Biology subjects

Abrahamsson, T.

Publications and source records attributed to Abrahamsson, T..

3 recordsLinked to original sources

bioRxiv2024

Hebbian induction adds AMPA-labile signaling units to CA3-CA1 synapses in the developing hippocampus

In the 2nd postnatal week hippocampus, Hebbian-induced long-term potentiation (LTP) of AMPA receptor-mediated transmission in CA3-CA1 synapses is not a genuine potentiation. Instead, it is a de-depression (unsilencing) and temporary stabilization of postsynaptically AMPA-labile synapses silenced by a prior test pulse (0.03 - 0.2 Hz) stimulation. In addition to such an LTP, Hebbian induction at these synapses also results in a labile potentiation that becomes depotentiated by test pulse stimulation, thus appearing as an Hebbian-induced short- term potentiation (STP). Although the induction of this labile potentiation was blocked in the combined presence of N-methyl-D-aspartate (NMDA) and metabotropic glutamate (mGlu) receptor antagonists, the depotentiation was not affected by these drugs. The labile potentiation was not associated with a change in paired-pulse ratio and was, after a depotentiation, fully re-established by a 20 min interruption of test pulse stimulation. These properties are shared with the silencing of previously non-stimulated (naive) AMPA-labile synapses by such test pulse stimulation. However, the depotentiation following an Hebbian induction is not a re-silencing of naive AMPA labile synapses since there is no correlation between the magnitudes of depotentiation and preceding silencing of naive synapses. The present results suggest that Hebbian induction at these neonatal CA3-CA1 synapses, in addition to unsilencing and temporary stabilization of AMPA-labile transmission, creates a labile potentiation based on the insertion/activation of an additional AMPA-labile signaling unit to a pre-existing synapse.

neuroscience↗

bioRxiv2021

Developmental emergence of two-stage nonlinear synaptic integration in cerebellar interneurons

Synaptic transmission, connectivity, and dendritic morphology mature in parallel during brain development and are often disrupted in neurodevelopmental disorders. Yet how these changes influence the neuronal computations necessary for normal brain function are not well understood. To identify cellular mechanisms underlying the maturation of synaptic integration in interneurons, we combined patch-clamp recordings of excitatory inputs in cerebellar stellate cells (SCs), 3D-reconstruction of SC morphology with excitatory synapse location, and biophysical modeling. We found that, during development, synaptic strength was homogeneously reduced along the somato-dendritic axis, but that dendritic integration was always sublinear. However, dendritic branching increased without changes in synapse density, leading to a substantial gain in distal inputs. Thus, changes in synapse distribution, rather than dendrite cable properties, are the dominant mechanism underlying the maturation of neuronal computation. These mechanisms favor the emergence of a spatially compartmentalized two-stage integration model promoting location-dependent integration within dendritic subunits.

neuroscience↗

bioRxiv2017

ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and ex-terior, in which all pixels have maximally similar time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cells morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.\n\nSignificance statementTwo-photon calcium imaging enables the study of brain activity during learning and behaviour at single-cell resolution. To decode neuronal spiking activity from the data, algorithms are first required to detect the location of cells in the video. It is still common for scientists to perform this task manually, as the heterogeneity in cell shape and frequency of cellular overlap impede automatic segmentation algorithms. We developed a versatile algorithm based on a popular image segmentation approach (the Level Set Method) and demonstrated its capability to overcome these challenges. We include no assumptions on cell shape or stereotypical temporal activity. This lends our framework the flexibility to be applied to new datasets with minimal adjustment.

neuroscience↗