Supervised learning

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

Biologically realistic mechanisms for supervised learning

When we learn to do something new, we are often provided with an explicit example of how we should perform. For example, when we learn to play an instrument, we may have a teacher who corrects our mistakes by showing us the proper way to play. This form of learning is referred to as supervised learning, and although it is probably important for many of the things we learn in life, scientists know almost nothing about how the brain actually does it. Interestingly, computer scientists have been able to develop algorithms for supervised learning that have been incredibly successful. These […]