Topic: spectral clustering

The classical sparse coding (SC) model represents visual stimuli as a convex combination of a handful of learned basis functions that are Gabor-like when trained on natural image data. However, the Gabor-like filters learned by classical sparse coding far overpredict well-tuned simple cell receptive field profiles observed empirically. While neurons fire sparsely, neuronal populations are also organized in physical space by their sensitivity to certain features. In V1, this organization is a smooth progression of orientations along the cortical sheet. A number of subsequent models have either

Characterizing the network properties of animal brains may lead to a better understanding of computation and information flow in these complex organs. However, to date very few whole-brain neuron-level reconstructions have been completed across organisms. The collaborative Flywire project1-2 has completed the proofreading of a connectome for a Drosophila female brain which contains both complete hemispheres of the central brain and includes neurons that receive inputs in the optic lobes. Here, we dissect the network properties of the complete central brain of the fruit fly. We characterized th

Cognitive flexibility involves adapting behaviour to changing rules or contexts by modifying input-output mappings of brain-wide networks to achieve context-appropriate behaviour. This process relies on distinct neural representations of different rules or contexts in multiple brain regions, as well as changes in functional connectivity across brain regions. While context-dependent changes in neural responses have been studied locally in various brain regions, it is not clear to what extent different contexts lead to changes in functional connectivity between brain regions. We imaged widefie