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Muenster, L.

Publications and source records attributed to Muenster, L..

2 recordsLinked to original sources

Tuft dendrites in frontal motor cortex enable flexible learning

Flexible learning relies on integrating sensory and contextual information to adjust behavioral output in different environments. The anterolateral motor cortex (ALM) is a frontal area critical for action selection in rodents. Here we show that inputs critical to decision-making converge on the apical tuft dendrites of L5b pyramidal neurons in ALM. We therefore investigated the role of these dendrites in a rule-switching paradigm. Activation of dendrite-inhibiting layer 1 interneurons impaired relearning, without affecting previously learned behavior. Remarkably, this inhibition profoundly suppressed calcium activity selectively in dendritic shafts but not spines while reducing burst firing. Moreover, excitatory synaptic inputs to tuft dendrites exhibited rule-dependent clustering. We conclude that active dendritic integration is a key computational component of flexible learning.

neuroscience↗

An ultrasensitive genetically encoded voltage indicator uncovers the electrical activity of non-excitable cells

Genetically encoded voltage indicators (GEVIs) are powerful, non-invasive tools for recording action potentials in excitable cells. However, most animal cell types are non-excitable, and yet variations in the membrane potential are biologically relevant in these cells as well. Resolving such small voltage signals demands GEVIs with exceptionally high sensitivity. In this study, we applied structure-guided engineering to the GEVI ASAP3 to generate rEstus, a sensor with optimized brightness, voltage sensitivity, and voltage range. rEstus is most sensitive in the resting voltage range of non-excitable cells, exhibits a 3.6-fold improvement in fast voltage spike detection, and allows for absolute voltage calibration at the single-cell level. Using rEstus, we resolved endogenous voltage fluctuations in several non-excitable cell types and demonstrate that correlation analysis of these optically recorded fluctuations provides an easy, non-invasive, real-time readout of electrical gap-junction coupling. Our work provides greatly enhanced tools and methods for the non-invasive study of electrical signaling in excitable and non-excitable cells.

biophysics↗