Advancements in neural imaging now allow simultaneous acquisition of multiple imaging modalities. Whenthese multimodal data are combined with advanced signal processing algorithms, they can provide a better understandingof brain dynamics, which was otherwise not possible. In this paper, we specifically demonstrate a newtechnique for the "fusion" of neural activity recorded with two-photon calcium imaging and Electrocorticography(ECoG) recordings acquired using an electrode array. Calcium signals are usually acquired at a low samplingfrequency and have a high spatial resolution, but suer in temporal resolution due to blurring of the spikingsignal. ECoG, on the other hand, has high temporal resolution and is acquired at high sampling frequency, butcan only detect aggregated neural population activity and suers from poor spatial resolution. In this paper,we will develop novel signal processing techniques using bilinear fusion and sparsity-aware reconstruction toovercome these drawbacks. The data from both modalities are represented by a bilinear model which is invertedto infer the spiking activity using suitable prior assumptions such as sparsity or independence of the sources.Our approach leverages on the complementary strengths of the two modalities (in terms of temporal and spatialresolution) along with use of powerful non-convex algorithms that harness the unique structure of the dataset.
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