Author(s)
Mohammad Reza Rezaei
Published 3 Projects
Bioinformatics Gaussian Mixture Model Mixture Model Mixture Merging Algorithm Real Time Filter
Ali Yousefi
Published 7 Projects
Neuroscience Bioinformatics Neurology Gaussian Mixture Model Neuromodulation
Content
Video Abstract (AI generated) (01:52) Paper PreprintRecent technological and experimental advances in recording from neural systems have led to a significant increase in the type and volume of data being collected in neuroscience experiments. This brings an increasing demand for development of appropriate analytical tools to analyze large scale neuroscience data. Simultaneously, advancement in deep neural networks (DNNs) and statistical modeling frameworks have provided new techniques for analysis of diverse forms of neuroscience data. DNNs like Long short-term memory (LSTM) or statistical modeling approaches like state-space point-process (SSPP) are widely used in the analysis of neural data including neural coding and inference analysis. Despite wide utilization of these techniques, there is a lack of comprehensive studies which systematically assess attributes of LSTM and SSPP approaches on a common neuroscience data analysis problem. As a result, this occasionally leads to inconsistent and divergent conclusions on the strength or weakness of either of the methodologies and also statistical significance of the analytical outcomes. In this research, we focus on providing a more systematic and multifaceted assessment of LSTM and SSPP techniques in a neural decoding problem.We examine different settings and modeling specifications to attain the optimal modeling solutions. We propose new LSTM network topologies and approximate filter solution to estimate a rat movement trajectory in a 2-D spaces using an ensemble of place cells' spiking activity. For each technique; we then study performance, computational efficiency, and generalizability of each technique in this decoding problem. By utilizing these results, we provided a succinct picture of the strength and weakness of each modeling approach and suggest who each of these techniques can be properly utilized in neural decoding problems. ### Competing Interest Statement The authors have declared no competing interest.
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Ali Yousefi. (2021, Nov 8).Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems[Video]. Scitok. https://scitok.com/project/p/f62df46f
Reza Rezaei Mohammad. "Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems" Scitok, uploaded by Yousefi Ali, 8 Nov, 2021, https://scitok.com/project/pf62df46f
Ali Yousefi. "Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems" Scitok. (Nov 8, 2021). https://scitok.com/project/p/f62df46f
Ali Yousefi (Nov 8, 2021). Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems Scitok. https://scitok.com/project/p/f62df46f
Ali Yousefi. Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems[video]. 2021 Nov 8. https://scitok.com/project/p/f62df46f
@online{al2006link, title={ Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems }, author={ Yousefi, Ali }, organization={Scitok}, month={ Nov }, day={ 8 }, year={ 2021 }, url = {https://scitok.com/project/p/f62df46f}, }