Deep learning in the brain

January 31, 2017

Guerguiev J, Lillicrap TP, Richards BA, “Deep learning with segregated dendrites”, arXiv:1610.00161

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 deep learning can be achieved by moving away from point neuron models and towards multi-compartment neurons. Like neocortical pyramidal neurons, neurons in our model receive feedforward sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, the network can calculate local synaptic weight updates that allow it to categorize images from the MNIST data-set with good accuracy. We show that our learning […]
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) […]