Brain Modeling

Neurological Models of Behavior Understanding the roots of human behavior is a complicated process. Attempting to explore this concept from a neurological perspective is even more complicated, as it requires some sense of an ongoing pattern to describe the neurological process of learning certain behavior traits. Kozma et al. attempted to generate a mathematical model within a controlled environment. According to Kozma et al., the human psyche is composed of complex combinations, known as vectors, which represent particular values at any one moment in question.

Each neuron has a designated condition that ultimately impacts the conditions of surrounding neurons, thus allowing for a mathematical modeling of human behavior given the combinations of values that exert their influence on numerological components of the brain (Sayama et al., 2013). This model is within a controlled environment, where learning inputs invoke a response but do not need full neurological supervision to conduct learning practices. Stimuli outside of this environment fail to provoke such a response and are left out as background noise.

In this sense, there is an element of non-determination. It is this non-determination that conflicts with the Human Performance model. This model asserts the unpredictable nature of the eternal environment which then impacts the learning processes of the brain and neurological system. In the Human Performance model, randomness is created to account for the white noise that is left out in Kozma et al. The model is limited to the fact that it must operate within a controlled environment.

Input data must be presented to the human learner in a controlled fashion, so that learner can determine the importance of certain features and functions, thus letting what is unimportant go. However, the external world around us is not so clean cut. The environment is not a closed system, and there are elements and stimuli which are not directly understood by the learner (Kelso et al., 2013). From this perspective, there is a sense of uncertainty in regards to how some certain noise will function within the neurological system.

Thus, Kozma et al. does provide some freedom in the interpretation, opening up the system. Within this uncertainty, Kozma does question how the brain helps reduce errors between input and output data. Due to the fact that there are a seemingly infinite number of reactions possible within an open system, error reduction is something that is not able to be portrayed correctly within his modeling. Thus, it is tough to predict with mathematical clarity.

Still, there are what are known as pioneer neurons that do provide some guidance and clarity in the fact that they work towards generating the highest level of strategic optimal development. There.