Eylem anlama ve duyguların beyin mekanizmalarına hesapsal yaklaşımlar

Through evolution living beings have gained unique features to deal with apparently easy but computationally expensive problems such as mate selection, learning sensorimotor skills and decision making. Thus, understanding how a biological system can process sensory information, interpret the probable results and find a solution in relatively short time to faced problems have become an attractive research area for computational neuroscience, artificial intelligence (AI) and robotics. In this thesis we focused on mirror neurons in the ventral premotor cortex (area F5) and the functional aspects of emotions from a computational but biologically plausible way. In the former part, the raw neural firing data from area F5 of macaque monkeys are analyzed to undercover neural representation using a decoding framework. For this, we propose two methods to detect mirror neurons by using machine learning and statistical analysis techniques. In the later part, we present that higher level emotions (those that have putatively evolved after the basic emotions of fear, anger etc.) are the behavioral manifestation of self-regulation mechanisms of computational (neuronal) energy expenditure for cognitive processing. To realize this proposal, we chose a tractable computational mechanism that may be considered as a model of neural computation mechanisms of the brain and deploy it on a robotic platform (Darwin-OP).