An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot.

In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. The neuromorphic vision system of the iCub is used to correct for drift in the pose estimation. Positions of objects in front of the robot are memorized using on-chip synaptic plasticity. We present real-time robotic experiments using 2 degrees of freedom (DoF) of the robot's head and show precise path integration, visual reset, and object position learning on-chip. We discuss the requirements for integrating the robotic system and neuromorphic hardware with current technologies.

High Precision of Spike Timing across Olfactory Receptor Neurons Allows Rapid Odor Coding in Drosophila.

In recent years, it has become evident that olfaction is a fast sense, and millisecond short differences in stimulus onsets are used by animals to analyze their olfactory environment. In contrast, olfactory receptor neurons are thought to be relatively slow and temporally imprecise. These observations have led to a conundrum: how, then, can an animal resolve fast stimulus dynamics and smell with high temporal acuity? Using parallel recordings from olfactory receptor neurons in Drosophila, we found hitherto unknown fast and temporally precise odorant-evoked spike responses, with first spike latencies (relative to odorant arrival) down to 3 ms and with a SD below 1 ms. These data provide new upper bounds for the speed of olfactory processing and suggest that the insect olfactory system could use the precise spike timing for olfactory coding and computation, which can explain insects' rapid processing of temporal stimuli when encountering turbulent odor plumes.