Michael Hines - US grants

The contributions of anatomy and biophysical properties to the function of neurons and neural circuits are best understood with the aid of computer simulations. NEURON, a program which we have developed and provide freely for Mac OS X, MS Windows, and UNIX, has simplified the creation and analysis of neural models for nonspecialists in numerical methods and programming. It can simulate individual neurons and networks of neurons on workstations, clusters, and massively parallel supercomputers. Model properties may include, but are not limited to, complex branching morphology, multiple channel types, inhomogeneous channel distribution, ionic diffusion, extracellular fields, electronic instrumentation, and artificial spiking neurons. NEURON is used by neuroscientists around the world to investigate cellular and network mechanisms that are involved in inborn and acquired disorders such as epilepsy, multiple sclerosis, and disorders of learning and memory, and how they are affected by therapeutic interventions such as medications and deep brain stimulation. The community of NEURON users is growing steadily as increasing numbers of investigators, experimentalists and theoreticians alike, incorporate it into their research plans. Since neuroscience is a rapidly advancing field, investigators' needs are always changing. Consequently NEURON must continue to develop in several critical areas in order to continue to satisfy the evolving requirements of neuroscience research. There is a need for ever greater computational resources; we will address this by extending NEURON's new parallel features with selectable methods optimized for widely used classes of network topology. There is a need to access analysis tools that have become available in other biological sciences, the physical sciences, and engineering; to this end, NEURON is adopting a modern programming language (Python). There is a need for more flexible and powerful ways to perform dynamic clamp experiments; NEURON can be employed in this mode to great advantage, but the setup, hardware testing, and experiment configuration effort must be reduced. The NEURON community can fully exploit these advanced capabilities only if they are easy to install and use. We have also identified patterns of usage that call for the creation of new GUI tools to facilitate model specification: a finite state machine builder for specifying new synaptic mechanisms and artificial spiking cells, and a tool for specifying the geometry and kinetics of ion and second messenger accumulation mechanisms. These needs require concerted efforts in the areas of increasing performance, robustness, packaging, documentation, training, and user consultation and collaboration. In this proposal we present a research plan that is designed to address these areas.

Not available.

DESCRIPTION (provided by applicant): The overall aim of SenseLab is to integrate multidisciplinary neuroscience data by means of innovative databases and tools, using the olfactory system as a model which can generalize across the nervous system. For this purpose we have created 8 interoperable databases that serve growing user communities for experimental data and computational models at multiple levels, from genes and proteins through neurons to circuits. SenseLab has three foundations: neuroinformatics directed by Perry Miller, experimental data by Gordon Shepherd, and computational modeling by Michael Hines. One focus will be on ModelDB, which is growing strongly with over 800 computational models. We will build new functionality to enable the models to be explored with new tools including ModelSearch and ModelView. We will support an emerging field of brain microcircuits through MicrocircuitDB, which currently contains over 200 models. A new BrainPathPhysiolDB will contain over 100 models of neuron pathophysiology with clinical relevance. A new ORModelDB will add molecular models to the Olfactory Receptor Database (ORDB) to enhance the utility of the 14,000+ chemosensory genes and proteins that it currently contains. To enhance interoperation we will continue to work closely with the Neuroscience Information Framework (NIF) and the International Neuroinformatics Coordination Facility (INCF) to develop a general ontology for neurons and microcircuits. Support by Dr. Miller and his colleagues in the Yale Center for Medical Informatics will be critical, and enable SenseLab to continue developing its state-of-the-art infrastructure and tools for database construction and interoperation. We will explore innovative ways in which individual SenseLab databases can be designed, adapted, and/or enhanced to facilitate robust interoperation with other neuroscience databases, tools, and resources such as the NIF. In our experimental and computational studies we will develop a new generation of large-scale microcircuit models which realistically represent the detailed 3 dimensional morphology of multiple neuron types with overlapping dendritic fields and distributed synaptic interactions. The NEURON simulator, developed by Dr. Hines, is unique in its capability for computing this model on massively parallel cluster computers. We will test the model with experimental data from an ongoing collaboration with the lab of Dr. Justus Verhagen, and will share the model with other labs working on the olfactory bulb and other systems. In summary, this multidisciplinary and multilevel approach using integration of experimental data into realistic computational simulations should serve as a model for analysis of olfactory processing and for current attempts at data integration throughout the brain.

community

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Abstract The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these `macro-scale' signals without simultaneous invasive recordings. This difficulty limits the translation of MEG/EEG finding into novel principles of information processing, or into new treatment modalities for neural pathologies. As such, there is a pressing need, and a unique opportunity, to bridge the `macro-scale' single with the underlying `meso-scale? cellular and circuit level generators. This problem is ideal for neural modeling where we can have specificity at both scales. We propose to build a user-friendly GUI driven neural modeling software tool, ?Human Neocortical Neurosolver (HNN)? that enables researchers without mathematical or neural modeling experience to test and develop hypotheses on the cellular and circuit level origin of their source localized MEG/EEG or ECoG data. Our software will work from a foundation of detailed anatomical and biophysical constraints to generate hypotheses as to the neural origin of observed neocortical brain signals. We will work with identified test-case users with existing MEG/EEG data to develop our model into a tool they can use to test and develop specific hypotheses on the neural origin of activity from one or multiple brain areas. We will also integrate the model with the source localization software MNE, so researchers can compute MEG/EEG source estimates and test hypotheses on the neural origin of their data in one integrated software package. We will build resources for freely using and expanding the software through the Neuroscience Gateway Portal, and online documentation and a user forum for interaction between users and developers.