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

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.

Intro to EEG Data Analysis

This post serves as an introduction to the EEG data processing and particularly the usage of MNE-Python package. The post goes over such preprocessing steps as labeling bad channels and trials, artifacts removal, and data epoching. Additionally, the event-related potential is calculated and compared between groups and conditions. Sample of EEG data is taken from Cavanagh et al. (2019) experiment.

Talks

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