Learning spike time codes through supervised and unsupervised structural plasticity - Roy, Subhrajit (2016)
Large-scale spiking neural networks (SNN) are typically implemented on the chip by using mixed analog-digital circuits. While the models of the network components (neurons, synapses, and dendrites) are implemented by analog VLSI techniques, the connectivity information of the network is stored in an on-chip digital memory. Since the connectivity information is virtual, the user has full flexibility in reconfiguring the network. When a new task is encountered, a software model of the network is trained in computer and the trained weights are downloaded to the digital memory. Hence, the analog part works in conjunction with the digital part to form a properly weighted SNN suitable for the task at hand. However, very few hardware systems emulating SNN have been reported to solve real-world pattern recognition tasks with performance comparable to their software counterparts. A major challenge in obtaining a classification accuracy comparable to a software implementation of a system on a computer is that statistical variations in VLSI devices diminish the accuracy of synaptic weights. Most of the current neuromorphic systems require high-resolution synaptic weights and hence are affected by this problem. In this thesis, for enhanced stability to mismatch and efficient hardware implementation we have considered neurons with binary synapses for recognition of spatiotemporal spike trains. To compensate for the reduced computational power provided by binary synapses we look into more bio-physical models of neurons. Inspired by the nonlinear properties of dendrites in biological neurons, the proposed networks incorporate neurons having multiple dendrites with a lumped nonlinearity (two compartment model). We have shown that such a neuron with nonlinear dendrites (NNLD) has higher memory capacity than their linear counterpart and networks employing them provide state-of-the-art performance. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or morphology of the network. Hence, the learning involves network rewiring (NRW) of the spiking neural network similar to structural plasticity observed in its biological counterparts.