Peng Liu

“The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects ” MIT news

From an engineering perspective, we should understand why the brain needs recurrent architectures, when we need them, and how we can operationalize this procedure into our deep neural networks.

This work definitely has started the first fundamental step to reach our goals. However, as I mentioned, we still need to know more profound about this research, such as the precise procedures and how many neurons involved.

Object recognition in our brains is not working alone instead of links with high-level cognition, such as emotion and memory. They are much likely cooperating with the visual cortex in object recognition. Thus, to overcome the challenges of object recognition in artificial intelligence, we have quite a lot of work to do indeed.

Source: http://news.mit.edu/2019/improved-deep-neural-network-vision-systems-just-provide-feedback-loops-0429

Paper: Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior -Authors: Kar, K, Kubilius, J, Schmidt, K, Issa, EB, DiCarlo, JJ Nature Neuroscience

Original post from https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9

In recent years, “deep learning” AI models have often been touted as “working like the brain,” in that they are composed of artificial neurons mimicking those of biological brains. From the perspective of a neuroscientist, however, the differences between deep learning neurons and biological neurons are numerous and distinct. In this blog post we’ll start by describing a few key characteristics of biological neurons, and how they are simplified to obtain deep learning neurons. We’ll then speculate on how these differences impose limits on deep learning networks, and how the movement toward more realistic models of biological neurons might advance AI as we currently know it.

The details can be found from the original https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9