Response properties of cat AMLS neurons to optic flow stimuli

2002 ◽  
Vol 45 (3) ◽  
pp. 268 ◽  
Author(s):  
Baowang LI
Keyword(s):  
Optic Flow ◽  
Response Properties

Simulated Optic Flow and Extrastriate Cortex. II. Responses to Bar Versus Large-Field Stimuli

1997 ◽  
Vol 77 (2) ◽  
pp. 562-570 ◽  
Author(s):  
Kathleen Mulligan ◽  
Jong-Nam Kim ◽  
Helen Sherk
Keyword(s):  
Optic Flow ◽  
Preceding Paper ◽  
Forward Direction ◽  
Direction Selectivity ◽  
Image Motion ◽  
Extrastriate Cortex ◽  
Large Field ◽  
Response Properties ◽  
Tuning Curves ◽  
Direction Tuning

Mulligan, Kathleen, Jong-Nam Kim, and Helen Sherk. Simulated optic flow and extrastriate cortex. II. Responses to bar versus large-field stimuli. J. Neurophysiol. 77: 562–570, 1997. In the preceding paper we described the responses of cells in the cat's lateral suprasylvian visual area (LS) to large-field optic flow and texture movies. To assess response properties such as direction selectivity, cells were also tested with moving bar stimuli. We expected that there would be good agreement between response properties elicited with optic flow movies and those revealed with bar stimuli. We first asked how well bar response properties predicted responsiveness to optic flow movies. There was no correlation between responsiveness to movies and the degree of end-stopping, length summation, or preference for bars that accelerated and expanded. We then considered only the 322 cells that responded to both bars and optic flow or texture movies and asked how well the strength of their response to movies could be predicted from the direction-tuning curves generated with bar stimuli. One-third of these cells responded much more strongly to movies than could be predicted from their direction-tuning curves. Generally, such cells were rather well tuned for the direction of bar motion and preferred a direction substantially different from what they saw in optic flow movies. Optic flow movies shown in the forward direction were the most effective variety of movie for two-thirds of these cells. To see whether this outcome stemmed from differential direction tuning for bars and large multielement displays, in a second series of experiments we compared direction tuning for bars and large-field texture movies. Many cells showed substantially different direction tuning for the two kinds of stimulus: almost [Formula: see text] of 409 cells had tuning curves that overlapped each other by <50%. But only a small number of cells (<10%) responded much better to texture movies than to bars in the predominant direction of image motion in optic flow movies. This result, like that reported in the preceding paper, suggests that cells in LS respond differently to optic flow than to texture displays lacking optic flow motion cues.

Response Properties of Optic Flow Neurons in the Accessory Optic System of Hummingbirds vs. Zebra Finches and Pigeons

2021 ◽  
Author(s):  
Andrea H Gaede ◽  
Vikram B Baliga ◽  
Graham Smyth ◽  
Cristian Gutiérrez-Ibáñez ◽  
Douglas Leonard Altshuler ◽  
...  
Keyword(s):  
Optic Flow ◽  
Receptive Fields ◽  
Bird Species ◽  
Visual Response ◽  
Zebra Finches ◽  
Optic System ◽  
Accessory Optic System ◽  
Stimulus Velocity ◽  
Response Properties ◽  
Optokinetic Responses

Optokinetic responses function to maintain retinal image stabilization by minimizing optic flow that occurs during self-motion. The hovering ability of hummingbirds is an extreme example of this behaviour. Optokinetic responses are mediated by direction-selective neurons with large receptive fields in the accessory optic system (AOS) and pretectum. Recent studies in hummingbirds showed that, compared to other bird species, (i) the pretectal nucleus lentiformis mesencephali (LM) is hypertrophied, (ii) LM has a unique distribution of direction preferences, and (iii) LM neurons are more tightly tuned to stimulus velocity. In this study, we sought to determine if there are concomitant changes in the nucleus of the basal optic root (nBOR) of the AOS. We recorded the visual response properties of nBOR neurons to largefield drifting random dot patterns and sine wave gratings in Anna's hummingbirds and zebra finches and compared these with archival data from pigeons. We found no differences with respect to the distribution of direction preferences: Neurons responsive to upwards, downwards and nasal-to-temporal motion were equally represented in all three species, and neurons responsive to temporal-to-nasal motion were rare or absent (<5%). Compared to zebra finches and pigeons, however, hummingbird nBOR neurons were more tightly tuned to stimulus velocity of random dot stimuli. Moreover, in response to drifting gratings, hummingbird nBOR neurons are more tightly tuned in the spatio-temporal domain. These results, in combination with specialization in LM, supports a hypothesis that hummingbirds have evolved to be "optic flow specialist" to cope with the optomotor demands of sustained hovering flight.

Response properties of cat striate neurons to simulated optic flow stimuli

2003 ◽  
Vol 32 (3) ◽  
pp. 197-210
Author(s):  
Bao-Wang Li ◽  
Bing Li ◽  
Yao Chen ◽  
Yun-Cheng Diao
Keyword(s):  
Optic Flow ◽  
Response Properties ◽  
Striate Neurons

scholarly journals The impulse response of optic flow-sensitive descending neurons to roll m-sequences

2021 ◽  
Vol 224 (23) ◽  
Author(s):  
Richard Leibbrandt ◽  
Sarah Nicholas ◽  
Karin Nordström
Keyword(s):  
Impulse Response ◽  
Optic Flow ◽  
Temporal Response ◽  
Impulse Responses ◽  
Response Properties ◽  
Time To Peak ◽  
Descending Neurons ◽  
Spatio Temporal ◽  
Head Roll ◽  
M Sequences

ABSTRACT When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow-sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensorimotor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. Therefore, here we used m-sequences to quantify the impulse responses of optic flow-sensitive descending neurons in male Eristalis tenax hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5–18.0 ms. This is similar to the impulse response time to peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.

Response properties of PMLS and PLLS neurons to simulated optic flow patterns

2000 ◽  
Vol 12 (5) ◽  
pp. 1534-1544 ◽  
Author(s):  
Bing Li ◽  
Bao-Wang Li ◽  
Yao Chen ◽  
Lian-Hong Wang ◽  
Yun-Cheng Diao
Keyword(s):  
Optic Flow ◽  
Flow Patterns ◽  
Response Properties

scholarly journals Spatiotemporal Response Properties of Optic-Flow Processing Neurons

Neuron ◽  
2010 ◽  
Vol 67 (4) ◽  
pp. 629-642 ◽  
Author(s):  
Franz Weber ◽  
Christian K. Machens ◽  
Alexander Borst
Keyword(s):  
Optic Flow ◽  
Response Properties ◽  
Spatiotemporal Response

A First Principles Approach for Partitioning Linear Response Properties into Additive and Cooperative Contributions

2018 ◽  
Author(s):  
Daniel Lambrecht ◽  
Eric Berquist
Keyword(s):  
First Principles ◽  
Linear Response ◽  
Building Blocks ◽  
Field Method ◽  
Response Property ◽  
Response Properties ◽  
Consistent Field ◽  
Molecular Building Blocks ◽  
Self Consistent ◽  
Self Consistent Field

We present a first principles approach for decomposing molecular linear response properties into orthogonal (additive) plus non-orthogonal/cooperative contributions. This approach enables one to 1) identify the contributions of molecular building blocks like functional groups or monomer units to a given response property and 2) quantify cooperativity between these contributions. In analogy to the self consistent field method for molecular interactions, SCF(MI), we term our approach LR(MI). The theory, implementation and pilot data are described in detail in the manuscript and supporting information.

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