To guide action, animals anticipate what events will occur, and when they will occur, based on experience. How animals anticipate future events is an unsettled question. Although reinforcement learning is often used to model anticipation, it is resource-intensive outside of the simplest scenarios. In this dissertation, I show evidence of memory that is persistent and carries timing information, and specify an algorithm for how animals might anticipate the identity and timing of future events.
This dissertation consists of two studies. In the first study, I found that identity and timing of remembered odors are jointly represented in the same cells in the dentate gyrus and lateral entorhinal cortex. Further, odor memories persist well after new odors emerge. The study analyzed results from an experiment conducted by Woods et al. (2020) on mice passively exposed to separate odors for a period of 20 s per exposure. The results are consistent with a memory framework known as timing using inverse Laplace transform (TILT).
In the second study, I constructed a computational algorithm based on the TILT memory framework to anticipate the identity and timing of future events. The algorithm generates predictions based on memories of past events, and stored associations between cues and outcomes. The algorithm is resource-efficient even when the future depends on the indefinite past. The algorithm is scale-invariant and works well with chains of events.
Together, the studies support a novel computational mechanism which anticipates what events will occur, and when they will occur. The algorithm could be applied in machine learning in cases of long-range dependence on history. These studies predict that behavioral and neural responses of animals could depend on events well into the past. / 2024-03-13T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44022 |
| Date | 14 March 2022 |
| Creators | Goh, Wei Zhong |
| Contributors | Howard, Marc W |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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