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Dictus, H. T.

Publications and source records attributed to Dictus, H. T..

2 recordsLinked to original sources

Do we understand orientation selectivity? A simulation study

Orientation selectivity in the visual cortex has been extensively studied since it was disovered in 1959. In this article we demonstrate that current mechanistic explanations of orientation selectivity in the visual cortex, which appear to work in simplified models, do not suffice when embedded in a more realistic network. Up to now, orientation selectivity has only been reproduced in models with distance-dependent connectivity and limited cellular diversity. We therefore constructed a mouse primary visual cortex model with, among other refinements, realistic neuronal diversity and morphologically constrained connectivity. We implemented proposed mechanisms of orientation selectivity - specifically the organization of thalamocortical input into elliptical subfields and peferential connectivity between neurons with similar receptive fields - within this model. In the process we explored limitations of pure anatomy in predicting the connectivity and function of the cortex. Simulations of our model did not reproduce realistic levels of orientation selectivy. This indicates that these mechanisms, despite working well in simpler models, are not sufficient to explain orientation selectivity in real brain networks. On the basis of our results, we suggest the hypothesis that homeostatic plasticity is necessary to reconcile orientation selectivity with realistic connectivity.

neuroscience↗

Computational synthesis of cortical dendritic morphologies

Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles and investigating structural alterations associated with diseased brain states. Recently, we introduced a topological descriptor that reliably categorizes dendritic morphologies. We apply this descriptor to digitally synthesize dendrites to address the challenge of insufficient biological reconstructions. The synthesized cortical dendrites are statistically indistinguishable from the corresponding reconstructed dendrites in terms of morpho-electrical properties and connectivity. This topology-guided synthesis enables the rapid digital reconstruction of entire brain regions from relatively few reference cells, thereby allowing the investigation of links between neuronal morphologies and brain function across different spatio-temporal scales. We synthesized cortical networks based on structural alterations of dendrites associated with medical conditions and revealed principles linking branching properties to the structure of large-scale networks. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/040410v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@1ae790org.highwire.dtl.DTLVardef@58c3eborg.highwire.dtl.DTLVardef@1a6a9b1org.highwire.dtl.DTLVardef@ceae91_HPS_FORMAT_FIGEXP M_FIG Graphical abstractA topological model of neuronal shapes is used to investigate the link between the branching patterns of dendritic morphologies and the connectivity of the neuronal networks they form. Starting from reconstructed cells (in black) of cortical dendrites, we extract the topological barcode that is used to create a statistically similar synthesized pyramidal cell (in red), and respectively a group of pyramidal cells of the same morphological type. From reconstructed cells examples of all layers and morphological types we generate synthesized dendrites and build a synthesized cortical column (colors corresponds to cortical layers). The synthesized dedrites are statistically similar to the reconstructed dendrites in terms of morpho-electrical properties and the connectome of the synthesized column (colored connectome) is almost indistinguishable from the connectome of the reconstructed column (greyscale). C_FIG

neuroscience↗