Computing Motion with 3D Memristive Grid
This work addresses motion detection for navigation systems, presenting an incremental advancement by mimicking biological retinal processes with memristive devices.
The paper tackled the problem of computing relative motion for navigation by introducing a novel memristive thresholding scheme and a double-layered 3D memristive network to detect moving edges and transient responses, enabling estimation of speed and direction of moving objects.
Computing the relative motion of objects is an important navigation task that we routinely perform by relying on inherently unreliable biological cells in the retina. The non-linear and adaptive response of memristive devices make them excellent building blocks for realizing complex synaptic-like architectures that are common in the human retina. Here, we introduce a novel memristive thresholding scheme that facilitates the detection of moving edges. In addition, a double-layered 3-D memristive network is employed for modeling the motion computations that take place in both the Outer Plexiform Layer (OPL) and Inner Plexiform Layer (IPL) that enables the detection of on-center and off-center transient responses. Applying the transient detection results, it is shown that it is possible to generate an estimation of the speed and direction a moving object.