scholarly journals Neural computations combine low- and high-order motion cues similarly, in dragonfly and monkey

2017 ◽  
Author(s):  
Eyal I. Nitzany ◽  
Gil Menda ◽  
Paul S. Shamble ◽  
James R. Golden ◽  
Qin Hu ◽  
...  

AbstractVisual motion analysis is fundamental to survival across the animal kingdom. In insects, our understanding of the underlying computations has centered on the Hassenstein-Reichardt motion detector, which computes two-point cross-correlation via multiplication; in mammalian cortex, it is postulated that a similar signal is computed by comparing matched squaring operations. Both of these operations are difficult to implement biophysically in a precise fashion; moreover, they fail to detect the more complex multipoint local motion cues present in the visual environment. Here, via single-unit recordings in two visual specialists, dragonfly "(Odonata)" and macaque, and via model simulations, we show that neuronal computations are not simply approximations to idealized behaviors forced by biological constraints, but rather, are signatures of a common computational strategy to capture multiple local motion cues. The similarity of motion computations at the neuronal level in the brains of two extremely dissimilar animals, with evolutionary divergence of over 700 Myr1, suggests convergence on a common computational scheme for detecting visual motion.

Neuron ◽  
2018 ◽  
Vol 100 (1) ◽  
pp. 229-243.e3 ◽  
Author(s):  
Erin L. Barnhart ◽  
Irving E. Wang ◽  
Huayi Wei ◽  
Claude Desplan ◽  
Thomas R. Clandinin

Author(s):  
Yuan Tian ◽  
Zhaohui Che ◽  
Wenbo Bao ◽  
Guangtao Zhai ◽  
Zhiyong Gao

The first five sections represent the foundation and offer various intelligent algorithms that are the basics for motion detectors and their realization. There are two classes of security system alarm triggers: physical motion sensor and visual motion sensors. Both analog motion detectors and digital motion detectors belong to the group of visual motion sensors. Digital motion detector systems should differentiate between activities that are acceptable and those that breach security. When security-breaching acts occur, the system should identify the individuals and instruct security personnel what to do. Motion detectors can surveil, detect, and assess, as well as analyze information and distribute information to security personnel. Motion detector systems drastically reduce the load of footage that guards must watch for a long period of time. Automated motion detectors are now a standard for serious medium to large security installations; they are necessary for high detection capabilities. All security systems must have an alarming device to signal the guard of irregular motion in a scene, even systems that have a tiny or huge number of cameras.


1999 ◽  
Vol 81 (3) ◽  
pp. 1057-1074 ◽  
Author(s):  
L. G. Nowak ◽  
M.H.J. Munk ◽  
A. C. James ◽  
P. Girard ◽  
J. Bullier

Cross-correlation study of the temporal interactions between areas V1 and V2 of the macaque monkey. Cross-correlation studies performed in cat visual cortex have shown that neurons in different cortical areas of the same hemisphere or in corresponding areas of opposite hemispheres tend to synchronize their activities. The presence of synchronization may be related to the parallel organization of the cat visual system, in which different cortical areas can be activated in parallel from the lateral geniculate nucleus. We wanted to determine whether interareal synchronization of firing can also be observed in the monkey, in which cortical areas are thought to be organized in a hierarchy spanning different levels. Cross-correlation histograms (CCHs) were calculated from pairs of single or pairs of multiunit activities simultaneously recorded in areas V1 and V2 of paralyzed and anesthetized macaque monkeys. Moving bars and flashed bars were used as stimuli. The shift predictor was calculated and subtracted from the raw CCH to reveal interactions of neuronal origin in isolation. Significant CCH peaks, indicating interactions of neuronal origin, were obtained in 11% of the dual single-unit recordings and 46% of the dual multiunit recordings with moving bars. The incidence of nonflat CCHs with flashed bars was 29 and 78%, respectively. For the pairs of recording sites where both flashed and moving stimuli were used, the incidences of significant CCHs were very similar. Three types of peaks were distinguished on the basis of their width at half-height: T (<16 ms), C (between 16 and 180 ms), and H peaks (>180 ms). T peaks were very rarely observed (<1% in single-unit recordings). H peaks were observed in 7–16% of the single-unit CCHs, and C peaks in 6–16%, depending on the stimulus used. C and H peaks were observed more often when the receptive fields were overlapping or distant by <2°. To test for the presence of synchronization between neurons in areas V1 and V2, we measured the position of the CCH peak with respect to the origin of the time axis of the CCH. Only in the case of a few T peaks did we find displaced peaks, indicating a possible drive of the V2 neuron by the simultaneously recorded V1 cell. All the other peaks were either centered on the origin or overlapped the origin of time with their upper halves. Thus similarly to what has been reported for the cat, neurons belonging to different cortical areas in the monkey tend to synchronize the time of emission of their action potentials with three different levels of temporal precision. For peaks calculated from flashed stimuli, we compared the peak position with the difference between latencies of V1 and V2 neurons. There was a clear correlation for single-unit pairs in the case of C peaks. Thus the position of a C peak on the time axis appears to reflect the order of visual activation of the correlated neurons. The coupling strength for H peaks was smaller during visual drive compared with spontaneous activity. On the contrary, C peaks were seen more often and were stronger during visual stimulation than during spontaneous activity. This suggests that C-type synchronization is associated with the processing of visual information. The origin of synchronized activity in a serially organized system is discussed.


PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0220878 ◽  
Author(s):  
Sean Dean Lynch ◽  
Anne-Hélène Olivier ◽  
Benoit Bideau ◽  
Richard Kulpa

Neuroforum ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. A61-A72 ◽  
Author(s):  
Giordano Ramos-Traslosheros ◽  
Miriam Henning ◽  
Marion Silies

Abstract Many animals use visual motion cues to inform different behaviors. The basis for motion detection is the comparison of light signals over space and time. How a nervous system performs such spatiotemporal correlations has long been considered a paradigmatic neural computation. Here, we will first describe classical models of motion detection and introduce core motion detecting circuits in Drosophila. Direct measurements of the response properties of the first direction-selective cells in the Drosophila visual system have revealed new insights about the implementation of motion detection algorithms. Recent data suggest a combination of two mechanisms, a nonlinear enhancement of signals moving into the preferred direction, as well as a suppression of signals moving into the opposite direction. These findings as well as a functional analysis of the circuit components have shown that the microcircuits that process elementary motion are more complex than anticipated. Building on this, we have the opportunity to understand detailed properties of elementary, yet intricate microcircuits.


Perception ◽  
1997 ◽  
Vol 26 (8) ◽  
pp. 995-1010 ◽  
Author(s):  
Oliver Braddick

Human subjects can perceive global motion or motions in displays containing diverse local motions, implying representation of velocity at multiple scales. The phenomena of flexible global direction judgments, and especially of motion transparency, also raise the issue of whether the representation of velocity at any one scale is single-valued or multi-valued. A new performance-based measure of transparency confirms that the visual system represents directional information for each component of a transparent display. However, results with the locally paired random-dot display introduced by Qian et al, show that representations of multiple velocities do not coexist at the finest spatial scale of motion analysis. Functionally distinct scales of motion processing may be associated with (i) local motion detectors which show a strong winner-take-all interaction; (ii) spatial integration of local signals to disambiguate velocity; (iii) selection of reliable velocity signals as proposed in the model of Nowlan and Sejnowski; (iv) object-based or surface-based representations that are not necessarily organised in a fixed spatial matrix. These possibilities are discussed in relation to the neurobiological organisation of the visual motion pathway.


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