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RISS 인기검색어
- 검색키워드
- 저자 : Schieber, Marc H. (검색결과 4 건)
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Desai, Priyanka S.,Kang, Beom-Goo,Katzarova, Maria,Hall, Ryan,Huang, Qifan,Lee, Sanghoon,Shivokhin, Maksim,Chang, Taihyun,Venerus, David C.,Mays, Jimmy,Schieber, Jay D.,Larson, Ronald G. American Chemical Society 2016 Macromolecules Vol.49 No.13
<P>We compare predictions of two of the most advanced versions of the tube model, namely the 'Hierarchical model' by Wang et al. [J. Rheol. 2010, 54, 223] and the BoB (branch-on-branch) model by Das et al. [J. Rheol. 2006, SO, 207], against linear viscoelastic G' and G '' data of binary blends of nearly monodisperse 1,4-polybutadiene 4-arm star polymer of arm molar mass 24 000 g/mol with a monodisperse linear 1,4-polybutadiene of molar mass 58 000 g/mol. The star was carefully synthesized and characterized by temperature gradient interaction chromatography and by linear rheology over a wide frequency region through time temperature superposition. We found large failures of both the Hierarchical and BoB models to predict the terminal relaxation behavior of the star/linear blends, despite their success in predicting the rheology of the pure star and pure linear polymers. This failure occurred regardless of the choices made concerning constraint release, such as assuming arm retraction in 'fat' or 'skinny' tubes. Allowing for 'disentanglement relaxation' to cut off the constraint release Rouse process at long times does lead to improved predictions for our blends, but leads to much worse predictions for other star/linear blends described in the literature, especially those of Shivokhin et al. [Macromolecules 2014, 47, 2451]. In addition, our blends and those of Shivokhin et al. were also tested against a coarse-grained slip-link model, the 'clustered fixed slip-link model (CFSM)' of Schieber and co-workers [J. Rheol. 2014, 58, 723], in which several Kuhn steps are clustered together for computational efficiency. The CFSM with only two molecular-weight- and chain-architecture-independent parameters was able to give very good agreement with all experimental data for both of these sets of blends. In light of its success, the CFSM slip-link model may be used to address the constraint release issue more rigorously and potentially help develop improved tube models.</P>
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Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding
Shin, Hyun-Chool,Aggarwal, Vikram,Acharya, Soumyadipta,Schieber, Marc H.,Thakor $^$, Nitish V. IEEE 2010 IEEE Transactions on Biomedical Engineering Vol.57 No.3
<P>We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum <I>a posteriori</I> (MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x)| finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x|m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist (12 movements). With as few as 20-25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.</P>
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Yong-Hee Kim,Thakor, Nitish V.,Schieber, Marc H.,Hyoung-Nam Kim IEEE 2015 IEEE transactions on neural systems and rehabilita Vol.23 No.3
<P>Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.</P>
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Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements
Choi, Hwayoung,You, Kyung-Jin,Thakor, Nitish V.,Schieber, Marc H.,Shin, Hyun-Chool IEEE 2018 IEEE transactions on neural systems and rehabilita Vol.26 No.12
<P>In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson’s correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.</P>
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