Learning Cognitive Maps in Neuromorphic Hardware

In this work group, we will combine a dynamic neural fields-based architecture for one-shot recognition of places (objects) with a grid/place-cells inspired architecture for representing space and an architecture for learning sequences to arrive at a map-building system. We will aim for implementing the neural architecture in neuromorphic hardware (mixed signal analog/digital devices ROLLS and CXQUAD) and testing its parts on small-size mobile vehicles (PushBot, Omnibot, and Khepera IV). Particular motivated tinkerers can also dare to play with a version of the architecture for a map-building neuromorphic UAV (quadcopter).

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Learning a Cognitive Map in Neuromorphic hardware

Building a representation of an unknown environment is a key capability for autonomous robots to be able to plan and generate goal-directed actions in real-world tasks. Such representation requires both recognition of objects or places and solving the SLAM (simultaneous localisation and mapping) problem in order to construct a correct map of the environment, which can be used for planning future actions.

Neuromorphic computing is well-suited both for the recognition task (realising feed-forward networks, but also ‘cognitive’ processes, such as attention and memory formation using recurrent networks, e.g., a winner-take-all architecture) and for the map-formation task (implementing a well-studies biological neural system for localisation and navigation — grid and place cells). In this project, we will aim to implement both object recognition and map formation using mixed-signal analog/digital neuromorphic hardware (ROLLS, CXQUAD) and a miniature computer with a parallel co-processor “parallella”. We will use small vehicles “Pushbot” and “Omnibot”  equipped with a neuromorphic camera DVS as the robotic platform to develop and test the neural architecture in real-world settings. 

Subprojects:

1. Grid and place cell system in hardware
2.  Robot navigation: obstacle avoidance and target acquisition
3. “Shallow” place-recognition in hardware
4. Sequence learning 
5. Relational representations

6. Bonus project: Neuromorhic controller for a UAV

 

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