Biology subjects

Dedieu, A.

Publications and source records attributed to Dedieu, A..

5 recordsLinked to original sources

bioRxiv2021

Learning attention-controllable border-ownership for objectness inference and binding

Human visual systems can parse a scene composed of novel objects and infer their surfaces and occlusion relationships without relying on object-specific shapes or textures. Perceptual grouping can bind together spatially disjoint entities to unite them as one object even when the object is entirely novel, and bind other perceptual properties like color and texture to that object using object-based attention. Border-ownership assignment, the assignment of perceived occlusion boundaries to specific perceived surfaces, is an intermediate representation in the mammalian visual system that facilitates this perceptual grouping. Since objects in a scene can be entirely novel, inferring border ownership requires integrating global figural information, while dynamically postulating what the figure is, a chicken-and egg process that is complicated further by missing or conflicting local evidence regarding the presence of boundaries. Based on neuroscience observations, we introduce a model - the cloned Markov random field (CMRF)- that can learn attention-controllable representations for border-ownership. Higher-order contour representations that distinguish border-ownerships emerge as part of learning in this model. When tested with a cluttered scene of novel 2D objects with noisy contour-only evidence, the CMRF model is able to perceptually group them, despite clutter and missing edges. Moreover, the CMRF is able to use occlusion cues to bind disconnected surface elements of novel objects into coherent objects, and able to use top-down attention to assign border ownership to overlapping objects. Our work is a step towards dynamic binding of surface elements into objects, a capability that is crucial for intelligent agents to interact with the world and to form entity-based abstractions.

neuroscience↗

bioRxiv2020

Targeted-Antibacterial-Plasmids (TAPs) combining conjugation and CRISPR /Cas systems achieve strain-specific antibacterial activity

The global emergence of drug-resistant bacteria leads to the loss of efficacy of our antibiotics arsenal and severely limits the success of currently available treatments. Here, we developed an innovative strategy based on Targeted-Antibacterial-Plasmids (TAPs) that use bacterial conjugation to deliver CRISPR/Cas systems exerting a strain-specific antibacterial activity. TAPs are highly versatile as they can be directed against any specific genomic or plasmid DNA using the custom algorithm (CSTB) that identifies appropriate targeting spacer sequences. We demonstrate the ability of TAPs to induce strain-selective killing by introducing lethal double strand breaks (DSBs) into the targeted genomes. TAPs directed against a plasmid-born carbapenem resistance gene efficiently resensitise the strain to the drug. This work represents an essential step towards the development of an alternative to antibiotic treatments, which could be used for in situ microbiota modification to eradicate targeted resistant and/or pathogenic bacteria without affecting other non-targeted bacterial species.

microbiology↗

bioRxiv2020

A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Efficient inference and generalization guided the representational choices in the original computational model. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.

neuroscience↗

bioRxiv2020

Bacterial filament division dynamics allows rapid post-stress cell proliferation

Many bacterial species grow into filaments under stress conditions. Initially regarded as an indicator of cell death, filamentation is now proposed to be a transient morphological change that improves bacterial survival in hostile environments. However, the mechanism of filament recovery remains poorly characterized. Using real-time microscopy in live-cells, we analysed the fate of filamentous Escherichia coli induced by antibiotic-mediated specific inhibition of cell division, or by UV-induced DNA-damage that additionally perturbs chromosome segregation. Both filament types recover by successive and accelerated rounds of divisions, which are preferentially positioned asymmetrically at the tip of the cell by the Min system. Such division dynamics allows the rapid production of daughter cells with normal size, which DNA content depends on the progression of chromosome segregation prior to division. In most filaments, nucleoid segregation precedes tip-division, which produces nucleated daughter cells that resume normal growth. However, when segregation is deficient, tip-division occurs in the absence of DNA and produces anucleated cells. These findings uncover the mechanism by which bacterial filamentation allows efficient post-stress cell proliferation. One Sentence SummaryBacterial filaments recover by successive, frequent and Min-dependent asymmetric tip-divisions that rapidly produce multiple daughter cells with normal size and viability

microbiology↗

bioRxiv2019

Learning cognitive maps for vicarious evaluation

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. These maps are critical for flexible behavior as they permit us to navigate vicariously, but their underlying representation learning mechanisms are still unknown. To form these abstract maps, hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization, efficient planning, and handling of uncertainty. Here we introduce a specific higher-order graph structure - clone-structured cognitive graph (CSCG) - which forms different clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a novel probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety cognitive map phenomena such as discovering spatial relations from an aliased sensory stream, transitive inference between disjoint episodes of experiences, formation of transferable structural knowledge, and shortcut-finding in novel environments. By learning different clones for different contexts, CSCGs explain the emergence of splitter cells and route-specific encoding of place cells observed in maze navigation, and event-specific graded representations observed in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for a variety of place cell remapping phenomena. By lifting the aliased observations into a hidden space, CSCGs reveal latent modularity that is then used for hierarchical abstraction and planning. Altogether, learning and inference using a CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

neuroscience↗