Abstract:How neurons become sensitive to the direction of visual motion represents a classic example of neural computation. Two alternative mechanisms have been discussed in the literature so far: preferred direction enhancement, by which responses are amplified when stimuli move along the preferred direction of the cell, and null direction suppression, where one signal inhibits the response to the subsequent one when stimuli move along the opposite, i.e. null direction. Along the processing chain in the Drosophila opt… Show more
“…Our data also corroborate prior findings that motion pathways functionally process information over at least three points in space and incorporate responses to null direction displacements [31, 48, 53]. The response patterns we observed could arise from LN-style models of motion detection, in which stimuli are linearly filtered before a static nonlinearity is applied [13, 29, 31].…”
Section: Discussionsupporting
confidence: 88%
“…This 1-dimensional natural scene eliminated spatial variation in the dimension perpendicular to the motion. While the lack of variation may have affected response amplitudes [48], these cells are relatively insensitive to spatial structure in the orientation perpendicular to the motion dimension [13]. Nonetheless, this stimulus retained edge-structure in the orientation preferred by these cells [49].…”
Section: Resultsmentioning
confidence: 99%
“…When we compared apparent motion responses with summed responses to each bar in the pair, we indeed found clear evidence of nonlinearity in T4 and T5 neurons, though some of that may be attributable to indicator nonlinearities (Figure S6). Many studies go further to categorize direction-selective mechanisms according to whether they enhance responses in the preferred-direction or suppress responses in the null-direction [31, 48, 49, 53]. However, it is important to note that it can be ambiguous to characterize the system as ‘enhancing’ or ‘suppressing’ by asking whether the response to the paired stimuli is greater than or less than the sum of the responses to each isolated stimulus component.…”
Summary
Both vertebrates and invertebrates perceive illusory motion, known as “reverse-phi”, in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila’s elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.
“…Our data also corroborate prior findings that motion pathways functionally process information over at least three points in space and incorporate responses to null direction displacements [31, 48, 53]. The response patterns we observed could arise from LN-style models of motion detection, in which stimuli are linearly filtered before a static nonlinearity is applied [13, 29, 31].…”
Section: Discussionsupporting
confidence: 88%
“…This 1-dimensional natural scene eliminated spatial variation in the dimension perpendicular to the motion. While the lack of variation may have affected response amplitudes [48], these cells are relatively insensitive to spatial structure in the orientation perpendicular to the motion dimension [13]. Nonetheless, this stimulus retained edge-structure in the orientation preferred by these cells [49].…”
Section: Resultsmentioning
confidence: 99%
“…When we compared apparent motion responses with summed responses to each bar in the pair, we indeed found clear evidence of nonlinearity in T4 and T5 neurons, though some of that may be attributable to indicator nonlinearities (Figure S6). Many studies go further to categorize direction-selective mechanisms according to whether they enhance responses in the preferred-direction or suppress responses in the null-direction [31, 48, 49, 53]. However, it is important to note that it can be ambiguous to characterize the system as ‘enhancing’ or ‘suppressing’ by asking whether the response to the paired stimuli is greater than or less than the sum of the responses to each isolated stimulus component.…”
Summary
Both vertebrates and invertebrates perceive illusory motion, known as “reverse-phi”, in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila’s elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.
“…To test the direct role of synaptic function in mPN2 connectivity refinement in the MB calyx, we drove the enzymatically active tetanus toxin light chain (UAS-TNT) to sever communication between mPN2 and downstream KCs [36]. This neurotoxin protease cleaves the essential synaptic vesicle v-SNARE synaptobrevin, leading to a complete blockade of neurotransmission [37].…”
Summary
Activity-dependent synaptic remodeling occurs during early-use critical periods, when naïve juveniles experience sensory input. Fragile X Mental Retardation Protein (FMRP) sculpts synaptic refinement in an activity sensor mechanism based on sensory cues, with FMRP loss causing the most common heritable autism spectrum disorder (ASD), Fragile X syndrome (FXS). In the well-mapped Drosophila olfactory circuitry, projection neurons (PN) relay peripheral sensory information to the central brain mushroom body (MB) learning/memory center. FMRP-null PNs reduce synaptic branching and enlarge boutons, with ultrastructural and synaptic reconstitution MB connectivity defects. Critical period activity modulation via odorant stimuli, optogenetics and transgenic tetanus toxin neurotransmission block show elevated PN activity phenocopies FMRP-null defects, while PN silencing causes opposing changes. FMRP-null PNs lose activity-dependent synaptic modulation, with impairments restricted to the critical period. We conclude FMRP is absolutely required for experience-dependent changes in synaptic connectivity during the developmental critical period of neural circuit optimization for sensory input.
“…The problem has long been analysed using mathematical models that compare two adjacent viewpoints, of which the Hassenstein-Reichardt (HR) elementary motion detector (EMD; Hassenstein and Reichardt, 1956; Figure 1A) or Barlow-Levick (BL) model (Barlow and Levick, 1965; Figure 1B) are both widely canvassed. Both models compare two visual inputs separated in time and space and simulate motion detection well (Haag et al, 2016). However, the exact neural mechanism underlying the detection of direction by motion-sensing cells is still understood only imperfectly.10.7554/eLife.24394.002Figure 1.Visual motion-detection pathways and circuits in Drosophila .( A, B ) Diagrams of two major predicted models of motion detection: ( A ) the Hassenstein-Reichardt (HR) and ( B ) Barlow-Levick (BL) type.…”
Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila‘s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4’s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.DOI:
http://dx.doi.org/10.7554/eLife.24394.001
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