Biology subjects

Choi, W.-H.

Publications and source records attributed to Choi, W.-H..

3 recordsLinked to original sources

bioRxiv2024

Generative Adversarial Implicit Successor Representation

We propose a novel method to implicitly encode Successor Representations (SRs) using a Generative Adversarial Network (GAN). SRs are a method to encode states of the environment in terms of their predictive relationships with other states, which can be used to predict long-term future rewards. In standard explicit methods, the value of SR is found from an explicit map between future states after an action or to find an approximate function. Instead, our method encodes SR implicitly using a GAN. The distribution of samples generated by the GAN system approximates the successor representation. We also suggest an action decision procedure for the implicit encoding of SR. The system makes the decision using an analysis-by-synthesis procedure that it attempts to synthesize a sample that can explain the action decision constraints of the current and target states. Our system is different from the classical SR in several points. It can sample actual samples reflecting SR distribution, which is not easy for explicit models. It can also get around the issue of explicitly representing probabilities or successor representation values and doing math over them. We tested our system in a toy environment, where the agent could learn the implicit successor representation successfully and use it for action decisions.

neuroscience↗

bioRxiv2023

Predictive Motor Control Based on a Generative Adversarial Network

Predictive processing models suggest that a brain decides actions through inference using its internal generative model over the worlds states and their transitions. Most of the predictive processing models have been formalized using explicit representations of the probability distributions, with explicit structures and parameters. They are difficult to learn in general and needs explanation about representation of structure and parameters of distributions, and the method for statistical arithmetic on them, each of which are questions not easy to answer. In this study, we explore an alternative representation for predictive processing which is based on an implicit model known as generative adversarial networks, which has been widely explored recently in machine learning studies as they can learn a distribution directly from data. We demonstrate how a generative adversarial network can be trained to learn an implicit generative model of motor dynamics. And then, we show that such a model can perform approximate inference using the trained model, providing the necessary computations for both the forward and inverse model of motor control. Our framework may provide another formalization for brains inference model, especially for learning process. Additionally, we suggest that the functional architecture of the cortical-basal ganglia circuit may modeled as the generator and discriminator in the generative adversarial network model.

neuroscience↗

bioRxiv2022

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

We present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest whole genomic resource of Koreans. Korea4K provides 45,537,252 variants and encompasses most of the common and rare variants in Koreans. We identified 1,356 new geno-phenotype associations which were not found by the previous Korea1K dataset. Phenomics analyses revealed 24 genetic correlations, 1,131 pleiotropic variants, and 127 causal relationships from Mendelian randomization. Moreover, the Korea4K imputation reference panel showed a superior imputation performance to Korea1K. Collectively, Korea4K provides the most extensive genomic and phenomic data resources for discovering clinically relevant novel genome-phenome associations in Koreans.

genomics↗