Phantom perceptions arise almost universally in people who sustain sensory deafferentation, and in multiple sensory domains. The question arises ‘why’ the brain creates these false percepts in the absence of an external stimulus? The model proposed answers this question by stating that our brain works in a Bayesian way, and that its main function is to reduce environmental uncertainty, based on the freeenergy principle, which has been proposed as a universal principle governing adaptive brain function and structure. The Bayesian brain can be conceptualized as a probability machine that constantly makes predictions about the world and then updates them based on what it receives from the senses. The freeenergy principle states that the brain must minimize its Shannonian free-energy, i.e. must reduce by the process of perception its uncertainty (its prediction errors) about its environment. As completely predictable stimuli do not reduce uncertainty, they are not worthwhile of conscious processing. Unpredictable things on the other hand are not to be ignored, because it is crucial to experience them to update our understanding of the environment. Deafferentation leads to topographically restricted prediction errors based on temporal or spatial incongruity. This leads to an increase in topographically restricted uncertainty, which should be adaptively addressed by plastic repair mechanisms in the respective sensory cortex or via (para)hippocampal involvement. Neuroanatomically, filling in as a compensation for missing information also activates the anterior cingulate and insula, areas also involved in salience, stress and essential for stimulus detection. Associated with sensory cortex hyperactivity and decreased inhibition or map plasticity this will result in the perception of the false information created by the deafferented sensory areas, as a way to reduce increased topographically restricted uncertainty associated with the deafferentation. In conclusion, the Bayesian updating of knowledge via active sensory exploration of the environment, driven by the Shannonian free-energy principle, provides an explanation for the generation of phantom percepts, as a way to reduce uncertainty, to make sense of the world.

Biologists by tradition have seldom used the term representation to describe their findings. Instead they have relied on phrases such as “receptor field” on the sensory side and “command” or “corollary discharge” on the motor side when discussing neural control of sensation and motion in goal–directed behavior. Such words connote dynamic process rather than symbolic content. One might suppose that this neglect of a now common word reflects diffidence about discussing so–called higher functions of the brain, owing to a humbling lack of understanding of the brain’s complexity. Inspection of biology textbooks belies this view. Biologists have shown no lack of hubris in pontificating about the properties of the brain supporting mental functions. On the contrary, they have always taken pride in being uniquely qualified to explain brain function to anyone willing to listen.

The essential task of brain function is to construct orderly patterns of neural activity from disorderly sensory inputs, so that effective actions can be mounted by the brain, a finite state system, to deal with the world’s infinite complexity. Two schools of thought are described, that characterize distinctive sources of the order within brains, one passive, the other active. These schools have profoundly influenced ways two groups of contemporary neuroscientists design their experiments and process their data, so that they have very different perspectives on the roles of noise and chaos in brain function.

The branch of semiotics called semantics deals with the relation between meanings and representations, widely known as the symbol grounding problem. The other branches of semiotics, syntactics which deals with symbol–symbol relations as in a dictionary, and pragmatics which deals with symbol-action paradigms as in traffic signs, are well done by computers, but semantics has eluded computer simulation. In my view, this is because computer programmers have neglected that aspect of Shannon’s definition by which information has no meaning; computers process information, whereas brains create meaning. Brains obtain information about the world through the consequences of their own embodied actions. The information thus obtained is used in constructing meaning and is then discarded. One kind of information in the world consists of representations made by other brains for social communication. Computers use representations for information processing and symbol manipulation. However, brains have no internal representations. They deploy dynamic neural operators in the form of activity patterns, which constitute and implement meaning but not information, so that the problem of symbol grounding does not arise. Brains construct external representations in the form of material objects or movements as their means for expressing their internal states of meaning, such as words, books, paintings, and music, as well as facial expressions and gestures in animals and humans, but even though those material objects are made with the intent to elicit meaning in other brains, they have no meanings in themselves and do not carry meanings as if they were buckets or placards. Meanings can only exist in brains, because each meaning expresses the entire history and experience of an individual. It is an activity pattern that occupies the entire available brain, constituting a location in the intentional structure of a brain. It is the limited sharing of meanings between brains for social purposes that requires reciprocal exchanges of representations, each presentation by a transmitting brain inducing the construction of new meaning in the receiving brain. EEG data indicate that neural patterns of meanings in each brain occur in trajectories of discrete steps, which are demarcated by first-order state transitions that enable formation of spatiotemporal patterns of spatially coherent oscillations. Amplitude modulation is the mode of expressing meanings. These wave packets do not represent external objects; they embody and implement the meanings of objects for each individual, in terms of what they portend for the future of that individual, and what that individual should do with and about them.

Recent “connectionist” models provide a new explanatory alternative to the digital computer as a model for brain function. Evidence from our EEG research on the olfactory bulb suggests that the brain may indeed use computational mechanisms like those found in connectionist models. In the present paper we discuss our data and develop a model to describe the neural dynamics responsible for odor recognition and discrimination. The results indicate the existence of sensory- and motor-specific information in the spatial dimension of EEG activity and call for new physiological metaphors and techniques of analysis. Special emphasis is placed in our model on chaotic neural activity. We hypothesize that chaotic behavior serves as the essential ground state for the neural perceptual apparatus, and we propose a mechanism for acquiring new forms of patterned activity corresponding to new learned odors. Finally, some of the implications of our neural model for behavioral theories are briefly discussed. Our research, in concert with the connectionist work, encourages a reevaluation of explanatory models that are based only on the digital computer metaphor.

Key words: brain theory, chaos, cognitivism, connectionism, eeg, nonlinear dynamics, olfaction, perception, sensation