Internal Structure of the Fly Elementary Motion Detector

2012-12-16T00:01:13Z

Sj:

{{Summary
|title=Internal Structure of the Fly Elementary Motion Detector
|authors=Hubert Eichner, Maximilian Joesch, Bettina Schnell, Dierk F. Reiff, Alexander Borst
|url=http://www.sciencedirect.com/science/article/pii/S089662731100376X
|tags=Reichardt detector Drosophila vision motion
|summary=Building on recent experiments showing that Drosophila motion detection splits the visual input into two parallel (L1 and L2) channels, this study further assumes that those two channels encode increases and decreases in brightness (ON and OFF). It then maps computations of directional selection to the implementation of an ON/OFF based Reichardt detector.

Under these assumptions, direction-selection is found that matches same-sign but not opposite-sign sequences, suggesting a two-quadrant (2Q) variation of the Reichardt detector. A model is proposed that both matches current observations and tries to account for past data that was interpreted as supporting four separate components to a (4Q) detector.

== Goals and Methods ==
There are biological and philosophical reasons to imagine that a 2Q detector might be preferably to a 4Q detector: it takes half the energy and biological material, and is known to account for some of the same basic edge detection. It might also explain how such detectors avoid signed multiplication, a process considered difficult to carry out biologically. The question is whether it accounts for the full range of motion detection afforded by a 4Q Reichardt detector, and whether it matches observable responses of real flies.

Apparent motion stimuli are used for these experiments, consisting of a sequence of light increments and decrements (from light to neutral to dark and back). The H1 neuron in Calliphora and VS1-5 in Drosophila are both observed. While an idealized analysis of the data would suggest that a 4Q detector is needed, that assumes that the detectors have no information about overall brightness. In contrast, real-world neurons have some memory and some awareness of overall brightness.

As a result, a real-world 2Q detector is modelled as an array of such detectors together covering a single period of a moving sine wave grating. 6 parameters are used to tune this model: the time constant of the high-pass and low-pass filters, the DC fraction, the clip point for OFF rectification, and the synaptic imbalance, as well as a weighting of the output of the two half-detectors to mimic excitator and inhibitory synaptic transmission.

Cells are assigned a primary and null direction (PD and ND), and a 'PD-ND inversion' effect is noted in which there is a differential response based on whether a PD or ND stimulus is encountered first. This inversion is considered characteristic of real cells, and looked for in a succesful model.

== Results and Analysis ==
The single-unit model developed is similar to a traditional Reichardt detector in many scenarios. In many it parallels the response of a Reichardt detector. It also responds to all four pairs of ON/OFF input combinations. There also remain significant differences between the modelled behavior and observed behavior. This is explained by the authors in part because firing rates 'can only decrease to 0 Hz'.

The large-scale 200-unit array of such detectors is tested against a number of inputs. It generally agrees with the expected output of a Reichardt detector. When shown a standing grating before motion begins, it shows oscillations, but when shown a moving sine grating with pseudorandom velocity profile it shows similar behavior to a Reichardt detector.

Overall the authors note a number of arguments for excluding the 2Q model, but have explanations for why each one can be neglected. They identify a few observations that argue for excluding the 4Q model, and highlight those as conclusive. Future research is needed into the presynaptic mechanism before signals get to the lobula plate, and the precise nature of the nonlinearity that takes place in the multiplication unit of the Reichardt detector (or similar model). Some specific ideas are suggested for future exploration.
|relevance=This added to the literature trying to assess the complexity of the motion-detection mechanism of the fly. It adds weight to the claim that a fly distinguishes two, rather than all four, pairwise interactions between sequences of responses to bright and dark edges. It suggests a 2Q model with 6 parameters that can fit some past data; however this remains a question of interpretation and does not settle the question conclusively.
|journal=Neuron
|pub_date=2011/06/23
|doi=10.1016/j.neuron.2011.03.028
|subject=Neuroscience
}}

Internal Structure of the Fly Elementary Motion Detector

2012-12-15T23:53:04Z

Sj:

{{Summary
|title=Internal Structure of the Fly Elementary Motion Detector
|authors=Hubert Eichner, Maximilian Joesch, Bettina Schnell, Dierk F. Reiff, Alexander Borst
|url=http://www.sciencedirect.com/science/article/pii/S089662731100376X
|tags=Reichardt detector Drosophila vision motion
|summary=Building on recent experiments showing that Drosophila motion detection splits the visual input into two parallel (L1 and L2) channels, this study further assumes that those two channels encode increases and decreases in brightness (ON and OFF). It then maps computations of directional selection to the implementation of an ON/OFF based Reichardt detector.

