Big data in neuroscience is driving the development of methods seeking to predict the state of neurons and neuronal circuitry. Model-based methods (e.g., data assimilation) and data-based methods (e.g. reservoir computing, AI) have had great success at constructing digital twins of neurons and small circuits such as central pattern generators. These digital twins have begun making remarkably accurate predictions of observable quantities such as membrane voltage oscillations and are unique in predicting the dynamics of variables inaccessible to experiment (ionic current waveforms, gate probabilities). Successful digital twins have the potential to advance fundamental biology by revealing channelopathies, they can help clinicians make diagnosis, provide therapies for chronic diseases when embodied in-silico as neurostimulation devices (e.g., neuronal pacemaker) or brain-machine interfaces.
The topic is aimed at providing a forum where various techniques for constructing digital twins in neurosciences will be explored. The topic will review recent progress in parameter inference and AI methods for transferring information from data to models. The topic will identify roadblocks and propose solutions to overcome them. Some of these roadblocks include model overspecification, ill-posed inference problems, observability, the need for objective criteria (Shannon entropy) to obtain for stimulation protocols that fulfill identifiability criteria. Contributions from neuroscientists and clinicians will review examples of the use of AI tools in neurosurgery e.g., for surgically removing tumors and identifying needs e.g. for digitals twins of healthy cells which cannot be extracted for ethical reasons. The implementation of digital twins in bioengineering will also be covered with a particular focus in bioelectronic medicine (e.g., neuronal pacemaker) and brain machine interfaces (Neuralink).
This Research Topic welcomes contributions in the form of Original Articles, Method Articles, Reviews around the following:
Main theme: Building digital twins in neuroscience.
Methods: Dynamical systems, nonlinear optimization, parameter inference in neuroscience, data assimilation, reservoir computing, AI applications to neuroscience, ill-posed problems, time series analysis
Application fields: Digital twins in bioelectronic medicine, Digital twins in bioengineering, Digital twins in clinical neuroscience, Brain machine interfaces.
Big data in neuroscience is driving the development of methods seeking to predict the state of neurons and neuronal circuitry. Model-based methods (e.g., data assimilation) and data-based methods (e.g. reservoir computing, AI) have had great success at constructing digital twins of neurons and small circuits such as central pattern generators. These digital twins have begun making remarkably accurate predictions of observable quantities such as membrane voltage oscillations and are unique in predicting the dynamics of variables inaccessible to experiment (ionic current waveforms, gate probabilities). Successful digital twins have the potential to advance fundamental biology by revealing channelopathies, they can help clinicians make diagnosis, provide therapies for chronic diseases when embodied in-silico as neurostimulation devices (e.g., neuronal pacemaker) or brain-machine interfaces.
The topic is aimed at providing a forum where various techniques for constructing digital twins in neurosciences will be explored. The topic will review recent progress in parameter inference and AI methods for transferring information from data to models. The topic will identify roadblocks and propose solutions to overcome them. Some of these roadblocks include model overspecification, ill-posed inference problems, observability, the need for objective criteria (Shannon entropy) to obtain for stimulation protocols that fulfill identifiability criteria. Contributions from neuroscientists and clinicians will review examples of the use of AI tools in neurosurgery e.g., for surgically removing tumors and identifying needs e.g. for digitals twins of healthy cells which cannot be extracted for ethical reasons. The implementation of digital twins in bioengineering will also be covered with a particular focus in bioelectronic medicine (e.g., neuronal pacemaker) and brain machine interfaces (Neuralink).
This Research Topic welcomes contributions in the form of Original Articles, Method Articles, Reviews around the following:
Main theme: Building digital twins in neuroscience.
Methods: Dynamical systems, nonlinear optimization, parameter inference in neuroscience, data assimilation, reservoir computing, AI applications to neuroscience, ill-posed problems, time series analysis
Application fields: Digital twins in bioelectronic medicine, Digital twins in bioengineering, Digital twins in clinical neuroscience, Brain machine interfaces.