Reverse correlation analysis of the stretch response of primary muscle spindle afferent fibers

1993 ◽  
Vol 69 (5-6) ◽  
pp. 447-456 ◽  
Author(s):  
J. Kröller
Keyword(s):  
Correlation Analysis ◽  
Muscle Spindle ◽  
Reverse Correlation ◽  
Afferent Fibers ◽  
Stretch Response ◽  
Reverse Correlation Analysis

Receptive-field maps of correlated discharge between pairs of neurons in the cat's visual cortex

1994 ◽  
Vol 71 (1) ◽  
pp. 330-346 ◽  
Author(s):  
G. M. Ghose ◽  
I. Ohzawa ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Correlation Analysis ◽  
Random Sequence ◽  
Receptive Fields ◽  
Simple Cell ◽  
Reverse Correlation ◽  
Sinusoidal Gratings ◽  
Reverse Correlation Analysis ◽  
Correlation Procedure

1. To investigate the functional significance of temporally correlated discharge between nearby cells in the visual cortex, we obtained receptive-field maps of correlated discharge for 68 cell pairs in kittens and cats. Discharge from cell pairs was measured by a single extracellular electrode. A reverse correlation procedure was used to relate neural discharge to particular stimuli within a random sequence of briefly flashed bright and dark bars. Bicellular receptive fields (BRFs) were mapped by applying reverse correlation to approximately synchronous discharge from two cells. Unicellular receptive fields (URFs) were simultaneously mapped by separately applying reverse correlation to the discharge of each cell. 2. The receptive fields of the two neurons within each pair were initially studied by varying the orientation and spatial frequency of drifting sinusoidal gratings. After these tests a random sequence of appropriately oriented bars was used to evoke discharge suitable for reverse correlation analysis. For most cell pairs, the temporal pattern or strength of correlated discharge produced by such stimulation is different from that observed with stimulation by sinusoidal gratings. This indicates that visually evoked correlated discharge between nearby cells is stimulus dependent. 3. BRFs were classified according to their pattern of spatial sensitivity into three groups that roughly correspond to the single-cell receptive-field types of the lateral geniculate nucleus (LGN; center-surround) and visual cortex (simple and complex). These classifications were compared with the receptive-field types of the single cells within each pair. LGN-type and simple-type BRFs were only seen for pairs in which at least one of the cells was simple. Conversely, complex-type BRFs were only seen for pairs in which at least one of the cells was complex. 4. Because the reverse correlation procedure can be used to characterize the spatiotemporal receptive-field structure of simple cells, we were able to compare both the spatial and temporal properties associated with the URFs and BRFs of simple cell pairs. The spatiotemporal structure of the BRF of a simple-cell pair can largely be predicted on the basis of the two URFs. Although this prediction suggests the possibility that BRFs are stimulus artifacts, a shuffle procedure, in which multiple repetitions of random sequences were presented, verifies the neural origin of BRFs. BRFs emerge from specific neural pathways and are not simply a consequence of unicellular response preferences. 5. Five measures were derived from the reverse correlation analysis of simple-cell receptive fields: width, duration, optimal spatial and temporal frequency, and optimal velocity.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Reverse correlation analysis of a reaction time task for stochastic stimuli

2020 ◽  
Vol 20 (11) ◽  
pp. 934
Author(s):  
Hironori Maruyama ◽  
Hiromi Sato ◽  
Ryuto Yashiro ◽  
Isamu Motoyoshi
Keyword(s):  
Reaction Time ◽  
Correlation Analysis ◽  
Reaction Time Task ◽  
Reverse Correlation ◽  
Time Task ◽  
Reverse Correlation Analysis

scholarly journals Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics

2015 ◽  
Author(s):  
Luis Hernandez-Nunez ◽  
Jonas Belina ◽  
Mason Klein ◽  
Guangwei Si ◽  
Lindsey Claus ◽  
...  
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Reverse Correlation ◽  
Activation Patterns ◽  
Sensorimotor Transformations ◽  
Strategic Value ◽  
Large Numbers ◽  
Drosophila Larva ◽  
Nonlinear Components ◽  
Reverse Correlation Analysis

Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. Here, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of Channelrhodopsin, in specific chemosensory neurons, and expose large numbers of freely moving animals to random optogenetic activation patterns. We quantify their behavioral responses and use reverse correlation analysis to uncover the linear and static nonlinear components of navigation dynamics as functions of optogenetic activation patterns of specific sensory neurons. We find that linear-nonlinear (LN) models accurately predict navigational decision-making for different optogenetic activation waveforms. We use our method to establish the valence and dynamics of navigation driven by optogenetic activation of different combinations of bitter sensing gustatory neurons. Our method captures the dynamics of optogenetically-induced behavior in compact, quantitative transformations that can be used to characterize circuits for sensorimotor processing and their contribution to navigational decision-making.

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