Models that contain the Region : Drosophila

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    Models   Description
1.  Accelerating with FlyBrainLab discovery of the functional logic of Drosophila brain (Lazar et al 21)
In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab’s User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.
2.  Comparing correlation responses to motion estimation models (Salazar-Gatzimas et al. 2016)
Code to generate responses of HRC-like and BL-like model elementary motion detectors to correlated noise stimuli, including two models with more realistic temporal filtering.
3.  Continuous lateral oscillations as a mechanism for taxis in Drosophila larvae (Wystrach et al 2016)
" ...Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. ..."
4.  Drosophila 3rd instar larval aCC motoneuron (Gunay et al. 2015)
Single compartmental, ball-and-stick models implemented in XPP and full morphological model in Neuron. Paper has been submitted and correlates anatomical properties with electrophysiological recordings from these hard-to-access neurons. For instance we make predictions about location of the spike initiation zone, channel distributions, and synaptic input parameters.
5.  Drosophila circadian clock neurone model of essential tremor (Smith et al 2018)
Model of Drosophila ventral lateral neuron (LNV) used to study a human ion channel associated with essential tremor.
6.  Drosophila lateral ventral clock neuron (LNV) model (Smith et al 2019)
LNVmodel models the activity of a Drosophila lateral ventral clock neurons (LNV) neurone.
7.  Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009)
We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model.
8.  Drosophila T4 neuron (Gruntman et al 2018)
Passive, multi-compartment conductance-based model of a T4 cell. The model reproduces the neuron's response to moving stimuli via integration of spatially offset fast excitatory and slow inhibitory inputs.
9.  Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)
"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies."
10.  Feature integration drives probabilistic behavior in Fly escape response (von Reyn et al 2017)
"... A Linear Model for Visual Feature Integration in the GF (Drosophila Giant Fiber) Circuit. To test our hypothesis that the GFs linearly integrate the separately encoded features of looming stimulus size and angular velocity, we developed a model to predict GF membrane potential across visual stimuli (Figure 8A). In this four-component model, the GFs linearly sum two excitatory components— non-LC4(Type 4 lobula columnar neurons)-mediated angular size excitation and LC4-mediated angular velocity excitation—and two inhibitory components— non-LC4- and LC4-mediated angular size inhibition."
11.  Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999)
In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme).
12.  Generation of stable heading representations in diverse visual scenes (Kim et al 2019)
"Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by ‘compass’ neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from ‘ring’ neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. ... " See the supplementary information for model details.
13.  How BK and SK channels benefit early vision (Li X et al 2019)
"Ca2+-activated K+ channels (BK and SK) are ubiquitous in synaptic circuits, but their role in network adaptation and sensory perception remains largely unknown. Using electrophysiological and behavioral assays and biophysical modeling, we discover how visual information transfer in mutants lacking the BK channel (dSlo- ), SK channel (dSK- ), or both (dSK- ;; dSlo- ) is shaped in the female fruit fly (Drosophila melanogaster) R1-R6 photoreceptor-LMC circuits (R-LMC-R system) through synaptic feedforward-feedback interactions and reduced R1-R6 Shaker and Shab K+ conductances. This homeostatic compensation is specific for each mutant, leading to distinctive adaptive dynamics. We show how these dynamics inescapably increase the energy cost of information and promote the mutants' distorted motion perception, determining the true price and limits of chronic homeostatic compensation in an in vivo genetic animal model. These results reveal why Ca2+-activated K+ channels reduce network excitability (energetics), improving neural adaptability for transmitting and perceiving sensory information. ..."
14.  Reichardt Model for Motion Detection in the Fly Visual System (Tuthill et al, 2011)
This simulation implements a correlation-type model for visual motion detection, as originally described by Hassenstein and Reichardt (1956), and analyzes the response of the model to standard and reverse-phi motion stimuli. Details are provided in: Tuthill JC, et al. (2011)

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