Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks

We present a novel learning rule for learning transformations of sophisticated neural representations in a biologically plausible manner. We show that the rule, which uses only information available locally to a synapse in a spiking network, can learn to transmit and bind semantic pointers. Semantic pointers have previously been used to build Spaun, which is currently the world’s largest functional brain model (Eliasmith et al., 2012). Two operations commonly performed by Spaun are semantic pointer binding and transmission. It has not yet been shown how the binding and transmission operations can be learned. The learning rule combines a previously proposed supervised learning rule and a novel spiking form of the BCM unsupervised learning rule. We show that spiking BCM increases sparsity of connection weights at the cost of increased signal transmission error. We also demonstrate that the combined learning rule can learn transformations as well as the supervised rule and the offline optimization used previously. We also demonstrate that the combined learning rule is more robust to changes in parameters and leads to better outcomes in higher dimensional spaces, which is critical for explaining cognitive performance on diverse tasks.