Saudi Cultural Missions Theses & Dissertations
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Item Restricted Improving Model-fitting for Computational Models of Neural Function and Behaviour(King's College London, 2024) Ramis, Sarah; wise, TobyParameter estimation plays a critical role in computational neuroscience, enabling the design of models that can predict neural mechanisms under various conditions using statistical methods. However, accurate estimation becomes challenging when the likelihood function is intractable due to the model’s complexity and high dimensionality. With the advent of artificial intelligence, various machine learning algorithms have been developed to estimate the posterior distribution of observed data using simulation-based inference approaches. This study aims to elucidate the capabilities of these machine learning models compared to conventional methods to aid in model selection. Rescorla-Wagner simulated data is used to compare the performance of several techniques: Markov Chain Monte Carlo (MCMC) as a conventional method, Automatic Posterior Transformation (APT) using the Masked Auto-regressive Flow model, and APT enhanced with Gated Recurrent Units (APT-GRU) for data embedding, to estimate the parameters alpha and beta. The experiment involved 12 trials with varying parameter pairs and data volumes. The models were evaluated based on computational time, Mean Squared Error (MSE), Mean Absolute Error (MAE), a customized fit metric, Kullback-Leibler (KL) divergence, convergence, and log-likelihood. Results indicated that computational time increased across all models as dataset volume grew, with MCMC requiring significantly more time than the others. APT-GRU achieved lower MSE and MAE scores than the other models, indicating higher accuracy, followed by APT. Both APT-based models demonstrated stable convergence on large datasets, with APT-GRU showing enhanced convergence on smaller datasets. APT models also approached zero in KL divergence scores and positive log-likelihood values, signifying a good model fit. MCMC, while performing better in estimating alpha than beta, showed large negative KL divergence values, suggesting underfitting. Furthermore, MCMC exhibited lower performance stability across trials, with higher estimation variances. In summary, the findings highlight the strengths of APT and APT-GRU in parameter estimation within the Rescorla-Wagner framework. They suggest that the inclusion of GRU for data embedding improves both model robustness and estimation accuracy. These insights pave the way for future research into exploring different machine learning algorithms and model architectures for parameter estimations.12 0