Takashi Yoshida and Kenichi Ohki : Natural images are reliably represented by sparse and variable populations of neurons in visual cortex., Nature Communications, 11, 1-19, 2020.
2.
Jumpei Ukita, Takashi Yoshida and Kenichi Ohki : Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network., Scientific Reports, 9, 1, 3791, 2019.
(Summary)
A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.
Ayako Hayashi, Takashi Yoshida and Kenichi Ohki : Cell type specific representation of vibro-tactile stimuli in the mouse primary somatosensory cortex, Frontiers in Neural Circuits, 12, 109, 2018.
(Summary)
Although the processing of whisker deflections in the barrel area of the rodent primary somatosensory cortex (S1) has been studied extensively, how cutaneous vibro-tactile stimuli are processed in the rodent S1 outside the barrel area has not been fully examined. Particularly, the cell-type specific representation of multiple vibration frequencies in genetically identified inhibitory cells in the S1 has not been examined. Using two-photon calcium imaging, we examined the responses to vibration stimuli of excitatory and inhibitory neurons in the S1 hind limb area of male and female mice. The excitatory cells showed relatively sharp selectivity to vibration stimuli, whereas the inhibitory cells exhibited less selectivity. The excitatory and inhibitory cells with different preferred stimuli were intermingled in a "salt and pepper" manner. Furthermore, the noise correlation tended to be especially strong in excitatory-inhibitory and inhibitory-inhibitory cell pairs that have similar stimulus selectivity. These results suggest that excitatory cells tend to represent specific stimulus information and work together with similarly tuned inhibitory cells as a functionally connected network.