Logarithmic Compression of Sensory Signals within the Dendritic Tree of a Collision-Sensitive Neuron

Jones, Peter W.; Gabbiani, Fabrizio

doi:10.1523/jneurosci.5777-11.2012

Cited by 32 publications

(36 citation statements)

References 52 publications

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“…The most compelling studies on this subject have been carried out in the LGMD neuron of the locust, using biophysical computational models (Rind and Bramwell, 1996;Peron and Gabbiani, 2009;Jones and Gabbiani, 2012). In the present study, we showed that MLG2 shares several physiological features with the locust's LGMD and with the crab's MLG1.…”

Section: Physiological Model Of the Mlg2 Response To Looming Stimulisupporting

confidence: 57%

“…The model proposed by Jones and Gabbiani (2012) for the biophysical implementation of the computation performed by the locust's LGMD neuron in response to looming stimuli includes two aspects: (1) the effect of synaptic excitation and inhibition at the level of the LGMD's membrane potential (see below) and (2) the transformation of the membrane potential into the LGMD's firing rate by a nonlinear input-output relationship. To evaluate the relationship between the MLG2 membrane potential and its firing rate we employed a median filter to cut off the action potentials (Fig.…”

Section: Synaptic Inhibitionmentioning

confidence: 99%

“…Our model for looming detection in the MLG2 neuron has been inspired by computational models for the locust's LGMD neuron (Rind and Bramwell, 1996;Peron and Gabbiani, 2009;Jones and Gabbiani, 2012). Similar to our model for MLG1 neurons (Oliva and Tomsic, 2014), the model for the MLG2 assumes that each columnar pathway supplies information on variations in luminosity for each ommatidium (Fig.…”

Section: Biophysical Model Of Mlg2 For Looming Detectionmentioning

confidence: 99%

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Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Oliva

Tomsic

2016

Journal of Experimental Biology

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Upon detection of an approaching object, the crab Neohelice granulata continuously regulates the direction and speed of escape according to ongoing visual information. These visuomotor transformations are thought to be largely accounted for by a small number of motion-sensitive giant neurons projecting from the lobula (third optic neuropil) towards the supraesophageal ganglion. One of these elements, the monostratified lobula giant neuron of type 2 (MLG2), proved to be highly sensitive to looming stimuli (a 2D representation of an object approach). By performing in vivo intracellular recordings, we assessed the response of the MLG2 neuron to a variety of looming stimuli representing objects of different sizes and velocities of approach. This allowed us to: (1) identify some of the physiological mechanisms involved in the regulation of the MLG2 activity and test a simplified biophysical model of its response to looming stimuli; (2) identify the stimulus optical parameters encoded by the MLG2 and formulate a phenomenological model able to predict the temporal course of the neural firing responses to all looming stimuli; and (3) incorporate the MLG2-encoded information of the stimulus (in terms of firing rate) into a mathematical model able to fit the speed of the escape run of the animal. The agreement between the model predictions and the actual escape speed measured on a treadmill for all tested stimuli strengthens our interpretation of the computations performed by the MLG2 and of the involvement of this neuron in the regulation of the animal's speed of run while escaping from objects approaching with constant speed.

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Section: Physiological Model Of the Mlg2 Response To Looming Stimulisupporting

confidence: 57%

Section: Synaptic Inhibitionmentioning

confidence: 99%

Section: Biophysical Model Of Mlg2 For Looming Detectionmentioning

confidence: 99%

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Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Oliva

Tomsic

2016

Journal of Experimental Biology

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“…In particular, the interaction between retinotopic synaptic inputs and active dendritic conductances could increase responses to looming stimuli and opposed to matched, spatially randomized controls, thus implementing a form of visual object segmentation (R. Dewell and F. Gabbiani, unpublished observations). Testing this hypothesis will require us to incorporate the wiring scheme and active conductances in detailed compartmental simulations of the LGMD neuron along the lines of our previous simulations (Jones and Gabbiani 2012).…”

Section: Discussionmentioning

confidence: 99%

Fine and distributed subcellular retinotopy of excitatory inputs to the dendritic tree of a collision-detecting neuron

Zhu

Gabbiani

2016

Journal of Neurophysiology

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Zhu Y, Gabbiani F. Fine and distributed subcellular retinotopy of excitatory inputs to the dendritic tree of a collision-detecting neuron. J Neurophysiol 115: 3101-3112, 2016. First published March 23, 2016 doi:10.1152/jn.00044.2016.-Individual neurons in several sensory systems receive synaptic inputs organized according to subcellular topographic maps, yet the fine structure of this topographic organization and its relation to dendritic morphology have not been studied in detail. Subcellular topography is expected to play a role in dendritic integration, particularly when dendrites are extended and active. The lobula giant movement detector (LGMD) neuron in the locust visual system is known to receive topographic excitatory inputs on part of its dendritic tree. The LGMD responds preferentially to objects approaching on a collision course and is thought to implement several interesting dendritic computations. To study the fine retinotopic mapping of visual inputs onto the excitatory dendrites of the LGMD, we designed a custom microscope allowing visual stimulation at the native sampling resolution of the locust compound eye while simultaneously performing two-photon calcium imaging on excitatory dendrites. We show that the LGMD receives a distributed, fine retinotopic projection from the eye facets and that adjacent facets activate overlapping portions of the same dendritic branches. We also demonstrate that adjacent retinal inputs most likely make independent synapses on the excitatory dendrites of the LGMD. Finally, we show that the fine topographic mapping can be studied using dynamic visual stimuli. Our results reveal the detailed structure of the dendritic input originating from individual facets on the eye and their relation to that of adjacent facets. The mapping of visual space onto the LGMD's dendrites is expected to have implications for dendritic computation. lobula giant movement detector; descending contralateral movement detector; looming; collision avoidance; locust

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“…Because the firing rate of the LGMD is described by multiplying the angular speed of an approaching object with a negative exponential of its angular size, this suggested that feed-forward inhibition might code for angular size [20]. Excitation has already been shown to encode a function of angular speed [27]. The electrophysiological recordings we report allowed us to test whether the firing rate of inhibitory neurons presynaptic to the LGMD encodes the angular size, or speed of looming stimuli, or functions of them.…”

Section: Introductionmentioning

confidence: 99%

Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit

et al. 2018

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Feedforward inhibition is ubiquitous as a motif in the organization of neuronal circuits. During sensory information processing, it is traditionally thought to sharpen the responses and temporal tuning of feedforward excitation onto principal neurons. As it often exhibits complex time-varying activation properties, feedforward inhibition could also convey information used by single neurons to implement dendritic computations on sensory stimulus variables. We investigated this possibility in a collision-detecting neuron of the locust optic lobe that receives both feedforward excitation and inhibition. We identified a small population of neurons mediating feedforward inhibition, with wide visual receptive fields and whose responses depend both on the size and speed of moving stimuli. By studying responses to simulated objects approaching on a collision course, we determined that they jointly encode the angular size of expansion of the stimulus. Feedforward excitation, on the other hand, encodes a function of the angular velocity of expansion and the targeted collision-detecting neuron combines these two variables non-linearly in its firing output. Thus, feedforward inhibition actively contributes to the detailed firing-rate time course of this collision-detecting neuron, a feature critical to the appropriate execution of escape behaviors. These results suggest that feedforward inhibition could similarly convey time-varying stimulus information in other neuronal circuits.

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Logarithmic Compression of Sensory Signals within the Dendritic Tree of a Collision-Sensitive Neuron

Cited by 32 publications

References 52 publications

Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Fine and distributed subcellular retinotopy of excitatory inputs to the dendritic tree of a collision-detecting neuron

Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit

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