in turn, may impact the functioning of the nervous system. For example, disruptions in the balance of the microbiota are associated with the development of depression, anxiety disorders, and even neurodegenerative diseases such as Alzheimer’s disease.
Evolution and Development of These Systems
Over time, through the process of evolution, the systems in various animal species, including humans, became increasingly complex and adapted to the surrounding environment. In the human brain, several levels of development can be distinguished: from ancient structures found in our ancestors, including reptiles, to more complex and specialized regions, such as the neocortex, responsible for abstract thinking, planning, and self-awareness.
In reptiles and their ancestors, including early mammals, there was a part of the brain responsible for basic survival functions, such as instincts, aggression, and sexual behavior. As evolution progressed, and more complex cognitive functions developed, new structures were added to this ancient brain, such as the limbic system, which is responsible for emotions, and the neocortex, which developed in mammals and enables more complex cognitive tasks like abstraction, planning, and self-reflection.
These changes led to the creation of brain structures that process information not only based on current events but also in anticipation of future states, allowing adaptation to the changing conditions of the environment. Brain evolution not only improved survival mechanisms but also created conditions for more complex forms of behavior, such as social interactions, empathy, and language.
Brain Development in Octopuses
The brain of octopuses has a remarkable structure and functional features that distinguish it from the brains of mammals. While octopuses do not possess the same complex brain system as mammals, they demonstrate a high level of cognitive abilities such as learning, tool use, problem-solving, and even signs of personality.
The octopus brain is divided into several parts, with the majority of its mass concentrated in the head. However, two-thirds of its neurons are located in the arms. This unique structure allows each arm to operate relatively independently and make its own decisions. This trait provides octopuses with exceptional flexibility in interacting with their environment and adapting to changing conditions.
Differences in Brain Function Between Octopuses and Humans
Mammals, including humans, developed complex social structures, which contributed to the evolution of a more centrally organized brain. As mammals, we have a highly developed cerebral cortex (especially the frontal lobes), which is responsible for functions such as planning, self-control, and abstract thinking. Our brain is also closely connected to the hypothalamus and the endocrine system, which allows hormones like cortisol and oxytocin to regulate behavior in response to external and internal stimuli.
In contrast, the octopus brain, while also highly developed, functions somewhat differently. The concentration of neurons in their arms allows octopuses to make decisions at a local level without needing to send signals to the central brain. This provides them with remarkable autonomy and the ability to adapt to a variety of situations. For example, octopuses can solve problems related to spatial perception and object manipulation, not only thanks to their central brain but also through their body, which is a unique feature.
In both cases – in mammals and octopuses – the brain serves as an adaptive organ that processes information about the external world and makes decisions based on the organism’s current needs. However, while mammals developed a central brain to coordinate actions and social interactions, octopuses use local brain structures to maintain a high degree of independence for their body parts. This difference reflects distinct evolutionary survival strategies, where mammals rely on collective behavior and complex social interactions, while octopuses depend on individual decision-making and flexibility in manipulating their environment.
The Bayesian Approach to the Mind: The Free Energy Principle and Predictive Coding Theory
Predictive Coding and its foundations, related to Bayesian approaches, play a central role in contemporary understanding of how the brain perceives and processes information. Unlike traditional views of perception, where the brain simply reacts to sensory data, the theory of predictive coding argues that the brain actively constructs models of the world and uses them to predict future events. These predictions are then compared with the actual sensory information received through the senses. Prediction error – the difference between what the brain expects and what it actually perceives – serves as a signal for updating the mental model. This process allows the brain to minimize energy costs, accelerating perception and increasing adaptability, which forms the basis for the effective functioning of cognitive processes.
In recent decades, the theory of predictive coding has increasingly been seen as part of the broader Free Energy Principle, which links it with Bayesian inference, Active Inference, and other approaches focused on minimizing uncertainty and adapting to environmental changes.1. However, despite the growing interest in this integrative approach, predictive coding itself remains a fundamental concept for understanding how the brain constructs models of the world and updates them based on new data. This work will focus primarily on predictive coding, its neurobiological mechanisms, and its role in cognitive processes.
The historical roots of the theory of predictive coding indeed trace back to the works of Pierre-Simon Laplace, who laid the foundation for the concept of determinism. Laplace, one of the first to consider ideas of probability and determinism in the context of predicting the future, proposed that if one had complete knowledge of the current state of the universe, the future could be predicted with absolute certainty. His hypothesis of the “Laplace Demon,” which could predict the future with perfect accuracy, was based on the idea that if we knew all the parameters of microstates, including the position and velocity of every particle, all events – including human thoughts and actions – could be predicted.
This idea of an all-knowing observer and the ability to predict future events based on complete knowledge of present conditions provided an early conceptual foundation for understanding how the brain processes information and makes predictions about the future. Predictive coding and the free energy principle are modern extensions of this concept, where the brain continually updates its internal models of the world to minimize prediction errors and uncertainty.
However, the concept of prediction and world modeling began to develop much later. In the 18th and 19th centuries, Laplace’s ideas about determinism started to be questioned by contemporary philosophers and scientists such as Isaac Newton, Carl Friedrich Gauss, and others. Ideas related to probabilistic calculations and uncertainty gained popularity with the development of statistics and thermodynamics.
The shift toward probabilistic thinking marked a key turning point in the evolution of predictive models. It became increasingly clear that the world is not fully deterministic and that knowledge of the present state is often insufficient to predict the future with absolute certainty. This uncertainty was formally recognized in statistical mechanics, which introduced the concept of entropy – a measure of disorder or uncertainty in a system. As a result, the idea that the brain might work with probabilities, updating predictions based on new information, became more plausible and relevant in the context of cognitive neuroscience.
In the 20th century, the works of Klaus Heisler, Richard Feynman, and Jan Frenkel represented a significant step toward understanding how predictions can operate in conditions of uncertainty and how the brain can construct hypotheses in the context of probability and imperfection. These scientists proposed mathematical approaches that ultimately laid the foundation for the theory of predictive coding in neurobiology.
Equally important contributions to the development of the idea of prediction and coding theory came from researchers in the field of neuroscience in the mid-20th century, such as Benjamin Libet and Nobel laureates Roger Sperry and Jean-Pierre Chevalier. For example, Libet conducted experiments that demonstrated the brain