Deep learning in the brain

January 31, 2017

Guerguiev J, Lillicrap TP, Richards BA, “Towards deep learning with segregated dendrites”

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a […]
June 24, 2015

Deep learning in the neocortex

Deep learning is an approach to artificial intelligence (AI) that loosely mimics how the brain works. By mimicking the operations of the brain, deep learning can rival, or even outperform, human beings in a number of functions, such as image recognition [1], motor control [2], and speech recognition [3]. Moreover, deep networks can develop representations that are a better match to recordings in the neocortex of humans or non-human primates than existing models in neuroscience [4,5]. This suggests that deep learning captures something important about how our own brains work. The key to “deep” learning (as opposed to “shallow” learning) […]