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Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods.
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Reproducing Fear Conditioning of Rats with Unmanned Ground Vehicles and Neuromorphic Systems
Deep learning accomplishes remarkable success through training with massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationship between concurrent events. This learning paradigm is referred to as associative memory. The successful implementation of associative memory potentially achieves self-learning schemes analogous to animals to resolve the challenges of deep learning. The state-of-the-art implementations of associative memory are limited to small-scale and offline paradigms. Thus, in this work, we implement associative memory learning with an Unmanned Ground Vehicle (UGV) and neuromorphic chips (Intel Loihi) for an online learning scenario. Our system reproduces the classic associative memory in rats. In specific, our system successfully reproduces the fear conditioning with no pretraining procedure and labeled datasets. In our experiments, the UGV serves as a substitute for the rats. Our UGV autonomously memorizes the cause-and-effect of the light stimulus and vibration stimulus, then exhibits a movement response. During associative memory learning, the synaptic weights are updated by Hebbian learning. The Intel Loihi chip is integrated with our online learning system for processing visual signals. Its average power usages for computing logic and memory are 30 mW and 29 mW, respectively.
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- Award ID(s):
- 2245712
- PAR ID:
- 10516637
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Symposium on Quality Electronic Design
- ISSN:
- 1948-3295
- ISBN:
- 979-8-3503-3475-3
- Page Range / eLocation ID:
- 1 to 7
- Subject(s) / Keyword(s):
- Associative Memory Hebbian Learning Neuromorphic Computing Unmanned Ground Vehicles.
- Format(s):
- Medium: X
- Location:
- San Francisco, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISQED57927.2023.10129317
