Taking inspiration from biology to solve engineering problems using the organizing\nprinciples of biological neural computation is the aim of the field of neuromorphic engineering.\nThis field has demonstrated success in sensor based applications (vision and audition) as well as in\ncognition and actuators. This paper is focused on mimicking the approaching detection functionality\nof the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to\nrobotics. These RGCs transmit action potentials when an expanding object is detected. In this work\nwe compare the software and hardware logic FPGA implementations of this approaching function\nand the hardware latency when applied to robots, as an attention/reaction mechanism. The visual\ninput for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads\nto an end-to-end event based processing system. The software model has been developed in Java,\nand computed with an average processing time per event of 370 ns on a NUC embedded computer.\nThe output firing rate for an approaching object depends on the cell parameters that represent the\nneeded number of input events to reach the firing threshold. For the hardware implementation, on a\nSpartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz.\nThe entropy has been calculated to demonstrate that the system is not totally deterministic in response\nto approaching objects because of several bioinspired characteristics. It has been measured that a\nSummit XL mobile robot can react to an approaching object in 90 ms, which can be used as an\nattentional mechanism. This is faster than similar event-based approaches in robotics and equivalent\nto human reaction latencies to visual stimulus.
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