Thomas Bayes (1701-1761) was an English theologian and philosopher who discovered the logic formulation known today as Bayes’ theorem. The theorem was first used to calculate probabilities in games of chance. Its applications were limited until the late twentieth century, as it required a high level of computational power. With the development of computer technology and algorithms, its use in statistics has increased in the 21st century, and it has also become an effective tool in fields such as neuroscience, machine learning and artificial intelligence.

Bayesian theory of the brain, or Bayesian approach to brain function, explores the capacity of the nervous system to work in situations of uncertainty according to Bayesian statistics. This theory is used in neuroscience and behavioral sciences to explain the cognitive ability of the brain according to statistical principles. It is assumed that the nervous system uses intrinsic probability models similar to those used in Bayesian inference to process sensory information.

According to the Bayesian theory of the brain, perception and feelings are beliefs. The world we perceive is our brain’s best guess at sensory signals.

A Brain in a Dark Room

Let’s imagine what it’s like to be a brain. Stuck in a bone box, trying to figure out what’s going on outside. The only tool we have for this is electrical signals coming from the senses like a waterfall. Signals from the senses are affected by both the outside world and the body itself. For example, what we see is affected by the ability of objects to emit or reflect light, as well as eye movements and the position of the head. In the 19th century, Hermann von Helmholtz realized that perception (solving the problem of what is outside) requires the brain to interpret sensory signals of external origin. The brain must organize sensory inputs in order to create an accurate internal model of the external world. For this reason, it has been suggested that the brain makes Bayesian inference. Bayesian inference describes updating beliefs as new evidence becomes available. In other words, the most probable causes of current sensory data are determined by combining previous opinion and information. Thus, perceptions occur. The differences (prediction errors) between the predicted signals and the actual sensory data obtained are used to update previous opinions. Thus, you will be more ready for the next sensory input. One consequence of the idea of ​​predictive coding is that the architecture of the cerebral cortex fits ideally with Bayesian inference. According to this view, information coming from the bottom up (from the senses to the brain) also includes prediction errors. “Top-down” signals from higher brain regions carry insights. The differences (prediction errors) between the predicted signals and the actual sensory data obtained are used to update previous opinions. Thus, you will be more ready for the next sensory input. One consequence of the idea of ​​predictive coding is that the architecture of the cerebral cortex fits ideally with Bayesian inference. According to this view, information coming from the bottom up (from the senses to the brain) also includes prediction errors. “Top-down” signals from higher brain regions carry insights. The differences (prediction errors) between the predicted signals and the actual sensory data obtained are used to update previous opinions. Thus, you will be more ready for the next sensory input. One consequence of the idea of ​​predictive coding is that the architecture of the cerebral cortex fits ideally with Bayesian inference. According to this view, information coming from the bottom up (from the senses to the brain) also includes prediction errors. “Top-down” signals from higher brain regions carry insights.

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Bayesian Brain: Artificial Intelligence, Machine Learning, Neuroscience

Unsupervised learning, which is a branch of Bayesian machine learning, is also important in analysis by synthesis approach. After all, the brain is a machine that makes predictions despite the uncertainty of the outside world. Many aspects of human perception and behavior can be modeled with Bayesian statistics. Apart from sensory perceptions, practices and experiments are carried out according to this theory in many sub-titles such as learning, memory, logic, language and decision making. There are various theories about how these processes develop in the brain. One of them is the hierarchical temporal memory theory (HTM) of George and Hawkins. It is based on the physiology and interaction of pyramidal neurons in the cerebral cortex. When applied on HTM computers, it has been found to be successful in areas such as prediction, anomaly detection and classification.

Bayesian theory of the brain implies deep connections to perception and imagination. The brain has a productive (generative) attitude towards sensory data, not passive. In order to feel something, the brain must be able to generate perception-like states corresponding to sensory data from top to bottom by itself. If this is true, the world we perceive is closely related to our own creative imagination.

Is the Brain Really Bayesian?

Although algorithms inspired by Bayes’ theorem can mimic various aspects of human consciousness, this does not prove that the brain does indeed use similar algorithms. For example, it may not explain the irrational functioning of our brain. The most important feature of scientific theories is that they are falsifiable. Although Bayesian brain theory can be designed to mimic almost any cognitive task with the adjustments to be made in assumptions and inputs, this flexibility also makes it resistant to falsification. Other information processing models, such as the neural network, can produce the same results as Bayesian models. In contrast, scientific research has not provided sufficient evidence that the brain does indeed use Bayesian information processing.

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Rehabilitasyonda Bayes

Bayes’ theorem is used to predict the behavior patterns of nerve cells and relate it to observed movements. These algorithms are useful in performing the movements desired by the person by the machine-prosthesis in the use of the brain-computer interface and neuroprosthesis. Hand, arm and leg movements in daily life are quite complex. Movements need to be constantly adapted to the environment and purpose. It is not possible to emulate this with pre-programmed fixed electrical signal patterns. Probability estimation algorithms are used to generate signals required for functional electrical stimulation of paralyzed muscles. Bayesian approach to robotic rehabilitation , brain stimulation and plasticityIt also finds both theoretical and practical applications in the fields of rehabilitation based on medicine.

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