Under these assumptions, direction-selection is found that matches same-sign but not opposite-sign sequences, suggesting a two-quadrant (2Q) variation of the Reichardt detector. A model is proposed that both matches current observations and tries to account for past data that was interpreted as supporting four separate components to a (4Q) detector.

== Goals and Methods ==
There are biological and philosophical reasons to imagine that a 2Q detector might be preferably to a 4Q detector: it takes half the energy and biological material, and is known to account for some of the same basic edge detection. The question is whether it accounts for the full range of motion detection afforded by a 4Q Reichardt detector, and whether it matches observable responses of real flies.

Apparent motion stimuli are used for these experiments, consisting of a sequence of light increments and decrements (from light to neutral to dark and back). The H1 neuron in Calliphora and VS1-5 in Drosophila are both observed. While an idealized analysis of the data would suggest that a 4Q detector is needed, that assumes that the detectors have no information about overall brightness. In contrast, real-world neurons have some memory and some awareness of overall brightness.

As a result, a real-world 2Q detector is modelled as an array of such detectors together covering a single period of a moving sine wave grating. 6 parameters are used to tune this model: the time constant of the high-pass and low-pass filters, the DC fraction, the clip point for OFF rectification, and the synaptic imbalance, as well as a weighting of the output of the two half-detectors to mimic excitator and inhibitory synaptic transmission.

== Results and Analysis ==
The single-unit model developed is similar to a traditional Reichardt detector in many scenarios. In many it parallels the response of a Reichardt detector. It also responds to all four pairs of ON/OFF input combinations. There also remain significant differences between the modelled behavior and observed behavior. This is explained by the authors in part because firing rates 'can only decrease to 0 Hz'.

The large-scale 200-unit array of such detectors is tested against a number of inputs. It generally agrees with the expected output of a Reichardt detector. When shown a standing grating before motion begins, it shows oscillations, but when shown a moving sine grating with pseudorandom velocity profile it shows similar behavior to a Reichardt detector.

|relevance=This added to the literature trying to assess the complexity of the motion-detection mechanism of the fly. It adds weight to the claim that a fly distinguishes two, rather than all four, pairwise interactions between sequences of responses to bright and dark edges. It suggests a 2Q model with 6 parameters that can fit some past data; however this remains a question of interpretation and does not settle the question conclusively.
|journal=Neuron
|pub_date=2011/06/23
|doi=10.1016/j.neuron.2011.03.028
|subject=Neuroscience
}}

Segregation of object and background motion in the retina

2012-12-15T23:24:21Z

Sj:

{{Summary
|title=Segregation of object and background motion in the retina
|authors=Bence P. Ölveczky, Stephen A. Baccus, Markus Meister
|url=http://www.oeb.harvard.edu/faculty/olveczky/docs/Nature.pdf
|tags=vision, retina, background, object discrimination
|summary=This study tries to determine where the process of distinguishing local motion within a scene begins en route to the brain. This is complicated by the fact that the eye is generally in constant motion across a scene. This distinction is an essential part of detecting moving objects. Responses of different cells in the retina are studied for receptivity to this motion, and a model of retinal circuitry is proposed that explains observed effects through nonlinear pooling over interneurons.

This sort of motion detection is suppressed by global image motion - it depends on separating object from background motion. A mechanism for this is proposed, and a plausible fast-responding biological process described.

