Dedieu, A. | Search bioRxiv

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↗