The risks of misrecognition are, in principle, unavoidable in existing image recognition technologies using artificial intelligence (AI). For example, AI is known to falsely recognize a picture that apparently looks like a panda to humans for something completely different when a small amount of noise is added. The recent years have witnessed the remarkable development of AI using deep learning. AI has been increasingly able to recognize objects at performance levels close to those of humans and is expected to be applied to a range of areas. However, such misrecognition errors pose as a fatal drawback that can affect human lives in fields such as autonomous driving and medical image analysis.

In this research, we aim to develop robust and flexible AI preventing misrecognition errors by reproducing in AI the information processing carried out by our brain. The elucidation of how animals, including humans, process visual information may prove useful for eliminating any risks of misrecognition by AI as described above. This means that if we can elucidate the information processing actually performed in the brain and reproduce it in AI, we may be able to develop robust and flexible AI preventing misrecognition, which is unrealizable by the current principle of AI.
To this end, this research aims to comprehensively measure the neural activity of cells in many regions of the cerebral cortex and reproduce the measured brain activity in AI to build such innovative AI.

Our research team has developed a technology to comprehensively measure neuronal activity in cells in multiple areas of the cerebral cortex. It is known that visual information processing in the brains of animals including humans is performed hierarchically in many areas of the cerebral cortex. To understand the actual mechanisms of the processing in the brain, it is important to investigate such mechanisms in specific regions and between different regions of the brain.
Our team has developed a unique “two-photon calcium imaging” technique for observing the activity of neuron cells in the brain of living animals using a special two-photon microscope. Using this method, we can observe the activity of tens of thousands of cells in an area of several millimeters square, even the activity in each cell.

In this research, we aim to further develop these technologies and measure the neural activity of cells in many areas of the cerebral cortex more comprehensively.

Our research team has also developed a technique for analyzing the activity of neuron cells in detail on computer by copying the activity of neuron cells in the visual cortex from various images onto deep neural networks.
Specific steps for analyzing the activity of neuron cells include: (1) Input the image shown to an animal into the deep neural network, and have the network learn the activity of neuron cells, taking the activity responding to the image as teaching signals; and (2) on a computer, generate an image where the output of the neural network has been maximized to estimate the response characteristics of the neuron cell responses copied to the neural network. Specifically, prepare a random noise image and update the brightness value of the image so that the output of the neural network for that image becomes larger. Repeat the updating process several times to maximize the output of the neural network. The finally updated image maximizes the activity of neuron cells copied to the neural network and is presumed to indicate the response characteristics of the neuron cells.
Our team has demonstrated that the technique can be applied to the analysis of neuron cells in the mouse primary visual cortex to explain the properties of various primary visual cortex. Applying the data obtained by measuring the neural activity of cells in many regions of the cerebral cortex using the technique, we further aim to comprehensively analyze the extraction, analysis, and integration of information between layers of the brain. Furthermore, by reproducing the activity of the neurons responsible for these visual information processing on artificial intelligence, and by copying the fluctuation of neural activity, including top-down and recursive connections, which have not been used in conventional artificial intelligence, onto artificial intelligence, we aim to construct a robust, flexible, and dynamic brain-type artificial intelligence that cannot be obtained with current artificial intelligence principles.

The AI equipped with human brain functions is the target of our research. It is completely different from present AI in principle and is expected to contribute to a very wide range of industries.
Since misrecognition, which has been unavoidable from the principle of existing AI, can be avoided, our innovative AI can be applied to, for instance, diagnostic imaging such as X-rays, CT, and MRI, and to histopathological diagnosis in the medical scene, thereby preventing possible diagnostic errors. Furthermore, its application to autonomous driving will be unlikely to cause fatal accidents resulting from misrecognition.
It is hoped that the results of our research will also one day enable AI to recognize emotions from human facial expressions. If this is the case, more person-focused services will be provided in the face-to-face area such as housework and nursing care.
In addition to the aforementioned examples, we believe that our research will open up new possibilities for AI that can be applied to more complex situations in the real world.