Biology subjects

Gabbiani, F.

Publications and source records attributed to Gabbiani, F..

6 recordsLinked to original sources

bioRxiv2018

Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve membrane synchrony

Our brain processes information through the coordinated efforts of billions of individual neurons, each of which transforms a small part of the overall information stream. Central to this is how neurons integrate and transform complex patterns of synaptic inputs. The neuronal membrane impedance determines the change in membrane potential in response to input currents, and therefore sets the gain and timing for synaptic integration. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the role of two active conductances gH and gM, meditated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-channels, in shaping the membrane impedance of a collision detection neuron in female Schistocerca americana grasshoppers. The neuron is known by its acronym LGMD, which stands for lobula giant movement detector. In contrast to other neurons where these conductances have been studied, we found that gH and gM promote broadband, synchronous integration over the LGMDs functional range of membrane potentials and input frequencies. Additionally, we found that the branching morphology of the LGMD helped increase both the gain and synchrony associated with the neurons membrane impedance. The same result held for a wide range of dendritic morphologies, including those of mammalian neocortical pyramidal neurons and cerebellar Purkinje cells. Thus, these findings further our understanding of the integration properties of individual neurons by showing the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.\n\nSignificance StatementInformation in the brain is processed by neurons that receive thousands of synaptic inputs. Understanding how neurons integrate these inputs is critical to neuroscience. Neuronal integration depends on complex interactions of synaptic input patterns and the electrochemical properties of dendrites. Although examining the input patterns and dendritic processing in vivo is not yet possible in the mammalian brain, it is within simpler nervous systems. Here, we used an identified collision detection neuron in grasshoppers to examine how its morphology and membrane properties determine the gain and synchrony of synaptic integration in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore will further a general understanding of neuronal computations, including those in our own brain.

neuroscience↗

bioRxiv2018

M-current regulates firing mode and spike reliability in a collision detecting neuron

All animals must detect impending collisions to escape them, and they must reliably discriminate them from non-threatening stimuli to prevent false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision detection neuron in the grasshopper Schistocerca americana using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye, and it has many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and non-burst firing. Here, we demonstrate that the LGMD neuron exhibits a large M current, generated by non-inactivating K+ channels, that narrows the window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMDs ability to detect impending collisions our results suggest that it may play an analogous role in other collision detection circuits.\n\nNew & NoteworthyThe ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision detecting neuron and showed that through regulation of burst firing and increasing spiking reliability the M current increases the ability to detect impending collisions.

neuroscience↗

bioRxiv2018

Optogenetic manipulation of medullary neurons in the locust optic lobe

Locust is a widely used animal model for studying sensory processing and its relation to behavior. Due to the lack of genomic information, genetic tools to manipulate neural circuits in locusts are not yet available. We examined whether Semliki Forest virus is suitable to mediate exogenous gene expression in neurons of the locust optic lobe. We subcloned a channelrhodopsin variant and the yellow fluorescent protein Venus into a Semliki Forest virus vector and injected the virus into the optic lobe of locusts (Schistocerca americana). Fluorescence was observed in all injected optic lobes. Most neurons that expressed the recombinant proteins were located in the first two neuropils of the optic lobe, the lamina and medulla. Extracellular recordings demonstrated that laser illumination increased the firing rate of medullary neurons expressing channelrhodopsin. The optogenetic activation of the medullary neurons also triggered firing of a postsynaptic, looming-sensitive neuron, the Lobula Giant Movement Detector (LGMD). These results indicate that Semliki Forest virus is efficient at mediating transient exogenous gene expression and provides a tool to manipulate neural circuits in the locust nervous system and likely other insects.\n\nNew and NoteworthyUsing Semliki Forest virus, we efficiently delivered channelrhodopsin into neurons of the locust optic lobe. We demonstrate that laser illumination increases the firing of the medullary neurons expressing channelrhodopsin and of an identified postsynaptic target neuron, the LGMD neuron. This technique allows to manipulate the neuronal activity in locust neural circuits using optogenetics.

neuroscience↗

bioRxiv2017

Neural code subserving feed-forward inhibition in a collision detection circuit

Feed-forward 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 feed-forward excitation onto principal neurons. As it often exhibits complex time-varying activation properties, feed-forward 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 feed-forward excitation and inhibition. We identified a small population of neurons mediating feed-forward 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. Feed-forward 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, feed-forward 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 feed-forward inhibition could similarly convey time-varying stimulus information in other neuronal circuits.

neuroscience↗

bioRxiv2017

Muscarinic Lateral Excitation Contributes to Visual Object Segmentation during Collision Avoidance

Visual neurons specialized in tracking objects on a collision course are often finely tuned to their target stimuli as this is critical for survival. The presynaptic neural networks converging on these neurons and their role in tuning them remains poorly understood. We took advantage of well-known characteristics of one such neuron to investigate the properties of its presynaptic input network. We find a structure more complex than hitherto realized. In addition to dynamic lateral inhibition used to filter out background motion, presynaptic circuits include normalizing inhibition and short-range lateral excitatory interactions mediated by muscarinic acetylcholine receptors. These interactions preferentially boost responses to coherently expanding visual stimuli generated by colliding objects, as opposed to spatially incoherent controls, helping implement object segmentation. Hence, in addition to active dendritic conductances within collision detecting neurons, multiple layers of both inhibitory and excitatory presynaptic connections are needed to finely tune neural circuits for collision detection.

neuroscience↗

bioRxiv2017

Biophysics of object segmentation in a collision-detecting neuron

Collision avoidance is critical for survival, including in humans, and many species possess visual neurons exquisitely sensitive to objects approaching on a collision course. The most studied such collision-detecting neuron within the optic lobe of grasshoppers has long served as a model for understanding collision avoidance behaviors and their underlying neural computations. Here, we demonstrate that this neuron detects the spatial coherence of a simulated impending object, thereby carrying out a computation akin to object segmentation critical for proper escape behavior. At the cellular level, object segmentation relies on a precise selection of the spatiotemporal pattern of synaptic inputs by dendritic membrane potential-activated channels. One channel type linked to dendritic computations in many neural systems, the hyperpolarization-activated cation channel, HCN, plays a central role in this computation as its pharmacological block abolishes the neuron's spatial selectivity and impairs the generation of visually guided escape behaviors, making it directly relevant to survival. Our results elucidate how active dendritic channels produce neuronal and behavioral object specificity by discriminating between complex spatiotemporal synaptic activation patterns.

neuroscience↗