The combined use of intrinsic optical imaging and electrophysiological recording has become an important method to reveal the fine-scale structure of orientation map in the primary visual cortex. However, it often needs many repetitions to obtain the mean activity as a result of the low signal-to-noise ratio of intrinsic optical imaging. To overcome this problem, we proposed a Bayesian method to obtain the highly accurate orientation map with less repetitions by fusing the intrinsic optical imaging and electrophysiological recording. We first used a Gaussian regression model to obtain the posterior distribution of the cortical orientation map with the intrinsic optical imaging data. And then we computed the conditional distribution of orientation map given the measurements from electrophysiological recording. The simulation results suggested that our method had significant improvement of performance compared with the classical methods and was very robust to noise.
Orientation selectivity of neurons in mammalian visual cortex is a unique physiological characteristic. In the primary visual cortex, neurons with the same orientation cluster together to form the orientation column of the visual cortex. The mechanism of orientation selectivity has been debated for a long time, and the discussion of the structure and function of orientation columns has also been a hot issue. Here we present two basic properties of the visual cortex response, orientation selectivity and surround suppression. Based on these two different characteristics of primary visual cortical neurons, an improved EN model is proposed. The model can well simulate the physiological structure of simple cells and take into account the suppression and facilitation of non-classical receptive fields.
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