Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons DOI
Salvador Durá-Bernal, Beatriz Herrera, Carmen Alina Lupaşcu

et al.

Journal of Neuroscience, Journal Year: 2024, Volume and Issue: 44(40), P. e1236242024 - e1236242024

Published: Oct. 2, 2024

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing circuit composition, connectivity, and activity is transforming neuroscience. However, integrating interpreting this data remains challenging. Concurrently, advances in supercomputing sophisticated modeling tools now enable development highly detailed, biophysical models. These mechanistic models offer a method systematically integrate experimental data, facilitating investigations into structure, function, disease. This review, based on Society for Neuroscience 2024 MiniSymposium, aims disseminate recent broader community. It highlights (1) examples current various regions developed through integration; (2) their predictive capabilities regarding cellular mechanisms underlying recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) function; (3) use simulating biomarkers diseases like epilepsy, depression, schizophrenia, Parkinson's, aiding understanding underpinnings developing novel treatments. review showcases state-of-the-art covering hippocampus, somatosensory, visual, motor, auditory cortical, thalamic circuits across species. predict neural at multiple scales provide insights sensation, motor behavior, signals, coding, disease, pharmacological interventions, stimulation. Collaboration with neuroscientists clinicians essential validation these models, particularly as grow. Hence, foster interest detailed leading cross-disciplinary collaborations that accelerate research.

Language: Английский

Integrating multimodal data to understand cortical circuit architecture and function DOI
Anton Arkhipov, Nuno Maçarico da Costa, Saskia de Vries

et al.

Nature Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

1
Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons DOI
Salvador Durá-Bernal, Beatriz Herrera, Carmen Alina Lupaşcu

et al.

Journal of Neuroscience, Journal Year: 2024, Volume and Issue: 44(40), P. e1236242024 - e1236242024

Published: Oct. 2, 2024

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing circuit composition, connectivity, and activity is transforming neuroscience. However, integrating interpreting this data remains challenging. Concurrently, advances in supercomputing sophisticated modeling tools now enable development highly detailed, biophysical models. These mechanistic models offer a method systematically integrate experimental data, facilitating investigations into structure, function, disease. This review, based on Society for Neuroscience 2024 MiniSymposium, aims disseminate recent broader community. It highlights (1) examples current various regions developed through integration; (2) their predictive capabilities regarding cellular mechanisms underlying recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) function; (3) use simulating biomarkers diseases like epilepsy, depression, schizophrenia, Parkinson's, aiding understanding underpinnings developing novel treatments. review showcases state-of-the-art covering hippocampus, somatosensory, visual, motor, auditory cortical, thalamic circuits across species. predict neural at multiple scales provide insights sensation, motor behavior, signals, coding, disease, pharmacological interventions, stimulation. Collaboration with neuroscientists clinicians essential validation these models, particularly as grow. Hence, foster interest detailed leading cross-disciplinary collaborations that accelerate research.

Language: Английский

Citations

1