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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.


Related in: MedlinePlus

Example use cases of neuroscience experimental setups using Bonsai. (A) High-speed tracking of zebrafish behavior. Insets depict the image processing steps for segmenting the shape of a fish from the background and extracting its spatial location and orientation. Right: example trajectories extracted from an individual fish. (B) Mouse tracking and bulk fluorescence measurement of neuronal calcium activity. Top insets: schematic of the fiber optic imaging setup for freely moving rodents with example fluorescence data frame and extracted fluorescence signal traces. Bottom insets: image processing steps for behavior tracking of a mouse as the largest dark object in the video. (C) Tracking human behavior during a stochastic sound discrimination task. Left insets: arm movements on the joystick on each trial tracked by brightness segmentation of a bright LED. Right insets: extraction of pupil dilation by computing the length of the major axis of the largest dark object. (D) 3D tracking of rodent head pose. Left inset: example video frame of a mouse carrying fiducial markers. A cube was rendered and superimposed on the image to demonstrate correct registration. Colored traces show representative single trial trajectories of an individual marker, aligned on center poke onset. Red and blue refer to left and right choice trials, respectively. Right inset: Three-dimensional plot of the same trajectories using isometric projection. (E) Real-time stimulation conditioned to a region in space. Top insets: example raw movie frame and stimulation state. Red and blue indicate no stimulation and stimulation regimes, respectively. Bottom insets: example video frames where the mouse is either outside or inside the region of interest. (F) Acute recordings from dense silicon probes. Left insets: example traces from raw amplified voltage signals and high-pass filtered spike triggered waveforms. Right inset: visualization of spike waveforms triggered on a single channel superimposed on the actual probe geometry. (G) Recording Drosophila feeding behavior. Left inset: example trace of a single-channel capacitive signal from the flyPAD. Right inset: simultaneously recorded video of the fly feeding behavior. (H) 2AFC task using video triggered reward. Left inset: schematic of the reactive state machine used for controlling the task. Each state is represented by a nested dataflow. Branches represent possible transitions. Right inset: example thresholded activity from a single region of interest activated by the mouse.
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Figure 4: Example use cases of neuroscience experimental setups using Bonsai. (A) High-speed tracking of zebrafish behavior. Insets depict the image processing steps for segmenting the shape of a fish from the background and extracting its spatial location and orientation. Right: example trajectories extracted from an individual fish. (B) Mouse tracking and bulk fluorescence measurement of neuronal calcium activity. Top insets: schematic of the fiber optic imaging setup for freely moving rodents with example fluorescence data frame and extracted fluorescence signal traces. Bottom insets: image processing steps for behavior tracking of a mouse as the largest dark object in the video. (C) Tracking human behavior during a stochastic sound discrimination task. Left insets: arm movements on the joystick on each trial tracked by brightness segmentation of a bright LED. Right insets: extraction of pupil dilation by computing the length of the major axis of the largest dark object. (D) 3D tracking of rodent head pose. Left inset: example video frame of a mouse carrying fiducial markers. A cube was rendered and superimposed on the image to demonstrate correct registration. Colored traces show representative single trial trajectories of an individual marker, aligned on center poke onset. Red and blue refer to left and right choice trials, respectively. Right inset: Three-dimensional plot of the same trajectories using isometric projection. (E) Real-time stimulation conditioned to a region in space. Top insets: example raw movie frame and stimulation state. Red and blue indicate no stimulation and stimulation regimes, respectively. Bottom insets: example video frames where the mouse is either outside or inside the region of interest. (F) Acute recordings from dense silicon probes. Left insets: example traces from raw amplified voltage signals and high-pass filtered spike triggered waveforms. Right inset: visualization of spike waveforms triggered on a single channel superimposed on the actual probe geometry. (G) Recording Drosophila feeding behavior. Left inset: example trace of a single-channel capacitive signal from the flyPAD. Right inset: simultaneously recorded video of the fly feeding behavior. (H) 2AFC task using video triggered reward. Left inset: schematic of the reactive state machine used for controlling the task. Each state is represented by a nested dataflow. Branches represent possible transitions. Right inset: example thresholded activity from a single region of interest activated by the mouse.

Mentions: The validation of Bonsai was performed by using the framework to implement a number of applications in the domain of neuroscience (Figure 4). The breadth of technologies at use in this field demands that modern experiments be able to handle many heterogeneous sources of data. Experimenters need to routinely record video and sensor data monitoring the behavior of an animal simultaneously with electrophysiology, optical reporters of neural activity or other physiological measures. Online manipulation and visualization of data is a fundamental part of the experiment protocol for many of the reported techniques. In the following, we highlight some of these practical applications of Bonsai in more detail in order to illustrate both “best practices” and implementation strategies.


