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Bonsai: an event-based framework for processing and controlling data streams.

Lopes G, Bonacchi N, Frazão J, Neto JP, Atallah BV, Soares S, Moreira L, Matias S, Itskov PM, Correia PA, Medina RE, Calcaterra L, Dreosti E, Paton JJ, Kampff AR - Front Neuroinform (2015)

Bottom Line: However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data.Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams.We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience.

View Article: PubMed Central - PubMed

Affiliation: Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown Lisbon, Portugal.

ABSTRACT
The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.

No MeSH data available.


Describing the behavior of dynamic environments using either state-machines or dataflows. (A) A state-machine model of the 1-site foraging task. Zero indicates non-availability of reward at the site. One indicates reward is now available at the site. Labels on edges indicate event transitions. (B) A non-exhaustive state-machine model for a foraging task with two sites. The active state is now a combination of the state of the two sites (indicated by a two character label) and all possible state combinations are tiled across the model. Event labels are omitted for clarity. Notation is otherwise kept. (C) A dataflow model of the 1-site foraging task. Events in the state-machine model are now modeled as data sources. The coincidence detector node propagates a signal only when the sample event closely follows reward availability. (D) A dataflow model for a foraging task with two sites. The number subscripts denote foraging site index.
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Figure 5: Describing the behavior of dynamic environments using either state-machines or dataflows. (A) A state-machine model of the 1-site foraging task. Zero indicates non-availability of reward at the site. One indicates reward is now available at the site. Labels on edges indicate event transitions. (B) A non-exhaustive state-machine model for a foraging task with two sites. The active state is now a combination of the state of the two sites (indicated by a two character label) and all possible state combinations are tiled across the model. Event labels are omitted for clarity. Notation is otherwise kept. (C) A dataflow model of the 1-site foraging task. Events in the state-machine model are now modeled as data sources. The coincidence detector node propagates a signal only when the sample event closely follows reward availability. (D) A dataflow model for a foraging task with two sites. The number subscripts denote foraging site index.

Mentions: One of the areas where we see the application of Bonsai becoming most significant is in the development of dynamic behavior assays (environments) using reactive control strategies. Brains evolved to generate and control behaviors that can deal with the complexity of the natural world. However, when neuroscientists try to investigate these behaviors in the lab, it is often difficult to design equivalent environmental complexity in a controlled manner. As an example, consider a simple foraging scenario in which a land animal must collect, in a timely manner, food items that become available at random intervals in many sites. If the item is not collected in time, it rots or gets eaten by competitors. In the case of a single foraging site, a FSM description intuitively represents the workings of the environment (Figure 5A). However, let us now consider a situation where the environment has two of these food sites operating independently, thus introducing the possibility of different events occurring simultaneously at each of the sites. If our environment is modeled as a finite-state machine, then we must represent every possible combination of states and transitions, as in Figure 5B. In the classical state machine formalism the machine can only be in one state at a time, which means we now need to model each state as the combination of the individual independent states at each reward location. Furthermore, because transitions between these states are asynchronous and independent, we thus have edges between nearly every pair of nodes, as each reward site can change its state at any point in time relative to the other.


Bonsai: an event-based framework for processing and controlling data streams.

Lopes G, Bonacchi N, Frazão J, Neto JP, Atallah BV, Soares S, Moreira L, Matias S, Itskov PM, Correia PA, Medina RE, Calcaterra L, Dreosti E, Paton JJ, Kampff AR - Front Neuroinform (2015)

Describing the behavior of dynamic environments using either state-machines or dataflows. (A) A state-machine model of the 1-site foraging task. Zero indicates non-availability of reward at the site. One indicates reward is now available at the site. Labels on edges indicate event transitions. (B) A non-exhaustive state-machine model for a foraging task with two sites. The active state is now a combination of the state of the two sites (indicated by a two character label) and all possible state combinations are tiled across the model. Event labels are omitted for clarity. Notation is otherwise kept. (C) A dataflow model of the 1-site foraging task. Events in the state-machine model are now modeled as data sources. The coincidence detector node propagates a signal only when the sample event closely follows reward availability. (D) A dataflow model for a foraging task with two sites. The number subscripts denote foraging site index.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389726&req=5

Figure 5: Describing the behavior of dynamic environments using either state-machines or dataflows. (A) A state-machine model of the 1-site foraging task. Zero indicates non-availability of reward at the site. One indicates reward is now available at the site. Labels on edges indicate event transitions. (B) A non-exhaustive state-machine model for a foraging task with two sites. The active state is now a combination of the state of the two sites (indicated by a two character label) and all possible state combinations are tiled across the model. Event labels are omitted for clarity. Notation is otherwise kept. (C) A dataflow model of the 1-site foraging task. Events in the state-machine model are now modeled as data sources. The coincidence detector node propagates a signal only when the sample event closely follows reward availability. (D) A dataflow model for a foraging task with two sites. The number subscripts denote foraging site index.
Mentions: One of the areas where we see the application of Bonsai becoming most significant is in the development of dynamic behavior assays (environments) using reactive control strategies. Brains evolved to generate and control behaviors that can deal with the complexity of the natural world. However, when neuroscientists try to investigate these behaviors in the lab, it is often difficult to design equivalent environmental complexity in a controlled manner. As an example, consider a simple foraging scenario in which a land animal must collect, in a timely manner, food items that become available at random intervals in many sites. If the item is not collected in time, it rots or gets eaten by competitors. In the case of a single foraging site, a FSM description intuitively represents the workings of the environment (Figure 5A). However, let us now consider a situation where the environment has two of these food sites operating independently, thus introducing the possibility of different events occurring simultaneously at each of the sites. If our environment is modeled as a finite-state machine, then we must represent every possible combination of states and transitions, as in Figure 5B. In the classical state machine formalism the machine can only be in one state at a time, which means we now need to model each state as the combination of the individual independent states at each reward location. Furthermore, because transitions between these states are asynchronous and independent, we thus have edges between nearly every pair of nodes, as each reward site can change its state at any point in time relative to the other.

Bottom Line: However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data.Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams.We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience.

View Article: PubMed Central - PubMed

Affiliation: Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown Lisbon, Portugal.

ABSTRACT
The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.

No MeSH data available.