The overarching goal of this research is to understand neural learning rules: What are the circumstances that give rise to strengthening and weakening of memories? Our specific goal is to evaluate the hypothesis that learning is competition-dependent. According to this hypothesis, whenever neural representations compete to be active, the winning representations are strengthened, the losing representations are weakened, and learning is modulated by the margin of victory (i.e., closer competition yields more learning). The present research builds on previously funded work where biologically-based computational models were used to explore the neural mechanisms of competition- dependent learning. These simulations demonstrated that competition-dependent learning could be implemented in the brain by leveraging oscillations in the strength of neural inhibition;they also demonstrated that competition- dependent learning could account for detailed patterns of human memory data. The first specific aim of this research is to refine our computational model of competition-dependent learning. In particular, the simulations will focus on paradigms where there is close competition between the sought-after memory and other memories. In situations of this sort, the competition-dependent learning hypothesis predicts that learning effects will be highly volatile - small differences in memory excitation can affect the outcome of the competition, which (in turn) will affect which memories are strengthened and which are weakened. The computational model will be extended to include a more realistic form of inhibition, a more biophysically detailed hippocampal model, and a cognitive control system that allows the model to recall the sought-after memory even when it is weaker than competitors. These changes should improve our ability to predict behavioral forgetting effects and neural activity patterns in these high-volatility situations. The second specific aim of this research is to develop and utilize new experimental methods for testing hypotheses about competition-dependent learning. Testing the competition-dependent learning hypothesis is difficult because (according to the model) learning effects depend on the precise level of excitation of the competing memories. To address this problem, the proposed studies will use highly sensitive pattern classifier algorithms, applied to brain imaging data, to track the extent to which memories compete on a trial-by-trial basis. This neural readout of the competing memories can be used to test the model's predictions about how competition drives learning. The proposed studies will test the model's predictions about memory weakening in a perceptual priming paradigm, a short-term memory paradigm, and a long-term paired-associate learning paradigm. With regard to mental health: This research will improve our understanding of the circumstances that trigger strengthening and weakening of memories, and it will improve our ability to detect (based on neural activity) when strengthening or weakening will occur. These developments will help us to devise better methods for strengthening desirable memory associations and also methods for weakening maladaptive memories (e.g., when treating phobias or post-traumatic stress disorder).
This research will improve our understanding of the circumstances that trigger strengthening and weakening of memories, and it will improve our ability to detect (based on neural activity) when strengthening or weakening will occur. These developments will help us to devise better methods for strengthening desirable memory associations and also methods for weakening maladaptive memories (e.g., when treating phobias or post-traumatic stress disorder).
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| Schapiro, Anna C; McDevitt, Elizabeth A; Rogers, Timothy T et al. (2018) Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nat Commun 9:3920 |
| Kok, Peter; Turk-Browne, Nicholas B (2018) Associative prediction of visual shape in the hippocampus. J Neurosci : |
| Antony, James W; Ferreira, Catarina S; Norman, Kenneth A et al. (2017) Retrieval as a Fast Route to Memory Consolidation. Trends Cogn Sci 21:573-576 |
| Poppenk, Jordan; Norman, Kenneth A (2017) Multiple-object Tracking as a Tool for Parametrically Modulating Memory Reactivation. J Cogn Neurosci 29:1339-1354 |
| Cohen, Jonathan D; Daw, Nathaniel; Engelhardt, Barbara et al. (2017) Computational approaches to fMRI analysis. Nat Neurosci 20:304-313 |
| Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M et al. (2017) Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci 372: |
| Schapiro, Anna C; McDevitt, Elizabeth A; Chen, Lang et al. (2017) Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information. Sci Rep 7:14869 |
| Kim, Ghootae; Norman, Kenneth A; Turk-Browne, Nicholas B (2017) Neural Differentiation of Incorrectly Predicted Memories. J Neurosci 37:2022-2031 |
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