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- W2949905462 abstract "In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation algorithm. Furthermore, it allows us to trace dynamics of macroscopic network properties such as entropy, sparseness, and fluctuation. Here we report that, in all examined stimulus conditions, pairwise interactions contribute to increasing sparseness and fluctuation. We then demonstrate that the orientation of the grating stimulus is in part encoded in the pairwise interactions of the neural populations. These results demonstrate the utility of the state-space Ising model in assessing contributions of neural interactions during stimulus processing." @default.
- W2949905462 created "2019-06-27" @default.
- W2949905462 creator A5071284440 @default.
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- W2949905462 date "2018-07-24" @default.
- W2949905462 modified "2023-10-16" @default.
- W2949905462 title "State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons" @default.
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- W2949905462 doi "https://doi.org/10.48550/arxiv.1807.08900" @default.
- W2949905462 hasPublicationYear "2018" @default.
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