Near-Optimal Decoding of Transient Stimuli from Coupled Neuronal Subpopulations

Trousdale, James; Carroll, Samuel R.; Gabbiani, Fabrizio; Josić, Krešimir

doi:10.1523/jneurosci.2671-13.2014

Cited by 5 publications

(17 citation statements)

References 76 publications

(19 reference statements)

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“…Only a few studies [ 56 , 57 ] have attempted to use a probabilistic approach to investigate population coding of motion in the VS network. These studies have focused exclusively on how well the axonal voltage, with/without GJs, could be used to estimate the axis of rotation.…”

Section: Discussionmentioning

confidence: 99%

Efficient encoding of motion is mediated by gap junctions in the fly visual system

et al. 2017

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Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

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Section: Discussionmentioning

confidence: 99%

Efficient encoding of motion is mediated by gap junctions in the fly visual system

et al. 2017

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“…Then we generate a movie mimicking an evasive flight in the natural environment by rotating this natural scene cage according to measured rotations in an evasive flight trajectory [23] showed that the VS network is not responsive to translation motion, we do not use the translation component of evasive maneuvers in this simulation, also see Materials and Methods). We next project this movie onto a unit sphere that represents the fly's retina, following the protocol introduced in [23,64].…”

Section: Visual Prediction Provides Substantial Information About Motmentioning

confidence: 99%

“…Our simulation uses a biophysically realistic simplified model of the VS network based on a reconstruction introduced in [21] (we used the modelDB python package introduced in [64]). This reconstruction models each VS cell with hundreds of dendritic compartments based on image stacks obtained by two-photon microscopy.…”

Section: Simulation Of the In Silico Vs Networkmentioning

confidence: 99%

“…We then rotate this natural scene cube according to the rotational motion during evasive maneuvers recorded in [20] (we sample the rotational motion at a ∆t = 1 ms interval, and integrate the VS response at a smaller time step of 0.01 ms to guarantee numerical accuracy). This yields the optic flow pattern which we then project onto a unit sphere that represents the fly's retina, following the protocol introduced in [23,64]. There are 5,500 local motion detectors (LMD) evenly distributed on that unit sphere.…”

Section: Simulation Of the In Silico Vs Networkmentioning

confidence: 99%

“…Assuming that the fly is located in the center of this cube, we obtain the visual experience of the fly's ego-rotational motion by rotating this cage around a particular motion direction shown by the dark blue arrow. We then project the moving natural scene cage to a unit sphere that represents the fly's retina, following the protocol introduced in[23,64]. There are ∼5,500 local motion detectors (LMD) evenly distributed on this unit sphere.…”

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Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

Wang

Borst

Segev

et al. 2019

Preprint

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9The visual system must make predictions to compensate for inherent delays in its processing, yet 10 little is unknown, mechanistically, about how prediction aids natural behaviors. Here we show that 11 despite a 30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly 12 can achieve maximally efficient prediction. This prediction enables fine discrimination of input 13 motion direction during evasive flight maneuvers, which last just 40ms. Combining a rich database 14 of behavioral recordings with detailed compartmental modeling of the VS network, we further 15 show that the VS network implements this optimal prediction with a specific wiring architecture; 16 axonal gap junctions between the VS cells are crucial for optimal prediction during the short 17 timespan of the evasive maneuver. Furthermore, a subpopulation output of the VS network can 18 selectively convey predictive information about the future visual input to the downstream neck 19 motor center. Our work links prediction to behavior, via its neural implementation. 20 21 36Insects, especially diptera, are excellent models for exploring this problem because precise 37 measurements of their evasive behaviors are available, the neuronal circuits involved are known, 38 and responses can be modeled in rich detail. During an escape, the fly elicits an in-flight evasive 39 maneuver after 60 ms of visual-motor delay. This maneuver adjusts its heading, accelerating the fly 40 1 of 17 away from the threat via a banked turn followed by a counter-banked turn (Muijres et al., 2014). 41 Previous work has shown that banking is the fastest way to change heading (Dickinson and Muijres, 42 2016). The unique challenge in the fly's evasive maneuver is that it lasts only 40 ms, which is 43 roughly the time scale of the visual sensory delay (Land and Collett, 1974). Therefore, previous 44 work hypothesized that the dynamic control of the evasive maneuver is only possible through 45 mechanosensory feedback, i.e., via the haltere circuit (Dickinson and Muijres, 2016). However, to 46 date, there is no quantitative evidence showing that such feedback alone suffices to guide in-flight 47 escape responses. 48 Here we explore whether the fly can use an alternate strategy to control its evasive maneuver, 49 based on predictions of its future visual input. We hypothesize that if the fly can accurately predict 50 the future visual input that it will experience during the fluid banked and counter banked turns, 51 it can use such information to actively control evasive flight maneuvers. We focus on how this 52 visual prediction emerges in the vertical motion sensing (VS) network of the fly visual system, 53 which is organized in four consecutive layers: retina, lamina, lobula and lobula plate. The VS 54 network is located in the lobula plate; it contains 10 lobula plate tangential cells (the VS cells) in 55 each hemisphere that integrate local motion signals from upstream processing. The VS network 56 is dedicated to pr...

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Non-uniform weighting of local motion inputs underlies dendritic computation in the fly visual system

Dan

Hopp

Borst

et al. 2018

Sci Rep

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The fly visual system offers a unique opportunity to explore computations performed by single neurons. Two previous studies characterized, in vivo, the receptive field (RF) of the vertical system (VS) cells of the blowfly (calliphora vicina), both intracellularly in the axon, and, independently using Ca2+ imaging, in hundreds of distal dendritic branchlets. We integrated this information into detailed passive cable and compartmental models of 3D reconstructed VS cells. Within a given VS cell type, the transfer resistance (TR) from different branchlets to the axon differs substantially, suggesting that they contribute unequally to the shaping of the axonal RF. Weighting the local RFs of all dendritic branchlets by their respective TR yielded a faithful reproduction of the axonal RF. The model also predicted that the various dendritic branchlets are electrically decoupled from each other, thus acting as independent local functional subunits. The study suggests that single neurons in the fly visual system filter dendritic noise and compute the weighted average of their inputs.

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Near-Optimal Decoding of Transient Stimuli from Coupled Neuronal Subpopulations

Cited by 5 publications

References 76 publications

Efficient encoding of motion is mediated by gap junctions in the fly visual system

Efficient encoding of motion is mediated by gap junctions in the fly visual system

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

Non-uniform weighting of local motion inputs underlies dendritic computation in the fly visual system

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