Echoes are a major part of our daily auditory lives, yet very few of us are even aware that we use them constantly to understand the space around us. Echolocating bats have made these echoes their primary sense (i.e. sonar), demonstrating agile flight in complex forest environments and successful insect capture in flight; all in darkness. Because the brain represents a computational engine that far surpasses any single man-made computer in both capabilities and power consumption, we are interested to understand the principles of neural computation.
For a number of years we have been constructing custom analog integrated circuits that mimic the basic neural processing of bat echolocation in an effort to demonstrate these principles on a small flying vehicle, requiring real-time performance, miniaturization, and power efficiency.
The 'neuromorphic' very-large-scale-integration (VLSI) approach (which implements neural computations by mimicking its structural morphology), offers advantages in size and power, but often lacks needed precision and flexibility. This has been one of the main concerns about this research field's long-term viability. In this proposal we pursue three main goals: 1) to expand the use of learning and adaptation techniques at the circuit level to increase computational precision and accuracy at the system-level, 2) to explore the use of 'virtual' wiring to mimic large-scale wiring changes akin to neural development, and 3) to adapt existing neural models of spatial attention for use in sampled sensory systems (e.g., sonar) to guide this learning process.
Through this research and our educational plan we hope to raise awareness to the rich acoustic cues around us and how we as humans and the machines we build can learn to make use of them.
