The wide spread of experimental techniques such as fMRI, calcium imaging and electron microscopy etc and their applications bring in enormous amount of data. Subsequently, neuroscientists are facing increasing challenges in modeling neural networks at various levels and preforming simulations within various boundary condition when navigating themselves in this massive amount of data. Specifically, they are calling for advanced methodologies and technologies to generate biological neural parameters and synaptic connectivity from abundant experimental data, as well as to describe and optimize biological neural networks such as spiking neural network that complying to biologically plausible plasticity laws.
Machine learning (ML) technologies, especially deep learning with unpreceded capacities in feature extraction and function fitting, demonstrated great potential in modeling and data processing as a tool and to accelerate hardware improvements, and has quickly adopted by researchers beyond traditional computer scientists who deals with large amount of data. Neurological modeling and simulations are no exception, but yet to catch up.
This collection aims at bridging the gap between neuroscience researchers investigating biological models and computer scientists developing methodologies and tools, covering the entire process of modeling and analysis from data cleaning and preparation to modeling, and to simulation and optimization. We hope to demonstrate a set of effective ways to process neuroscience data from either experiments or public datasets for computational neural scientists, while inspire machine learning researchers to generate ever-efficient methods and tools specialized in handling neuroscience data, changing the research paradigm of neuroscience.
Potential topics of interests include, but are not limited to the following, and for articles dealing with experimental data, we would like them to be used as bases for models and simulations:
* Generating and calibrating rational neuronal models, connectivity of a neural circuit and stimuli from synthesized data and/or experimental data;
* Modeling and optimization of biological neural networks based on biologically plausible plasticity laws and experimental data;
* Novel methods to training biological neural networks for task datasets;
* Computing framework and/or modeling interface for computational neural models
* Data processing & modeling benchmarking metrics, datasets, and tasks for neuroscience and brain-inspired computing.
The wide spread of experimental techniques such as fMRI, calcium imaging and electron microscopy etc and their applications bring in enormous amount of data. Subsequently, neuroscientists are facing increasing challenges in modeling neural networks at various levels and preforming simulations within various boundary condition when navigating themselves in this massive amount of data. Specifically, they are calling for advanced methodologies and technologies to generate biological neural parameters and synaptic connectivity from abundant experimental data, as well as to describe and optimize biological neural networks such as spiking neural network that complying to biologically plausible plasticity laws.
Machine learning (ML) technologies, especially deep learning with unpreceded capacities in feature extraction and function fitting, demonstrated great potential in modeling and data processing as a tool and to accelerate hardware improvements, and has quickly adopted by researchers beyond traditional computer scientists who deals with large amount of data. Neurological modeling and simulations are no exception, but yet to catch up.
This collection aims at bridging the gap between neuroscience researchers investigating biological models and computer scientists developing methodologies and tools, covering the entire process of modeling and analysis from data cleaning and preparation to modeling, and to simulation and optimization. We hope to demonstrate a set of effective ways to process neuroscience data from either experiments or public datasets for computational neural scientists, while inspire machine learning researchers to generate ever-efficient methods and tools specialized in handling neuroscience data, changing the research paradigm of neuroscience.
Potential topics of interests include, but are not limited to the following, and for articles dealing with experimental data, we would like them to be used as bases for models and simulations:
* Generating and calibrating rational neuronal models, connectivity of a neural circuit and stimuli from synthesized data and/or experimental data;
* Modeling and optimization of biological neural networks based on biologically plausible plasticity laws and experimental data;
* Novel methods to training biological neural networks for task datasets;
* Computing framework and/or modeling interface for computational neural models
* Data processing & modeling benchmarking metrics, datasets, and tasks for neuroscience and brain-inspired computing.
