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Primate Inspires Computer To See Image

on Tuesday, 19 September 2017. Posted in Artificial Intelligence

Up to this point, no computer model has held the capacity to match the primate brain at recognizing objects amid a short look. Since year, researchers have tried to build a system that mimics the brain's ability to see images, recognize speech and understand it.  However, the neuroscientist have come up with the latest study on deep neural network that can mimic the primate brain. The model shows the best understanding of the researchers on how the brain recognizes the object.

 Working of the model

Scientists got inspired from the representation of visual information hierarchy in the brain. The image information passes from the retina towards the primary visual cortex and then to the  inferior temporal cortex. The image is processed at each stage until it is recognized by the brain. The neural network designers have created numerous layers for computing in the model, to mimic the hierarchy. The layer performs the linear dot product mathematical operation. The object location, which is not needed, is cast aside. The representation of the object becomes complex at each layer.

For the study, researchers initially measured the object recognition capacity of the brain. The scientists have embedded series of electrodes in the inferior temporal cortex and a part that feeds visual in the inferior temporal cortex. This helps in getting the number of neurons reacting to a particular image by the animal. The  scientist the compared the result of the experiment with the representation created by the deep neural networks. The representations were in the form of a matrix generated by the total computational units in the system. Each image used, created a different series of the numbers and the model accuracy is determined by whether it can gather objects into into comparative groups.


The study has taken a leap in the computer processing capacity. Researchers are using small chips intended for elite in handling the gigantic measure of visual content required in video games. The large dataset is available to feed the system' algorithm. Before the study was conducted, the neural network was unable to identify the images. Now, due to the availability of numerous images, the system can mold calculations accordingly to become more accurate in recognition of the object. 

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