== Goals and Methods ==
During fixation of the eye on an object, the retina still drifts over half a degree of retinal angle, at a speed of around 0.5 degrees/s. Any visual processing happens in the background of this drift. Other animals show similar drift, including salamanders and rabbits (used in this experiment). This motion has not received much attention even though a) it seems to make detecting object motion within a scene computationally hard and b) it feels effortless to humans. This study aims to find an easy way for the eye to segregate object from image motion within the retina, before the images from both eyes merge.

The authors study the spike trains of ganglion cells in isolated retinas of salamanders and rabbits. In both cases, scanning the whole retina while showing various images identified a number of ganglions that were highly selective for object motion within a scene and not at all for overall scene motion.
The authors refer to these cells as object motion sensitive (OMS) cells, though they include several functional cell types. In salamanders, these are classified as ON, Weak OFF, and Fast OFF cells; in rabbits they are classified as ON-OFF Direction Selective, ON Brisk Transient, OFF Brisk Transient, and Local Edge Detectors.

Visual images are projected from a computer monitor onto the photoreceptor layer, at a mean intensity of 8mW/m. The spatiotemporal receptive fields of the retinas are mapped through reverse correlation to a flickering black-and-white checkerboard.

By studying these cells in more detail, mechanisms for suppression of coherent motion detection are considered and evaluated, along with models for selectivity for object motion. Underlying neural circuits are suggested to account for this selectivity, for both single and multiple images.

Analogies are drawn between the species studied and others known to have similar cells in their retinas.

== Results and Analysis ==
The retina is clearly observed to sense differential motion via OMS cells, including up to 20% of the retinal cells. They are excited by motion near the center of the field.

While neurons can be suppressed by peripheral motion, this is found to arrive in brief pulses of 100ms or less, when from a similar local feature as would trigger OMS sensing in the center field. This does not account for the general inhibition of background motion. By looking specifically for interneurons that might mediate this suppression, a large amacrine cell is found that responds to coherent jitter with sharp depolarization aligning with the excitation of OMS cells.

By comparing the amacrine cell membrane potential to the time of a ganglion cell spike, an estimate of the precise function of inhibition was made. These cells are polyaxona, with the size and reach to carry out this role

Selectivity to object motion is independent of the pattern that is moving, and to the speed of jitter. And the OMS cells seem to respond to very fine elements of a scene, suggesting they may work via nonlinear pooling of the rectified response of many different subunits, similar to Y-ganglia in cats.

The similar distribution of magnocellular ganglia in primate retinas, known to have nonlinear spatial summation as well, suggests they may have similar OMS properties.

Finally, this mechanism would explain well-known motion illusions such as the Ouchi illusion, which occurs predominantly with images that have horizontal edges in the center field and vertical edges in the periphery, or vice-versa: since these perceptions are processed separately.
|relevance=This analysis suggests that many species have cells that are specially sensitive to object motion, related to those known to carry out nonlinear spatial summation. It presents an analysis that could be carried out in a variety of other settings to probe motion tracking and object-background distinction.

It also identifies a class of illusion that this visual system explains.
|journal=Nature
|pub_date=2003/05/22
|doi=10.1038/nature01652
|subject=Neuroscience
}}

Defining the Computational Structure of the Motion Detector in Drosophila

2012-12-15T22:54:19Z

Sj:

Defining the Computational Structure of the Motion Detector in Drosophila

2012-12-15T22:51:49Z

Sj:

{{Summary
|title=Deﬁning the Computational Structure of the Motion Detector in Drosophila
|authors=Damon A. Clark, Limor Bursztyn, Mark A. Horowitz, Mark J. Schnitzer, Thomas R. Clandinin
|url=http://www.cell.com/neuron/retrieve/pii/S0896627311004417
|tags=Drosophila, Reichardt detector, vision, motion
|summary=This paper tests the hypothesis that information about motion is extracted from shifting intensity patterns on the back of the retina. It shows that a Reichardt-detector model can closely match the neuronal response to visual inputs, and identifies two specific pathways that may carry out pieces of the related computation.

These two pathways (via laminar cells L1 and L2) are shown to respond differently to light vs. dark moving edges, and it is shown they both carry out part of the idealized computation described by a Reichardt detector. A theoretical model is proposed that would be tuneable to match observed responses closely (possibly changing over time).

