1 research outputs found
State-space analysis of an ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons
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.Comment: 10 pages, 4 figures, ICANN201