Hugo Cornelis

Python as a federation tool for GENESIS 3.0

The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be ‘glued’ together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational models. Employed in this way, the stand-alone software components of the GENESIS 3.0 simulator provide a framework for progressive federated software development in computational neuroscience.

A federated design for a neurobiological simulation engine: The CBI federated software architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components.

Dendritic excitability modulates dendritic information processing in a purkinje cell model

Using an electrophysiological compartmental model of a Purkinje cell we quantified the contribution of individual active dendritic currents to processing of synaptic activity from granule cells. We used mutual information as a measure to quantify the information from the total excitatory input current (IGlu) encoded in each dendritic current. In this context, each active current was considered an information channel. Our analyses showed that most of the information was encoded by the calcium (ICaP) and calcium activated potassium (IKc) currents. Mutual information between IGlu and ICaP and IKc was sensitive to different levels of excitatory and inhibitory synaptic activity that, at the same time, resulted in the same firing rate at the soma. Since dendritic excitability could be a mechanism to regulate information processing in neurons we quantified the changes in mutual information between IGlu and all Purkinje cell currents as a function of the density of dendritic Ca (gCaP) and Kca (gKc) conductances. We extended our analysis to determine the window of temporal integration of IGlu by ICaP and IKc as a function of channel density and synaptic activity. The window of information integration has a stronger dependence on increasing values of gKc than on gCaP, but at high levels of synaptic stimulation information integration is reduced to a few milliseconds. Overall, our results show that different dendritic conductances differentially encode synaptic activity and that dendritic excitability and the level of synaptic activity regulate the flow of information in dendrites.

The CBI architecture for computational simulation of realistic neurons and circuits in the GENESIS 3 software federation

Introduction As computational neuroscience in particular and computational biology in general continue to expand, simulator flexibility and adaptability will be increasingly important. Unfortunately, most current simulators have been constructed by either single individuals or a restricted group of insiders and therefore are limited in their scalability and adaptability. While, the scripting language interface of most neuronal modeling platforms generate highly efficient simulations, neither simulations nor the platform are easy to modify or extend. The primary reason is that the basecode for these simulators does not cleanly separate into its underlying components, thus reducing the scalability of the software. This results in increasing difficulty in the addition of more modern interfaces and new simulator functionality, alternate script parsers, or the implementation of different, field specific, interfaces including those facing the world wide web [1]. In this presentation we will describe an architecture we have developed within the Computational Biology Initiative (CBI) in San Antonio that was specifically designed to allow for simulator scalability. The CBI architecture is the basis for the third generation of the GENESIS simulator (GENESIS 3.0).

NeuroML for plug and play neuronal modeling

Modern software systems for simulation, database access, visualisation and data analysis, supporting distributed, extensible, evolutionary development, are designed around a small core that loads plug-in components. We have designed such a system for the neurosciences using an XML-based protocol, NeuroML, to exchange information between components. NeuroML supports high-level descriptions of data, models, references, and other types of information. We have built simulation kernel plug-ins, visualisation plug-ins, and model-description GUI plug-ins which interoperate in this framework. We describe the current status of these plug-ins and our future plans for further plug-in components.

Using GENESIS 3 for single neuron modeling

Introduction GENESIS, now referred to as GENESIS 3.0 (G3), has recently been reconfigured to adhere to the software design requirements specified by the Computational Biology Initiative (CBI) software architecture [1]. This highly modular approach to the development of simulator software leads to independent stand-alone components that integrate on a just-in-time basis to form a federated software platform. Different modules contribute functionality to the workflow of model development, model exploration and analysis, model simulation, and (ultimately) data analysis and model publication. Here we introduce the G3 workflow and through it present examples of how we use G3 to define and examine single neuron models, run simulations, and extract results using modern and backwardly compatible interfaces.

Neurospaces: Towards automated model partitioning for parallel computers

Parallel computers have the computing power needed to simulate biologically accurate neuronal network models. Partitioning is the process of cutting a model in pieces and assigning each piece to a CPU. Automatic partitioning algorithms for large models are difficult to design for two fundamental reasons. First, the algorithms must track the intrinsic asymmetries in the models and the dynamical behavior of the simulation. Second, the procedural nature of current modeling languages makes it difficult to extract the information needed by the algorithms. From the start, the Neurospaces modeling system has been designed to deal with large and complicated neuronal models. The declarative nature of the software system allows to extract any kind of information from the model. In this work, we first show how to extract the information needed to partition a large model for simulation on parallel computers. Next, we use this information to compute a possible partitioning for a small and a large network model.

The GENESIS 3.0 Project: a universal graphical user interface and database for research, collaboration, and education in computational neuroscience

Background The General Neural Simulation System (GENESIS) was first released for general use in 1988 as part of the first Methods in Computational Neuroscience Meeting at the Marine Biological Laboratory in Woods Hole, Mass. Since its release 19 years ago, GENESIS has provided one of the foundations for the ongoing course in Woods Hole, as well as courses offered by the European Union, courses in Mexico, Brazil, and India and soon in Japan, At last count GENESIS has also provided support for courses in at least 49 universities around the world where it has been used both as an instruction tool in realistic modeling of the nervous system, and as a simulation based tool for neurobiological education in general. The Book of GENESIS [1], which was designed to support both computational and neurobiological instruction has sold more than 6000 copies worldwide. This substantial support for the use of GENESIS in instruction has also provided the base for extensive and growing use of this software system in biological research providing the foundation for literally hundreds of peer reviewed scientific papers.

The implications of evolutionary changes in the dendritic morphology of cerebellar Purkinje cells for information processing

© 2013 Kidd et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The role of the Neurospaces project browser in the GENESIS 3 software federation: Design and targets

A computational model is a tool to increase human understanding of a complex system. The scientific advances made through computational modeling result in refinements of the biological models used for research. This interaction between a biological model and scientific advance is in sharp contrast to current publication media which fix in time both scientific knowledge as well as the tools used to acquire it.