# Event-based attention and tracking on neuromorphic hardware

## TL;DR

This paper introduces a neuromorphic system combining event-based vision and attention mechanisms for object tracking, utilizing spiking neural networks on Loihi hardware to improve efficiency and robustness in dynamic environments.

## Contribution

It presents a fully event-driven attention and tracking system implemented on neuromorphic hardware, integrating dynamic neural fields with spiking neural networks for the first time.

## Key findings

- System can sustain object tracking amidst distractors.
- Reduces event generation when objects slow or stop.
- Demonstrates effective neuromorphic attention mechanism.

## Abstract

We present a fully event-driven vision and processing system for selective attention and tracking, realized on a neuromorphic processor Loihi interfaced to an event-based Dynamic Vision Sensor DAVIS. The attention mechanism is realized as a recurrent spiking neural network that implements attractor-dynamics of dynamic neural fields. We demonstrate capability of the system to create sustained activation that supports object tracking when distractors are present or when the object slows down or stops, reducing the number of generated events.