Animals combine various sensory cues with previously acquired knowledge to safely travel towards a target destination. In close analogy to biological systems, we propose a neuromorphic system which decides, based on auditory and visual input, how to reach a sound source without collisions. The development of this sensory integration system, which identifies the shortest possible path, is a key achievement towards autonomous robotics. The proposed neuromorphic system comprises two event based sensors (the eDVS for vision and the NAS for audition) and the SpiNNaker processor. Open loop experiments were performed to evaluate the system performances. In the presence of acoustic stimulation alone, the heading direction points to the direction of the sound source with a Pearson correlation coefficient of 0.89. When visual input is introduced into the network the heading direction always points at the direction of null optical flow closest to the sound source. Hence, the sensory integration network is able to find the shortest path to the sound source while avoiding obstacles. This work shows that a simple, task dependent mapping of sensory information can lead to highly complex and robust decisions.

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input. In this work we propose a novel integration between an eccentric down-sampling of the visual field, taking inspiration from the varying size of receptive fields (RFs) in the mammalian retina, and the Spiking Elementary Motion Detector (sEMD) model. We characterize the system functionality with simulated data and real world data collected with bio-inspired event driven cameras, successfully implementing motion detection along the four cardinal directions and diagonally.

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.

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 due 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, the temporal information of event-based signals can be difficult to extract when using rate coding. In this work, we present a novel digital implementation of the model, called time difference encoder (TDE), 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 that require the encoding of the time difference between events, such as optical Manuscript