Knowledge Driven Matrix Factorization To Reconstruct Modular Gene Regulatory Network. ... were co... more Knowledge Driven Matrix Factorization To Reconstruct Modular Gene Regulatory Network. ... were compared to that of the Bayesian approach, eg using the gene ... In summary, we have developed a knowledge driven matrix factorization approach that can simultaneously identify ...
Identifying functional connectivity from simultaneously recorded spike trains is important in und... more Identifying functional connectivity from simultaneously recorded spike trains is important in understanding how the brain processes information and instructs the body to perform complex tasks. We investigate the applicability of dynamic Bayesian networks (DBN) to infer the structure of neural circuits from observed spike trains. A probabilistic point process model was used to assess the performance. Results confirm the utility of DBNs in inferring functional connectivity as well as directions of signal flow in cortical network models. Results also demonstrate that DBN outperforms the Granger causality when applied to populations with highly non-linear synaptic integration mechanisms.
Knowledge Driven Matrix Factorization To Reconstruct Modular Gene Regulatory Network. ... were co... more Knowledge Driven Matrix Factorization To Reconstruct Modular Gene Regulatory Network. ... were compared to that of the Bayesian approach, eg using the gene ... In summary, we have developed a knowledge driven matrix factorization approach that can simultaneously identify ...
Identifying functional connectivity from simultaneously recorded spike trains is important in und... more Identifying functional connectivity from simultaneously recorded spike trains is important in understanding how the brain processes information and instructs the body to perform complex tasks. We investigate the applicability of dynamic Bayesian networks (DBN) to infer the structure of neural circuits from observed spike trains. A probabilistic point process model was used to assess the performance. Results confirm the utility of DBNs in inferring functional connectivity as well as directions of signal flow in cortical network models. Results also demonstrate that DBN outperforms the Granger causality when applied to populations with highly non-linear synaptic integration mechanisms.
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Papers by Yang Zhou