Grid, place, and border cells in the mammalian hippocampus and entorhinal cortex perform highly sophisticated navigational tasks with an extremely low power budget. While previous algorithms for simultaneous localization and mapping (SLAM) in robotics have used these cells for inspiration, they have sacrificed the robust, low-power gains achieved with bioplausible models for ease of implementation. This paper presents steps towards robotic navigation with biologically realistic hippocampal models by implementing velocity-controlled oscillators, a basis for any spatially-tuned neuron, on mixed-mode neuromorphic spiking hardware.
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Reinforcement Learning and Place Cell Replay in Spatial Navigation
In the last decade, studies have demonstrated that hippocampal place cells influence rats’ navigational learning ability. Moreover, researchers have observed that place cell sequences associated with routes leading to a reward are reactivated during rest periods. This phenomenon is known as Hippocampal Replay, which is thought to aid navigational learning and memory consolidation. These findings in neuroscience have inspired new robot navigation models that emulate the learning process of mammals. This study presents a novel model that encodes path information using place cell connections formed during online navigation. Our model employs these connections to generate sequences of state-action pairs to train our actor-critic reinforcement learning model offline. Our results indicate that our method can accelerate the learning process of solving an open-world navigational task. Specifically, we demonstrate that our approach can learn optimal paths through open-field mazes with obstacles.
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- Award ID(s):
- 1703225
- PAR ID:
- 10518706
- Editor(s):
- Weitzenfeld, A
- Publisher / Repository:
- University of South Florida
- Date Published:
- Subject(s) / Keyword(s):
- Place Cells, Hippocampal Replay, Reinforcement. Learning, Actor-Critic, Latent Learning
- Format(s):
- Medium: X
- Location:
- Tampa, FL
- Sponsoring Org:
- National Science Foundation
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