DataJoint: managing big scientific data using MATLAB or Python

Yatsenko, Dimitri; Reimer, Jacob; Ecker, Alexander; Walker, Edgar Y.; Sinz, Fabian H.; Berens, Philipp; Hoenselaar, Andreas; Cotton, R.; Siapas, Athanassios S.; Tolias, Andreas S.

doi:10.1101/031658

Cited by 91 publications

(89 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, F norm =( F − F min )/( F max − F min ) is used in all other analyses in order not to over- or under-weight highly active calcium traces. The data processing chain for this and subsequent analysis relied on the DataJoint library for MATLAB ( http://datajoint.github.com/datajoint-matlab/)59.…”

Section: Methodsmentioning

confidence: 99%

Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

et al. 2016

Self Cite

View full text Add to dashboard Cite

Rapid variations in cortical state during wakefulness have a strong influence on neural and behavioural responses and are tightly coupled to changes in pupil size across species. However, the physiological processes linking cortical state and pupil variations are largely unknown. Here we demonstrate that these rapid variations, during both quiet waking and locomotion, are highly correlated with fluctuations in the activity of corticopetal noradrenergic and cholinergic projections. Rapid dilations of the pupil are tightly associated with phasic activity in noradrenergic axons, whereas longer-lasting dilations of the pupil, such as during locomotion, are accompanied by sustained activity in cholinergic axons. Thus, the pupil can be used to sensitively track the activity in multiple neuromodulatory transmitter systems as they control the state of the waking brain.

show abstract

Section: Methodsmentioning

confidence: 99%

Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data were organised in a custom written schema using the DataJoint for Matlab framework (github.com/datajoint/datajoint-matlab) 54 . All data as well as basic visualisation routines are available at www.retinal-functomics.org.…”

Section: Methodsmentioning

confidence: 99%

Inhibition decorrelates visual feature representations in the inner retina

Franke

Berens

Schubert

et al. 2017

Nature

Self Cite

264

430

View full text Add to dashboard Cite

SUMMARY The retina extracts visual features for transmission to the brain. Different types of bipolar cell split the photoreceptor input into parallel channels and provide the excitatory drive for downstream visual circuits. Anatomically and genetically, mouse bipolar cell types have been described at great detail, but a similarly deep understanding of their functional diversity is lacking. By imaging light-driven glutamate release from more than 13,000 bipolar cell axon terminals in the intact retina, we here show that bipolar cell functional diversity is generated by the interplay of dendritic excitatory inputs and axonal inhibitory inputs. The resultant centre and surround components of bipolar cell receptive fields interact to decorrelate bipolar cell output in the spatial and temporal domain. Our findings highlight the importance of inhibitory circuits in generating functionally diverse excitatory pathways and suggest that decorrelation of parallel visual pathways begins already at the second synapse of the mouse visual system.

show abstract

“…This data acquisition system also received timestamps from the video recording system and the stimulus delivery system to allow these data streams to be synchronized for analysis. All data analysis was performed in MATLAB using the DataJoint data processing chain toolbox (Yatsenko et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Spatial tuning and brain state account for dorsal hippocampal CA1 activity in a non-spatial learning task

Shan

Lubenov

Παπαδοπούλου

et al. 2016

eLife

View full text Add to dashboard Cite

The hippocampus is a brain area crucial for episodic memory in humans. In contrast, studies in rodents have highlighted its role in spatial learning, supported by the discovery of place cells. Efforts to reconcile these views have found neurons in the rodent hippocampus that respond to non-spatial events but have not unequivocally dissociated the spatial and non-spatial influences on these cells. To disentangle these influences, we trained freely moving rats in trace eyeblink conditioning, a hippocampally dependent task in which the animal learns to blink in response to a tone. We show that dorsal CA1 pyramidal neurons are all place cells, and do not respond to the tone when the animal is moving. When the animal is inactive, the apparent tone-evoked responses reflect an arousal-mediated resumption of place-specific firing. These results suggest that one of the main output stages of the hippocampus transmits only spatial information, even in this non-spatial task.DOI: http://dx.doi.org/10.7554/eLife.14321.001

show abstract

DataJoint: managing big scientific data using MATLAB or Python

Cited by 91 publications

References 17 publications

Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

Inhibition decorrelates visual feature representations in the inner retina

Spatial tuning and brain state account for dorsal hippocampal CA1 activity in a non-spatial learning task

Contact Info

Product

Resources

About