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)

Example use cases of neuroscience experimental setups using Bonsai. (A) High-speed tracking of zebrafish behavior. Insets depict the image processing steps for segmenting the shape of a fish from the background and extracting its spatial location and orientation. Right: example trajectories extracted from an individual fish. (B) Mouse tracking and bulk fluorescence measurement of neuronal calcium activity. Top insets: schematic of the fiber optic imaging setup for freely moving rodents with example fluorescence data frame and extracted fluorescence signal traces. Bottom insets: image processing steps for behavior tracking of a mouse as the largest dark object in the video. (C) Tracking human behavior during a stochastic sound discrimination task. Left insets: arm movements on the joystick on each trial tracked by brightness segmentation of a bright LED. Right insets: extraction of pupil dilation by computing the length of the major axis of the largest dark object. (D) 3D tracking of rodent head pose. Left inset: example video frame of a mouse carrying fiducial markers. A cube was rendered and superimposed on the image to demonstrate correct registration. Colored traces show representative single trial trajectories of an individual marker, aligned on center poke onset. Red and blue refer to left and right choice trials, respectively. Right inset: Three-dimensional plot of the same trajectories using isometric projection. (E) Real-time stimulation conditioned to a region in space. Top insets: example raw movie frame and stimulation state. Red and blue indicate no stimulation and stimulation regimes, respectively. Bottom insets: example video frames where the mouse is either outside or inside the region of interest. (F) Acute recordings from dense silicon probes. Left insets: example traces from raw amplified voltage signals and high-pass filtered spike triggered waveforms. Right inset: visualization of spike waveforms triggered on a single channel superimposed on the actual probe geometry. (G) Recording Drosophila feeding behavior. Left inset: example trace of a single-channel capacitive signal from the flyPAD. Right inset: simultaneously recorded video of the fly feeding behavior. (H) 2AFC task using video triggered reward. Left inset: schematic of the reactive state machine used for controlling the task. Each state is represented by a nested dataflow. Branches represent possible transitions. Right inset: example thresholded activity from a single region of interest activated by the mouse.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Example use cases of neuroscience experimental setups using Bonsai. (A) High-speed tracking of zebrafish behavior. Insets depict the image processing steps for segmenting the shape of a fish from the background and extracting its spatial location and orientation. Right: example trajectories extracted from an individual fish. (B) Mouse tracking and bulk fluorescence measurement of neuronal calcium activity. Top insets: schematic of the fiber optic imaging setup for freely moving rodents with example fluorescence data frame and extracted fluorescence signal traces. Bottom insets: image processing steps for behavior tracking of a mouse as the largest dark object in the video. (C) Tracking human behavior during a stochastic sound discrimination task. Left insets: arm movements on the joystick on each trial tracked by brightness segmentation of a bright LED. Right insets: extraction of pupil dilation by computing the length of the major axis of the largest dark object. (D) 3D tracking of rodent head pose. Left inset: example video frame of a mouse carrying fiducial markers. A cube was rendered and superimposed on the image to demonstrate correct registration. Colored traces show representative single trial trajectories of an individual marker, aligned on center poke onset. Red and blue refer to left and right choice trials, respectively. Right inset: Three-dimensional plot of the same trajectories using isometric projection. (E) Real-time stimulation conditioned to a region in space. Top insets: example raw movie frame and stimulation state. Red and blue indicate no stimulation and stimulation regimes, respectively. Bottom insets: example video frames where the mouse is either outside or inside the region of interest. (F) Acute recordings from dense silicon probes. Left insets: example traces from raw amplified voltage signals and high-pass filtered spike triggered waveforms. Right inset: visualization of spike waveforms triggered on a single channel superimposed on the actual probe geometry. (G) Recording Drosophila feeding behavior. Left inset: example trace of a single-channel capacitive signal from the flyPAD. Right inset: simultaneously recorded video of the fly feeding behavior. (H) 2AFC task using video triggered reward. Left inset: schematic of the reactive state machine used for controlling the task. Each state is represented by a nested dataflow. Branches represent possible transitions. Right inset: example thresholded activity from a single region of interest activated by the mouse.
Mentions: The validation of Bonsai was performed by using the framework to implement a number of applications in the domain of neuroscience (Figure 4). The breadth of technologies at use in this field demands that modern experiments be able to handle many heterogeneous sources of data. Experimenters need to routinely record video and sensor data monitoring the behavior of an animal simultaneously with electrophysiology, optical reporters of neural activity or other physiological measures. Online manipulation and visualization of data is a fundamental part of the experiment protocol for many of the reported techniques. In the following, we highlight some of these practical applications of Bonsai in more detail in order to illustrate both “best practices” and implementation strategies.

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.


Related in: MedlinePlus