== Goals and Methods ==
These experiments probe the structure and possible component parts of the part of motion detection in Drosophila that responds to moving edges like a Reichardt detector. While no specific physical components have been identified as carrying out the individual steps of the Reichardt detector algorithm, this work tests the hypothesis that two pathways map to specific subsets of the overall Reichardt computation.

Directional selectivity of flies is observed in response to light and dark edges in intensity patterns, the traditional input used to characterize Reichardt detection. Responses to "phi" and "reverse phi" stimuli are also observed: changes in contrast in the same direction or in opposite direction. Reverse phi is related to an illusion: it causes the observer to turn in the opposite direction that the spatial sequence of contrast change would -- into rather than away from an edge. As it is also observed in animals other than flies, this suggests some overlap in analysis for future study.

In this experiment the flies studied are conscious and held in place suspended just above a ball, which floats at low friction in an airstream. The fly can move its legs to 'walk' along the ball, and the rotation of the ball captures its motion. While in this setup, the flies are shown three screens, ahead and to the left and right; shown the image of a rotationg virtual cylinder with periodic patterns.

First the response of wild-type Drosophila's Reichart correlation is characterized, by showing them two bars side by side, with a randomized range of contrast changes and time delays between successive images. They are also shown rotating cylinders with a fixed periodic square wave.
Genetically modified Drosophila missing either the L1 or L2 pathway are then bred and tested with the same system.

Various tests of the L1 and L2 terminals are carried out to see whether each of them responded to both light and dark stimuli in isolation, and to both increases and decreases in intensity. The response of flies to both phi and reverse phi stimuli are noted and compared to motion sensing in other species.

To understand the role and function of edge selectivity in the L1 and L2 pathways, flies with only one or the other are shown a variety of inputs to find differential responses: including light-light, dark-dark, and phi and revers-phi stimuli.

It is a point of contemporary debate whether all four possible unit computations of the Reichardt detector are carried out: dark-dark, dark-light, light-dark, light-light. Drosophila are shown not only the four combinations of dark and light bars, but also phi and reverse-phi stimuli. Reverse phi stimuli are predicted to produce the opposite response of phi stimuli if all four computations are possible.

A numerical model is developed to match observed edge preferences and responses, with parameters weighting the individual computations of the Reichardt detector to fit observations.

Finally, the implications of two distinct pathways within each Reichardt detector process, for fly motion detection and for understanding other observations of fly neuron responses, are analyzed.

== Results and Analysis ==
The fly response to randomly generated bars roughly matches that of an ideal Reichardt correlator.

Losing L1 reduces response to only the light edges; losing L2 reduces response to only dark edges. To distinguish edge detection from overall changes in light level, an equiluminant stimulus is developed where light and dark edges move in opposite directions at equal speeds, at once. Control flies move only slightly; those without L1 turn towards the dark edge, those without L2 turned towards the light.

This strongly suggests that L1 and L2 pathways are tied to the ability to detect motion of light and dark edges, respectively. However both L1 and L2 are found to respond similarly to light and dark flashes, and to respond to the motion of both light and dark edges. They also respond highly linearly to changes in contrast - either increases or decreases. In contrast to contemporary work that suggested L2 terminals respond to decreases and not increases in brightness, this is shown to be linear quite uniformly across the spectrum of stimulus intensity.

As reverse phi stimuli produce the response predicted by a full Reichardt detector, the authors concluded that all four unit compoutations are carried out. This did not entirely settle the question for other researchers however. Reverse phi stimuli had another interesting property: they produced different complementary responses in flies missing L1 or L2 pathways.

To understand this better, a numerical model is designed for an array of Reichardt detectors, with responses to phi and reverse-phi inputs tuned by weighting the four different unit multiplications in the detector. Weighting phi stimuli equally while weighting reverse-stimuli differentially is enough to reproduce the edge-selction seen in L1 and L2 pathways.

As reverse phi responses are observed in many species other than Drosophila, the analysis and model presented at the end of this study are of potential interest to studies of edge and motion detection in other creatures.

