Temporal constrained objects for modelling neuronal dynamics

Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also...

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Bibliographic Details
Published in:PeerJ. Computer science Vol. 4; p. e159
Main Authors: Nair, Manjusha, Manchan Kannimoola, Jinesh, Jayaraman, Bharat, Nair, Bipin, Diwakar, Shyam
Format: Journal Article
Language:English
Published: United States PeerJ, Inc 23.07.2018
PeerJ Inc
Subjects:
Brain
Complex systems
Computation
Computational Biology
Computer science
Computer simulation
Constraint modelling
Constraint programming
Declarative modelling
Microprocessors
Neural networks
Neuron models
Neurons
Neurosciences
Object-oriented languages
Ordinary differential equations
Parallel computers
Partial differential equations
Programming Languages
Scientific Computing and Simulation
Synapses
Temporal constrained objects
United States > US
India
Object-oriented languages
Declarative modelling
Neuron models
Constraint programming
Temporal constrained objects
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary:Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. In this paper, we propose a novel programming paradigm, called which facilitates a principled approach to modelling complex dynamical systems. are an extension of with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of . Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.159