Sin, Duho Kim, Jinsoo Choi, Jee Hyun Kim, Sung-Phil 2024-01-20T09:32:27Z 2024-01-20T09:32:27Z 2021-09-04 2014-07 1110-757X https://pubs.kist.re.kr/handle/201004/126664 As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks. English Hindawi Publishing Corporation Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model Article 10.1155/2014/218373 1 Journal of Applied Mathematics, v.2014 Journal of Applied Mathematics 2014 N scie scopus 000339179500001 2-s2.0-84904650225 Mathematics, Applied Mathematics, Interdisciplinary Applications Mathematics Article NEURAL-NETWORKS ORIENTATION INFORMATION