; Notes for future and contemporary researchers
The idea of half-wave rectification of the signal at each input has often been proposed since it is not known how sign-correct multiplication of inputs could be implemented. Rectification may happen elsewhere in the neural circuit, however. These experiments demonstrate that the L1 and L2 cells do not themselves carry out such rectification.

The authors tackle the ongoing debate between "two-computation" and "four-computation" models of a Reichardt detector, which are related to ideas about whether the fly visual system is organized into ON and OFF pathways or simply detecting light v. dark edges. They conclude from their work that the fly visual system is not organized into ON / OFF pathways -- that is, that L1 transmits information about more than just increases in contrast, and L2 transmits information about more than just decreases. This is in contrast to Joesch and Eichner.<ref>see [[Internal Structure of the Fly Elementary Motion Detector]], published in the same issue of Neuron, 2011.</ref>

A few time dependencies are noted related to the laminar cells, as pointers to future study. The flies studied remember the last image they had seen for at least 1 second for the purposes of future reactions. L1 and L2 terminals show different kinetics in respond to prolonged stimuli. And previous work suggests that electrical changes in the cell membrane potential would last for 50-100ms, in contrast to the Ca responses in axonal terminals that last for seconds. This could indicate adaptation of the synapse, or processing within the axon.

Reverse phi is proposed as more than an illusion - but as a specific sort of motion captured by neurons, with functional use. In humans for instance, reverse phi shares properties with motion aftereffects, and may contribute to perception of moving edges of a specific polarity. Edge-detecting cells are a fundamental unit of visual computation, and this work suggests edge-polarity detection is as well.

Finally, as demonstrated most concisely in the numerical model presented, edge selectivity can be characterized by selective weighting of L1 and L2 pathways. Furthermore, this study may only have captured a subset of the actual computations carried out. Other cells may be involved as well - L4 cells may have a significant role to play.

== References ==
<references/>

|relevance=
Changes in light and shadow over time and space are shown to be detected by a system that closely parallels a simple Reichardt detector (or Hassenstein-Reichardt correlator). A model for such a detector is proposed that can be tuned to detect moving edges of contrast.

This clearly distinguished two different neuronal pathways (L1 and L2) as responsible for different parts of vision, by selectively disabling each one separately and quantifying the different changes in edge detection.
|journal=Neuron
|pub_date=2011
|doi=10.1016/j.neuron.2011.05.023
|subject=Neuroscience
}}

2012-12-14T22:30:10Z

Sj:

{{Summary
|title=Energy, Quanta, and Vision
|authors=Selig Hecht, Simon Shlaer, Maurice Henri Pirenne
|tags=vision quanta light eye
|summary=This paper has become a canonical reference for any discussion about how sensitive the human eye is to individual quanta of light. While it was not the first to ask the question and carry out experiments to measure how small a signal was needed to stimulate the eye, it used a simple and universal technique, was careful in its error analysis, and compared its work cleanly with those of past attempts to measure the visual threshold. Previous estimates of the visual threshhold were on the order of 20 quanta of light; this work used statistical analysis to reduce that to 5-7.


|relevance=This was a standard reference for human visual threshold analysis for decades, and the new statistical technique they used changed how future experiments would be analysed.
|journal=The Journal of General Physiology
|pub_date=1942
|subject=Biology
}}

Motility-associated hair-bundle motion in mammalian outer hair cells

2012-12-14T21:48:27Z

Sj:

Motility-associated hair-bundle motion in mammalian outer hair cells

2012-12-14T21:14:38Z

Sj: Created page with "{{Summary |title=Motility-associated hair-bundle motion in mammalian outer hair cells |authors=Shuping Jia, David Z Z He |url=http://courses.washington.edu/pbio525/Paper%20PDFs/J..."

Segregation of object and background motion in the retina

2012-12-14T21:10:03Z

Sj: Created page with "{{Summary |title=Segregation of object and background motion in the retina |authors=Bence P. Ölveczky, Stephen A. Baccus, Markus Meister |url=http://www.oeb.harvard.edu/faculty/..."