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Ohki, K.

Publications and source records attributed to Ohki, K..

4 recordsLinked to original sources

Astrocytes in the mouse visual cortex reliably respond to visual stimulation

Astrocytes are known to contact with a great number of synapses and may integrate sensory inputs. In the ferret primary visual cortex, astrocytes respond to a visual stimulus with a delay of several seconds with respect to the surrounding neurons. However, in the mouse visual cortex, it remains unclear whether astrocytes respond to visual stimulations. In this study, using dual-color simultaneous in vivo two-photon Ca2+ imaging of neurons and astrocytes in the awake mouse visual cortex, we examined the visual responsiveness of astrocytes and their precise response timing relative to the surrounding neurons. Neurons reliably responded to visual stimulations, whereas astrocytes often showed neuromodulator-mediated global activities, which largely masked small periodic activities. Administration of the selective 1-adrenergic receptor antagonist prazosin substantially reduced such global astrocytic activities without affecting the neuronal visual responses. In the presence of prazosin, astrocytes showed weak but consistent visual responses mostly at their somata. Cross-correlation analysis estimated that the astrocytic visual responses were delayed by approximately 5 s relative to the surrounding neuronal responses. In conclusion, our research demonstrated that astrocytes in the primary visual cortex of awake mice responded to visual stimuli with a delay of several seconds relative to the surrounding neurons, which may indicate the existence of a common mechanism of neuron-astrocyte communication across species.\n\nHighlightsO_LIWe performed dual-color in vivo two-photon Ca2+ imaging of neurons and astrocytes.\nC_LIO_LI1-adrenoblocker prazosin substantially reduced global astrocytic activities.\nC_LIO_LIAstrocytes showed weak but reliable visual responses in the awake mouse visual cortex.\nC_LIO_LIAstrocytic visual responses were delayed by 5 s relative to the neuronal ones.\nC_LI

neuroscience

Characterization of nonlinear receptive fields of visual neurons by convolutional neural network

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 analyzing the nonlinearity of neuronal responses. In computer vision, artificial neural networks, especially convolutional neural networks (CNNs), have demonstrated state-of-the-art performance in image recognition by capturing the higher-order statistics of natural images. Here, we incorporated CNN for encoding models of neurons in the visual cortex to develop a new method of nonlinear response characterization, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses of neurons to natural images, we synthesized the RF image such that the image would predictively evoke a maximum response (\"maximization-of-activation\" method). We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity, revealing that CNN could be used to estimate the nonlinear RF of simulated cells. In particular, we could visualize various types of nonlinearity underlying the responses, such as shift-invariant RFs or rotation-invariant RFs. These results suggest that the method may be applicable to neurons with complex nonlinearities, such as rotation-invariant neurons in higher visual areas. Next, we applied the method to a dataset of neurons in the mouse primary visual cortex (V1) whose responses to natural images were recorded via two-photon Ca2+ imaging. We could visualize shift-invariant RFs with Gabor-like shapes for some V1 neurons. By quantifying the degree of shift-invariance, each V1 neuron was classified as either a shift-variant (simple) cell or shift-invariant (complex-like) cell, and these two types of neurons were not clustered in cortical space. These results suggest that the novel CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.

neuroscience

Representation of natural image contents by sparsely active neurons in visual cortex

Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons robustly represent natural images and how the information is optimally decoded from the representation have not been revealed. We reconstructed natural images from V1 activity in anaesthetized and awake mice. A single natural image was linearly decodable from a surprisingly small number of highly responsive neurons, and an additional use of remaining neurons even degraded the decoding. This representation was achieved by diverse receptive fields (RFs) of the small number of highly responsive neurons. Furthermore, these neurons reliably represented the image across trials, regardless of trial-to-trial response variability. The reliable representation was supported by multiple neurons with overlapping RFs. Based on our results, the diverse, partially overlapping RFs ensure sparse and reliable representation. We propose a new representation scheme in which information is reliably represented while the representing neuronal patterns change across trials and that collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons

neuroscience

Neuronal origin of the temporal dynamics of spontaneous BOLD activity correlation

Resting-state functional connectivity (FC) has become a major fMRI method to study network organization of human brains. There has been recent interest in the temporal fluctuations of FC calculated using short time windows (\"dynamic FC\") because this method could provide information inaccessible with conventional \"static\" FC, which is typically calculated using the entire scan lasting several tens of minutes. Although multiple studies have revealed considerable temporal fluctuations in FC, it is still unclear whether the fluctuations of FC measured in hemodynamics reflect the dynamics of underlying neural activity. We addressed this question using simultaneous imaging of neuronal calcium and hemodynamic signals in mice and found coordinated temporal dynamics of calcium FC and hemodynamic FC measured in the same short time windows. Moreover, we found that variation in transient neuronal coactivation patterns (CAPs) was significantly related to temporal fluctuations of sliding window FC in hemodynamics. Finally, we show that the observed dynamics of FC cannot be fully accounted for by simulated data assuming stationary FC. These results provide evidence for the neuronal origin of dynamic FC and further suggest that information relevant to FC is condensed in temporally sparse events that can be extracted using a small number of time points.

neuroscience