FBFlorence Blanchard

Florence Blanchard

*This is an algorithmically generated author record based on publicly available information.

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Florence Blanchard has a total of 33 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Binocular Mirror-Symmetric Microsaccadic Sampling Enables Drosophila Hyperacute 3D-Vision

    by Joni Kemppainen, Ben Scales, Keivan Razban Haghighi, Jouni Takalo, Neveen Mansour, James McManus, Gabor Leko, Paulus Saari, James Hurcomb, Andra Antohi, et al.

    Neural mechanisms behind stereopsis, which requires simultaneous disparity inputs from two eyes, have remained mysterious. Here we show how ultrafast mirror-symmetric photomechanical contractions in the frontal forward-facing left and right eye photoreceptors give Drosophila super-resolution 3D-vision. By interlinking multiscale in vivo assays with multiscale simulations, we reveal how these photoreceptor microsaccades - by verging, diverging and narrowing the eyes’ overlapping receptive fields - channel depth information, as phasic binocular image motion disparity signals in time. We further show how peripherally, outside stereopsis, microsaccadic sampling tracks a flying fly’s optic flow field to better resolve the world in motion. These results change our understanding of how insect compound eyes work and suggest a general dynamic stereo-information sampling strategy for animals, robots and sensors. Significance statement: To move efficiently, animals must continuously work out their x,y,z-positions in respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a new morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila super-resolution 3D-vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.


  • Ca2+-Activated K+ Channels Reduce Network Excitability, Improving Adaptability and Energetics for Transmitting and Perceiving Sensory Information

    by Xiaofeng Li, Ahmad Abou Tayoun, Zhuoyi Song, An Dau, Diana Rien, David Jaciuch, Sidhartha Dongre, Florence Blanchard, Anton Nikolaev, Lei Zheng, et al.

    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 mutants9 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. SIGNIFICANCE STATEMENT In this study, we directly link in vivo and ex vivo experiments with detailed stochastically operating biophysical models to extract new mechanistic knowledge of how Drosophila photoreceptor-interneuron-photoreceptor (R-LMC-R) circuitry homeostatically retains its information sampling and transmission capacity against chronic perturbations in its ion-channel composition, and what is the cost of this compensation and its impact on optomotor behavior. We anticipate that this novel approach will provide a useful template to other model organisms and computational neuroscience, in general, in dissecting fundamental mechanisms of homeostatic compensation and deepening our understanding of how biological neural networks work.