Vision plays a peculiar role in intelligence.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 becom...Vision plays a peculiar role in intelligence.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.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.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.展开更多
基金supported by National Natural Science Foundation of China(Nos.62176003 and 62088102)the Royal Society Newton Advanced Fellowship of the UK(No.NAF-R1-191082)。
文摘Vision plays a peculiar role in intelligence.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.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.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.
