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Key research themes
1. How can embodied brain simulations provide a validated platform for whole brain emulation experiments?
This theme explores integrating biologically realistic brain models with simulated bodies and environments (embodiment) to facilitate validation of whole brain emulation (WBE) models. Realistic sensory-motor tasks within a dynamic environment enable testing and refinement of brain models, overcoming current computational limits of real-time physical embodiment. Embodied simulations also foster closed-loop interactions crucial for understanding sensorimotor integration and cognition in simulated brains.
Key finding: Introduces the Neurorobotics Platform (NRP), a web-based framework that interconnects detailed spiking neural network brain models with simulated robot bodies and environments for in silico experimentation. The platform... Read more
Key finding: Develops a scalable software framework deploying large-scale, biologically realistic spiking neural network simulations embodied in a biomechanically accurate musculoskeletal system interacting with a virtual environment.... Read more
Key finding: Presents SNS-Toolbox, an open-source Python package supporting biologically plausible synthetic nervous system design with spiking and non-spiking conductance-based neurons capable of real-time or faster simulation on... Read more
Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues
Key finding: Highlights the importance of large-scale brain simulations for understanding neural correlates of consciousness (NCC) and their application in clinical contexts such as disorders of consciousness. Emphasizes that realistic... Read more
2. What are the theoretical, ethical, and practical challenges in realizing whole brain emulation, particularly regarding substrate fidelity and consciousness?
This theme investigates philosophical and ethical considerations in whole brain emulation including identity persistence, consciousness, and the moral status of emulated brains. It assesses competing transfer mechanisms (scan-and-copy vs gradual replacement), the role of non-neuronal and non-synaptic factors in emulation fidelity, and the implications of potential sentience in brain organoids and digital minds. Addressing these challenges is central to ensuring that WBE achieves continuity of self and ethical deployment.
Key finding: Argues against the common conjecture that nondestructive mind uploading preserves identity only in the original body, proposing a branching identity model instead where the original and the upload share identity lineage. This... Read more
Key finding: Demonstrates that whole brain emulation requires modeling not only neurons and synapses but also extensive non-synaptic signaling (via monoamines, neuropeptides, extracellular vesicles) and non-neuronal components (e.g.,... Read more
Key finding: Evaluates neural correlates of consciousness (NCC) theories in the context of biologically inspired neural networks (BNN) built from human cortical cells embedded in digital environments. Reports that Information Integration... Read more
Key finding: Proposes the 'Sentiomics' framework to induce sentience in brain organoids via rich, dynamically patterned stimulation mimicking natural environmental signals, thereby supporting the development of mental potentialities in... Read more
Key finding: From a neo-Aristotelian naturalist virtue ethics perspective, challenges the straightforward attribution of human-like moral status to WBEs by emphasizing their artifact status and the differences from biological humans.... Read more
3. What are the current technological and methodological approaches for brain data acquisition, analysis, and simulation to support Whole Brain Emulation?
This theme covers advancements in neuroimaging, connectomics, neural data visualization, and simulation software frameworks that underpin WBE efforts. It addresses large-scale data challenges, cross-platform software tools, and multimodal integration from electrode arrays to cloud platforms. Improved technologies for comprehensive brain data collection, analysis, and scalable simulation form the technical foundation needed to approximate the complexity of the human brain for accurate emulation.
Key finding: Develops brainlife.io, an open-source cloud platform enabling scalable, interoperable, and FAIR-compliant neuroimaging data processing and analysis integrating diverse data types (MRI, EEG, etc.) and toolkits. By lowering... Read more
Key finding: Creates HPC workflows and visualization tools for rendering outputs from large-scale neural simulations (e.g., membrane potentials, spiking activity, local field potentials) using packages like ParaView. Real-time and... Read more
Key finding: Apart from embodiment, emphasizes the orchestration of multiple simulation engines and data exchange between brain models and robot/environment simulators. This software integration is pivotal for managing complex... Read more
Key finding: Presents novel technological contributions, such as the Digital Consciousness Fidelity Index (DCFI) and advanced error-correction algorithms for high-resolution neural imaging, addressing accuracy in synaptic and subcellular... Read more