Sound source localization is used in various applications such as industrial noise-control, speech detection in mobile phones, speech enhancement in hearing aids and many more. Newest video conferencing setups use sound source localization. The position of a speaker is detected from the difference in the audio waves received by a microphone array. After detection the camera focuses onto the location of the speaker. The human brain is also able to detect the location of a speaker from auditory signals. It uses, among other cues, the difference in amplitude and arrival time of the sound wave at the two ears, called interaural level and time difference. However, the substrate and computational primitives of our brain are different from classical digital computing. Due to its low power consumption of around 20 Watts and its performance in  real time the human brain has become a great source of inspiration for emerging technologies. One of these technologies is neuromorphic hardware which implements the fundamental principles of brain computing identified until today using \ac{CMOS} technologies and new devices. In this work we propose the first neuromorphic closed-loop robotic system that uses the interaural time difference for sound source localization in real time. Our system can successfully locate sound sources such as human speech. In a closed-loop experiment, the binaural robotic platform turned immediately into the direction of the sound source with a turning velocity linearly proportional to the angle difference between sound source and pan-tilt unit. After this initial turn, the robotic platform remains at the direction of the sound source. Even though the system only uses very few resources of the available hardware and was only tuned by hand it already reaches performances comparable to other neuromorphic approaches. The sound source localization system presented in this article brings us one step closer towards neuromorphic event-based systems for robotics and embodied computing.

Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss, thanks to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, event-based signals may contain temporal information which is complex to extract when using rate coding. In this work, we present a novel digital implementation of the model, called Time Difference Encoder, for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks which require the encoding of the time difference between events, such as optical flow based obstacle avoidance, sound source localization and gas source localization. This proposed bio-inspired model offers an alternative to the Jeffress model for the Interaural Time Difference estimation, validated with a sound source lateralization proof-of-concept. The model has been simulated and implemented on an FPGA, requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.