Ruslan Klymentiev

Ruslan Klymentiev

50% Data Scientist, 50% Neuroscientist

About me

class AboutMe:
    
    def __init__(self, name=None):
        self.name = name
        self.interests = []
    
    def me(self, life_credo, learning=True):
        self.life_credo = life_credo
        if learning:
            self.activity = 'learning'
        else:
            self.activity = 'traveling'
            
    def add_interest(self, interest):
        if interest not in self.interests:
            self.interests.append(interest)

    
RK = AboutMe(name='Ruslan Klymentiev')
RK.me(life_credo='Never stop learning')
RK.add_interest('computational psychiatry')
RK.add_interest('decision making')
RK.add_interest('Bayesian statistics')
Want to improve your programming skills? Check out my free online course Python for Neuroscience!

Recent Posts

How Do Criminals Make Decisions?

The ability to read the mind of criminals for sure sounds exciting. However, as exciting as it may sound, assessing anyone’s decision-making processes is not the most straightforward task. This post will give a brief overview of the behavioral economics, psychology, and criminology literature on offenders’ decision-making. It will be discussed whether actions of crime can be considered rational and whether social influence plays a role in criminal behavior.

Psychopaths: the Good, the Bad, and the Crazy

The post aims to give a brief review of psychopathy as a neuropsychiatric disorder. It starts with a discussion of features that define the psychopath. Then it takes a closer look at some of the traits, such as empathy, to show the general beliefs and controversy across the studies with the provided overview of the dysfunctions in the brain and impairments that stem from them. Lastly, the post includes an overview of research on “successful” and “unsuccessful” psychopaths and assesses the literature-based opinion on whether psychopathy can be treated.

Computational Models of Behavior, part 2

How to fit reinforcement learning models to behavioral data using Bayesian inference. This part is focused on the hierarchical Bayesian modeling and particularly on the usage of hBayesDM package. Approaches for the model diagnostic, selection, validation are discussed. The post also goes over groups comparison using posterior distributions of model parameters. Additionally, a brief results comparison between Bayesian inference and Maximum Likelihood Estimation is provided.

Computational Models of Behavior, part 1

How to fit reinforcement learning models to behavioral data using Maximum Likelihood Estimation (MLE). The main goal is to show how to answer research questions using modeling. Post goes over important steps of modeling, such as model selection, model validation, and data generation.

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