Bioinspired Robotic Vision with Online Learning Capability and Rotation‐Invariant Properties

Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software‐based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation‐invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed

On the area scalability of valence-change memristors for neuromorphic computing

The ability to vary the conductance of a valence-change memristor in a continuous manner makes it a prime choice as an artificial synapse in neuromorphic systems. Because synapses are the most numerous components in the brain, exceeding the neurons by several orders of magnitude, the scalability of artificial synapses is crucial to the development of large scale neuromorphic systems but is an issue which is seldom investigated. Leveraging on the conductive atomic force microscopy method, we found that the conductance switching of nanoscale memristors (∼25 nm2) is abrupt in a majority of the cases examined. This behavior is contrary to the analoglike conductance modulation or plasticity typically observed in larger area memristors. The result therefore implies that plasticity may be lost when the device dimension is scaled down. The contributing factor behind the plasticity behavior of a large-area memristor was investigated by current mapping, and may be ascribed to the disruption of the plurality of conductive filaments happening at different voltages, thus yielding an apparent continuous change in conductance with voltage. The loss of plasticity in scaled memristors may pose a serious constraint to the development of large scale neuromorphic systems.Published versio