A bio-inspired electronic mechanism for unsupervised learning using structural plasticity

Learning in the human brain is mediated by different forms of plasticity. The circuit processing spatiotemporal inputs we demonstrate here highlights the mechanisms and role of structural plasticity. In many neuromorphic systems, synaptic plasticity is used for learning, memory and information processing. In these systems, neurons are fully or sparsely connected and synaptic connections between neurons become stronger or weaker during the learning process. However, in living organisms, structural plasticity plays a role in learning as well. To demonstrate the utility of structural plasticity, our example circuit autonomously learns spatiotemporal patterns with structural plasticity rather than synaptic plasticity, learning by long-term changes in neural pathways rather than by updating synaptic weights. Our neuromorphic system has a completely non Von-Neumann architecture; it does not have separate units for memory and processing. Instead, its memory and processing capability emerge from the way its neurons are connected. Finally, inputs do not have preconceived meaning and therefore the circuit learns to recognize repeated input patterns in an unsupervised mode. Incorporating both structural plasticity and synaptic plasticity into individual neurons is the ultimate goal of the BioRC project.