The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

The design of robots that interact autonomously with the environment and exhibit complex
behaviours is an open challenge that can benefit from understanding what makes living …

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Roboticists have long drawn inspiration from nature to develop navigation and simultaneous
localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and …

Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots
exploring unknown environments. The mammalian brain solves SLAM via a network of …

Brain-inspired navigation technologies combine environmental perception, spatial cognition,
and target navigation to create a comprehensive navigation research system. Researchers …

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …

To navigate in new environments, an animal must be able to keep track of its position while
simultaneously creating and updating an internal map of features in the environment, a …

The dynamic membrane potential threshold, as one of the essential properties of a
biological neuron, is a spontaneous regulation mechanism that maintains neuronal …

Spiking neural networks (SNNs) offer both compelling potential advantages, including
energy efficiency and low latencies and challenges including the non-differentiable nature of …