Real-time experimental control using network-based parallel processing

Kim, Byoung-Hoon; Kenchappa, Shobha Channabasappa; Sunkara, Adhira; Chang, Tsun-Hou; Thompson, Lowell; Doudlah, Raymond; Rosenberg, Ari

doi:10.7554/elife.40231

Cited by 14 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental control was performed using the open-source REC-GUI software (RRID: SCR_019008; Kim et al, 2019 ). Stimuli were rendered using Psychtoolbox 3 (MATLAB R2016b; NVIDIA GeForce GTX 970).…”

Section: Methodsmentioning

confidence: 99%

Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex

Chang

Doudlah

Kim

et al. 2020

eLife

Self Cite

View full text Add to dashboard Cite

Three-dimensional (3D) representations of the environment are often critical for selecting actions that achieve desired goals. The success of these goal-directed actions relies on 3D sensorimotor transformations that are experience-dependent. Here we investigated the relationships between the robustness of 3D visual representations, choice-related activity, and motor-related activity in parietal cortex. Macaque monkeys performed an eight-alternative 3D orientation discrimination task and a visually guided saccade task while we recorded from the caudal intraparietal area using laminar probes. We found that neurons with more robust 3D visual representations preferentially carried choice-related activity. Following the onset of choice-related activity, the robustness of the 3D representations further increased for those neurons. We additionally found that 3D orientation and saccade direction preferences aligned, particularly for neurons with choice-related activity, reflecting an experience-dependent sensorimotor association. These findings reveal previously unrecognized links between the fidelity of ecologically relevant object representations, choice-related activity, and motor-related activity.

show abstract

Section: Methodsmentioning

confidence: 99%

Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex

Chang

Doudlah

Kim

et al. 2020

eLife

Self Cite

View full text Add to dashboard Cite

show abstract

“…After recovery, each monkey was trained to sit in a custom primate chair with head restraint, and to fixate a visual target within 2° version and 1° vergence windows for a liquid reward. We verified the ability to perceive stereoscopically-defined depth by having the monkeys fixate simulated targets between −20 and 40 cm of depth from the screen [45]. Binocular eye position was monitored optically at a sampling rate of 1,000 Hz (EyeLink 1000 plus, SR Research).…”

Section: Methodsmentioning

confidence: 99%

“…Experimental control was performed using an open-source, network-based parallel processing framework [45]. Stimuli were created in MATLAB using Psychtoolbox 3 [46], and rendered using an NVIDIA GeForce GTX 970 graphics card on a Linux workstation (Ubuntu 16.04 LTS, Intel Xeon Processor, 24 GB RAM).…”

Section: Methodsmentioning

confidence: 99%

Optimized but not maximized cue integration for 3D visual perception

Chang

Kim

Thompson

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

36Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-37 posed problem. Despite this, our 3D perception of the world based on 2D retinal images is 38 seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D 39 perception in humans, but it is unclear if this mechanism is conserved in non-human primates, 40 and how the underlying neural architecture constrains 3D perception. Here we assess 3D 41 perception in macaque monkeys using a surface orientation discrimination task. We find that 42 perception is generally accurate, but precision depends on the spatial pose of the surface and 43 available cues. The results indicate that robust perception is achieved by dynamically reweighting 44 the integration of stereoscopic and perspective cues according to their pose-dependent 45 reliabilities. They further suggest that 3D perception is influenced by a prior for the 3D orientation 46 statistics of natural scenes. We compare the data to simulations based on the responses of 3D 47 orientation selective neurons. The results are explained by a model in which two independent 48 neuronal populations representing stereoscopic and perspective cues (with perspective signals 49 from the two eyes combined using nonlinear canonical computations) are optimally integrated 50 through linear summation. Perception of combined-cue stimuli is optimal given this architecture. 51However, an alternative architecture in which stereoscopic cues and perspective cues detected 52 by each eye are represented by three independent populations yields two times greater precision 53 than observed. This implies that, due to canonical computations, cue integration for 3D perception 54 is optimized but not maximized. 55 56 Author summary 57Our eyes only sense two-dimensional projections of the world (like a movie on a screen), yet we 58 perceive the world in three dimensions. To create reliable 3D percepts, the human visual system 59 integrates distinct visual signals according to their reliabilities, which depend on conditions such 60 as how far away an object is located and how it is oriented. Here we find that non-human primates 61 similarly integrate different 3D visual signals, and that their perception is influenced by the 3D 62 orientation statistics of natural scenes. Cue integration is thus a conserved mechanism for 63 creating robust 3D percepts by the primate brain. Using simulations of neural population activity, 64 based on neuronal recordings from the same animals, we show that some computations which 65 occur widely in the brain facilitate 3D perception, while others hinder perception. This work 66addresses key questions about how neural systems solve the difficult problem of generating 3D 67 percepts, identifies a plausible neural architecture for implementing robust 3D vision, and reveals 68 how neural computation can simultaneously optimize and curb perception. 69 were linearly summed [10]). However, an alternative architecture in which stereoscopic as we...

show abstract

“…Bpod is a useful point of comparison as it is probably the most similar project to pyControl in terms of functionality and implementation, Bonsai because it represents a very different but powerful formalism for controlling experiments that is often complementary. Space constraints preclude detailed comparison with other projects, but see (Devarakonda et al, 2016;O'Leary et al, 2018;Kim et al, 2019;Gurley, 2019;Bhagat et al, 2020;Buscher et al, 2020). Despite these commonalities, there are significant differences which it is useful for prospective users to understand.…”

Section: Discussionmentioning

confidence: 99%

pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments

Akam

Lustig

Rowland

et al. 2021

Preprint

View full text Add to dashboard Cite

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends we developed pyControl, a system of open source hardware and software for controlling behavioural experiments comprising; a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features.

show abstract

Real-time experimental control using network-based parallel processing

Cited by 14 publications

References 18 publications

Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex

Functional links between sensory representations, choice activity, and sensorimotor associations in parietal cortex

Optimized but not maximized cue integration for 3D visual perception

pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments

Contact Info

Product

Resources

About