Basics of Neuroaccounting

Computing all neuronal processes within its complexity in relation to behavior and intelligence is an objective for computational neuroethology. Neuroaccounting is a methodological approach for measurement of sensory pre-processing, biomechanical properties, processing (analyzing), and integration of models that represent brain, body, and environment as it sensing and acting during active perception.
Basic Principles of Neuroaccounting:
-Neuroaccounting cannot be used to predict human behavior – it only can be used to study complex neuronal interconnections and associations for future adjustment (change) through learning;
-Neuroaccounting cannot be used to establish developmental, mental, or other disorders – it only can be used for educational purposes and learning assessments;
-Social interactions in human communities defined as deterministic chaotic dynamics and responsible for environmental changes;
– Humans defined as a self-organizing deterministic systems displaying sensitivity to initial conditions in the environment and follow arrow of time;
– Humans can exhibit an almost infinite number of behaviors in different environments;
– Humans can employ, manipulate, and create infinite number of tools for different actions;
-Humans have constantly pass qualitatively different physical, emotional, and mental stages to exhibit more complex behavior in same environment;
-Human behavior objected by current modality, subjected by emotional response to environmental change, and affected by specific tools used during adaptation period;
– Neural plasticity responsible for motor control and learning. It characterized by long-term depression of inactive associated neuronal networks;
-Mechanisms for intrinsic physiological control of complex interacting networks within fluctuating environment include unconscious and conscious components;
-Individual subjectivism can be established and recorded only based on difference for particular community in response to the variety of learning techniques and multitude of learning parameters/rates;
-Strength of neuronal signaling between interacting networks should pass established threshold to be accounted as affecting or effecting and could not change the overall balance if modality of the communicating networks is different;

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