optical techniques, buckling and self-folding MEAs, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two-way information transfer with brain organoids. This article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.

The Promise and Potential of Brain Organoids

Brain organoids are 3D in vitro culture systems derived from human pluripotent stem cells that self-organize to model features of the (developing) human brain. This review examines the techniques behind organoid generation, their current and potential applications, and future directions for the field. Brain organoids possess complex architecture containing various neural cell types, synapses, and myelination. They have been utilized for toxicology testing, disease modeling, infection studies, personalized medicine, and gene-environment interaction studies. An emerging concept termed Organoid Intelligence (OI) combines organoids with artificial intelligence systems to generate learning and memory, with the goals of modeling cognition and enabling biological computing applications. Brain organoids allow neuroscience studies not previously achievable with traditional techniques, and have the potential to transform disease modeling, drug development, and the understanding of human brain development and disorders. The aspirational vision of OI parallels the origins of artificial intelligence, and efforts are underway to map a roadmap toward its realization. In summary, brain organoids constitute a disruptive technology that is rapidly advancing and gaining traction across multiple disciplines.

Active Inference for Learning and Development in Embodied Neuromorphic Agents

Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.

Investigating Communication Dynamics in Neuronal Network using 3D Gold Microelectrode Arrays

Although in vitro neuronal network models hold great potential for advancing neuroscience research, with the capacity to provide fundamental insights into mechanisms underlying neuronal functions, the dynamics of cell communication within such networks remain poorly understood. Here, we develop a customizable, polymer modified three-dimensional gold microelectrode array with sufficient stability for high signal-to-noise, long-term, neuronal recording of cultured networks. By using directed spatial and temporal patterns of electrical stimulation of cells to explore synaptic-based communication, we monitored cell network dynamics over 3 weeks, quantifying communication capability using correlation heatmaps and mutual information networks. Analysis of synaptic delay and signal speed between cells enabled us to establish a communication connectivity model. We anticipate that our discoveries of the dynamic changes in communication across the neuronal network will provide a valuable tool for future studies in understanding health and disease as well as in developing effective platforms for evaluating therapies.

Pruning functional connections in human induced pluripotent stem cell derived neural networks

The failure of human neuronal stem cells to integrate with brain tissue suggests the need to provide functional cues to modify and re-organize the existing naive network. Understanding how human neural networks respond to external stimuli is crucial to realizing this goal. Here, we stimulate a human induced pluripotent stem cell (hIPSC)-derived neural network on a microelectrode array in a Hebbian fashion to explore the resulting network changes. Short exposure to our stimulation protocol resulted in rapid de-correlation of prior functional connections as well as the emergence of a few strong negative connections. Furthermore, stimulation of the network increased median firing rates with observed network reorganization maintained over the course of 15 days.

Neuroinflammation-on-a-chip for multiple sclerosis research: a narrative review

Introduction

Multiple sclerosis (MS) is a chronic inflammatory condition that impacts the central nervous system. It is distinguished by processes like demyelination, gliosis, neuro-axonal harm, and inflammation. The prevailing theory suggests that MS originates from an immune response directed against the body's own antigens within the central nervous system.

Aim

The main aim of this research paper "Neuroinflammation-on-a-Chip" for studying multiple sclerosis is to enhance our comprehension of MS development, demonstrate the application of cutting-edge technology, and potentially provide valuable insights for therapeutic approaches.

Methods

The available literature for this Narrative Review was searched on various bibliographic databases, PubMed, NCBI, and many other medical references using an individually verified, prespecified approach. Studies regarding the significance of MS and its neuroinflammatory pathogenesis in addition to the development and optimization of neuroinflammatory-on-a-chip and the advancement in innovations in this field have been reviewed in this research for a better understanding of "Neuroinflammation-on-a-chip for multiple sclerosis". The level of evidence of the included studies was considered as per the Centre for Evidence-Based Medicine recommendations.

Results

Several studies have indicated that the brain-chip model closely mimics cortical brain tissue compared to commonly used conventional cell culture methods like the Transwell culture system. Additionally, these studies have clearly demonstrated that further research using brain chips has the potential to enhance our understanding of the molecular mechanisms and roles of blood-brain barrier (BBB) transporters in both normal and disease conditions.

Conclusion

Understanding neuroinflammation processes remains essential to establish new MS treatments approaches. The utilization of brain chips promises to advance our understanding of the molecular processes involving BBB transporters, both in normal and diseased states. Further research needs to be addressed in order to enhance the performance and understanding of neuroinflammation on a chip, hence aiming to provide more effective treatments for all CNS diseases.

The Role of Biophysical Factors in Organ Development: Insights from Current Organoid Models

Biophysical factors play a fundamental role in human embryonic development. Traditional in vitro models of organogenesis focused on the biochemical environment and did not consider the effects of mechanical forces on developing tissue. While most human tissue has a Young's modulus in the low kilopascal range, the standard cell culture substrate, plasma-treated polystyrene, has a Young's modulus of 3 gigapascals, making it 10,000-100,000 times stiffer than native tissues. Modern in vitro approaches attempt to recapitulate the biophysical niche of native organs and have yielded more clinically relevant models of human tissues. Since Clevers' conception of intestinal organoids in 2009, the field has expanded rapidly, generating stem-cell derived structures, which are transcriptionally similar to fetal tissues, for nearly every organ system in the human body. For this reason, we conjecture that organoids will make their first clinical impact in fetal regenerative medicine as the structures generated ex vivo will better match native fetal tissues. Moreover, autologously sourced transplanted tissues would be able to grow with the developing embryo in a dynamic, fetal environment. As organoid technologies evolve, the resultant tissues will approach the structure and function of adult human organs and may help bridge the gap between preclinical drug candidates and clinically approved therapeutics. In this review, we discuss roles of tissue stiffness, viscoelasticity, and shear forces in organ formation and disease development, suggesting that these physical parameters should be further integrated into organoid models to improve their physiological relevance and therapeutic applicability. It also points to the mechanotransductive Hippo-YAP/TAZ signaling pathway as a key player in the interplay between extracellular matrix stiffness, cellular mechanics, and biochemical pathways. We conclude by highlighting how frontiers in physics can be applied to biology, for example, how quantum entanglement may be applied to better predict spontaneous DNA mutations. In the future, contemporary physical theories may be leveraged to better understand seemingly stochastic events during organogenesis.

Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function

Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.

Advances in Material-Assisted Electromagnetic Neural Stimulation

Bioelectricity plays a crucial role in organisms, being closely connected to neural activity and physiological processes. Disruptions in the nervous system can lead to chaotic ionic currents at the injured site, causing disturbances in the local cellular microenvironment, impairing biological pathways, and resulting in a loss of neural functions. Electromagnetic stimulation has the ability to generate internal currents, which can be utilized to counter tissue damage and aid in the restoration of movement in paralyzed limbs. By incorporating implanted materials, electromagnetic stimulation can be targeted more accurately, thereby significantly improving the effectiveness and safety of such interventions. Currently, there have been significant advancements in the development of numerous promising electromagnetic stimulation strategies with diverse materials. This review provides a comprehensive summary of the fundamental theories, neural stimulation modulating materials, material application strategies, and pre-clinical therapeutic effects associated with electromagnetic stimulation for neural repair. It offers a thorough analysis of current techniques that employ materials to enhance electromagnetic stimulation, as well as potential therapeutic strategies for future applications.

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Landscape of human organoids: Ideal model in clinics and research

In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.