A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture

Abstract

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.

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

“…Large-scale simulations running efficiently on supercomputers are now possible, and software development systems have been designed and tested (Bhalla et al, 1992 ; Hines and Carnevale, 1997 ; Bower and Beeman, 2007 ; Gleeson et al, 2007 , 2010 ; Davison et al, 2009 ; Hines et al, 2009 ; Cornelis et al, 2012a ). While this may be sufficient for elaborating complex codes in an iterative reconstruction/validation process, simulating network adaptation during learning would require several repetitions over prolonged time periods.…”

“…Eventually, neuromorphic hardware platforms will have to be considered for the cerebellum as well as for the cerebral cortex (Pfeil et al, 2013 ; Galluppi et al, 2015 ; Lagorce et al, 2015 ). It can be envisaged that realistic modeling of the cerebellum, with the reconstruction of neurons and large-scale networks based on extended data-sets and running on supercomputing infrastructures, will require a world-wide collaborative effort as it has been proposed for other brain structures like the neocortex and hippocampus (Markram, 2006 ; Cornelis et al, 2012a ; Crook et al, 2012 ; Kandel et al, 2013 ; Bower, 2015 ; Ramaswamy et al, 2015 ).…”

“…In a sense, realistic modeling is emerging as one of the most powerful tools in the hands of neuroscientists (Davison, 2012 ; Gerstner et al, 2012 ; Markram, 2013 ). The cerebellum has actually been the work bench for the development of ideas and tools fuelling realistic modeling over almost 40 years (for review see Bhalla et al, 1992 ; Baldi et al, 1998 ; Cornelis et al, 2012a ; D’Angelo et al, 2013a ; Bower, 2015 ; Sudhakar et al, 2015 ).…”

“…In all these cases, the cerebellum has provided a work bench that has remarkably contributed to write the history of realistic modeling. Examples are the development of integrated simulation platforms (Bhalla et al, 1992 ; Bower and Beeman, 2007 ), the definition of model optimization and evaluation strategies (Baldi et al, 1998 ; Vanier and Bower, 1999 ; Cornelis et al, 2012a , b ; Bower, 2015 ), the generation of complex neuron models as exemplified by the Purkinje cells (De Schutter and Bower, 1994a , b ; Bower, 2015 ; Masoli et al, 2015 ) and the GrCs (D’Angelo et al, 2001 ; Nieus et al, 2006 ; Diwakar et al, 2009 ) and the generation of complex microcircuit models (Maex and De Schutter, 1998 ; Medina and Mauk, 2000 ; Solinas et al, 2010 ). Now, the cerebellar neurons, synapses and network pose new challenges for realistic modeling depending on recent discoveries on neuron and circuit biology and on the possibility of including large-scale realistic circuit models into closed loop robotic simulations.…”

A modeling study of cortical waves in primary auditory cortex

“…The simulations described here were implemented with the GENESIS 2.3 and 3 (G-3) simulators [1]. The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves.…”