Decoding the visual and subjective contents of the human brain. Decoding of temporal visual information from electrically evoked retinal ganglion cell activities in photoreceptor-degenerated retinas. Reconstruction of natural visual scenes from neural spikes with deep neural networks. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. Neural networks for efficient Bayesian decoding of natural images from retinal neurons. Decoding natural signals from the peripheral retina. In Proceedings of the 9th International IEEE/EMBS Conference on Neural Engineering, IEEE, San Francisco, USA, pp. Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex. Visual reconstruction from 2-photon calcium imaging suggests linear readout properties of neurons in mouse primary visual cortex. High-accuracy decoding of complex visual scenes from neuronal calcium responses. Reconstructing visual experiences from brain activity evoked by natural movies. Bayesian reconstruction of natural images from human brain activity. Spatio-temporal correlations and visual signalling in a complete neuronal population. Reconstruction of natural images from responses of primate retinal ganglion cells. Decoding visual information from a population of retinal ganglion cells. ![]() Toward the next generation of retinal neuroprosthesis: Visual computation with spikes. Nonlinear decoding of a complex movie from the mammalian retina. Natural images are reliably represented by sparse and variable populations of neurons in visual cortex. High accuracy decoding of dynamical motion from a large retinal population. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. How does the brain solve visual object recognition? Neuron, vol.73, no.3, pp.415–434, 2012. Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration. Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization. Representing the dynamics on high-dimensional data with non-redundant wavelets. Extracting information from neuronal populations: Information theory and decoding approaches. Humans integrate visual and haptic information in a statistically optimal fashion. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Vision plays a peculiar role in intelligence.
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