Identifying Neuronal Activity from Calcium Imaging Sequences
Calcium imaging is an increasingly popular technique for monitoring simultaneously the neuronal activity of hundreds of cells at single cell resolution. This makes an essential tool for studying spatio-temporal patterns of distributed activity that are crucial determinants of behavioral and cognitive functions such as perception, memory formation, motor activity, decision making and emotion. However, most approaches only focus on identifying positions of each cell (or parts of cells) by eye or semi-automated. Therefore, in this talk, we present two main approaches for detecting automatically neural activity. The first approach formulates the identification of neuronal activity of single cells and the neuronal co-activation into the same framework. That is, we propose a decomposition of the matrix of observations into a product of more than two sparse matrices, with the rank decreasing from lower to higher levels, driven by the semantic concepts of pixel -> neuron -> assembly in the neurosciences image sequence. In contrast to prior work, we allow for both hierarchical and heterarchical relations of lower-level to higher-level concepts.
Moreover, the proposed bilevel SHMF (sparse heterarchical matrix factorization) is the first formalism that allows to simultaneously interpret a calcium imaging sequence in terms of the constituent neurons, their membership in assemblies, and the time courses of both neurons and assemblies. The second approach describes a unified formulation and algorithm to find an extremely sparse representation for Calcium image sequences in terms of cell locations, cell shapes, spike timings and impulse responses. Solution of a single optimization problem yields cell segmentations and activity estimates that are on par with the state of the art, without the need for heuristic pre- or postprocessing."
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