Although the usual way of representing Markov processes is time asymmetric, there is a way of describing Markov processes, due to Schrodinger, which is time symmetric. This observation provides a link between quantum mechanics and the layered Bayesian networks that are often used in automated pattern recognition systems. In particular, there is a striking formal similarity between quantum mechanics and a particular type of Bayesian network, the Helmholtz machine, which provides a plausible model for how the mammalian brain recognizes important environmental situations. One interesting aspect of this relationship is that the “wake‐sleep” algorithm for training a Helmholtz machine is very similar to the problem of finding the potential for the multi‐channel Schrodinger equation. As a practical application of this insight it may be possible to use inverse scattering techniques to study the relationship between human brain wave patterns, pattern recognition, and learning. We also comment on whether there is a relationship between quantum measurements and consciousness.
Although the usual way of representing Markov processes is time asymmetric, there is a way of describing Markov processes, due to Schrodinger, which is time symmetric. This observation provides a link between quantum mechanics and the layered Bayesian networks that are often used in automated pattern recognition systems. In particular, there is a striking formal similarity between quantum mechanics and a particular type of Bayesian network, the Helmholtz machine, which provides a plausible model for how the mammalian brain recognizes important environmental situations. One interesting aspect of this relationship is that the “wake‐sleep” algorithm for training a Helmholtz machine is very similar to the problem of finding the potential for the multi‐channel Schrodinger equation. As a practical application of this insight it may be possible to use inverse scattering techniques to study the relationship between human brain wave patterns, pattern recognition, and learning. We also comment on whether there is a relationship between quantum measurements and consciousness.
It has long been known that the Schrodinger equation provides a description of a certain kind of statistical Markov process. We show here that as a consequence, one can view quantum mechanics as an extension of the Helmholtz machine approach to pattern recognition. Not only does this observation throw new light on the relationship between quantum mechanics and computing theory, but it suggests that mathematical techniques developed for inverse scattering problems might provide an alternative to conventional pattern recognition algorithms.
In recent decades, progress in the field of neurosciences has triggered an interest in understanding mind-brain relationships. Quantum Mechanics (QM) has been present in the debate from its beginnings through the well-known measurement paradox. The standard interpretation of QM considers two basic, fundamentally irreducible, processes: the deterministic evolution of the wave-function according to the Schrodinger equation, once the initial conditions have been settled; and the indeterministic wave-function collapse into one of the possible outcomes, after performing a specific measurement. So, QM would point to the limits of a purely deterministic view of nature and, in particular, of brains. Nevertheless, QM’s relevance for the brain’s physics is still to be proven. Detractors of the QM influence are confident of the role of decoherent processes at different physical scales in order to ensure a classical deterministic behavior of the brain. However, little attention is paid to the epistemic implications of invoking decoherence for the mind-brain problem. In this paper, (i) we present lasting QM models stating a specific view of human consciousness and make explicit their position regarding the relationship between the physical activity of neurons and/or networks of neurons and the phenomenal conscious experience; (ii) we review the main criticisms of the relevance of QM in the brain and, most importantly, we bring out the philosophical implications behind the usual recourse to decoherence in the transition from the quantum to the classical world, explaining why the mind-brain problem and the measurement paradox should not be disentangled.
It has been suggested that consciousness plays an important role in quantum mechanics as it is necessary for the collapse of wave function during the measurement. Furthermore, this idea has spawned a symmetrical proposal: a possibility that quantum mechanics explains the emergence of consciousness in the brain. Here we formulated several predictions that follow from this hypothetical relationship and that can be empirically tested. Some of the experimental results that are already available suggest falsification of the first hypothesis. Thus, the suggested link between human consciousness and collapse of wave function does not seem viable. We discuss the constraints implied by the existing evidence on the role that the human observer may play for quantum mechanics and the role that quantum mechanics may play in the observer's consciousness.
This note was written by a theoretical physicist currently working on physical aspects of consciousness, mind-matter relationships, and mind-body interactions. My purpose here is to inform the readers of this journal, in not too technical terms, of some recent ideas relating quantum mechanics, gravitation, and psychological phenomena. Attempts to relate the nature of consciousness to quantum mechanics have been known almost from the be+g of quantum mechanics. For an extended discussion and earlier references see, e.g., Stapp (1982). The common thread of the majority of the suggested hypotheses linking consciousness to a quantum level of description is a drawing of a between the spontaneous localization of mental patterns and (spontaneous or induced) reduction of the wave function in quantum mechanics. The latter essentially means that quantum systems are inherently described in probabilistic terms and any external observation reduces the manyfold of all potential possibilia to a single given realization. Interpretation of this process is one of the most difficult and controversial topics of quantum mechanics since in quantum mechanics it is thus far essentially impossible to demarcate unambiguously the observer from the quantum system itself. Despite that gravitational effects are usually deemed to be too weak at atomic level, a few authors recently discussed gravity as an inherent route used by nature to achieve an automatic spontaneous reduction of the wave functions of extended objects. In his recent book, Roger Penrose (1989) looks for the possible connections between consciousness, quantum mechanical reduction and gravitational phenomena. To simplify, the essence of these hypotheses is that subtle gravitational effects at the atomic-molecular level lock the brain into a particular mental state, thereby providing the materialization of the potential (quantum) mental image by the route similar to the reduction of the wave function in quantum mechanics. Isotopic diversity of chemical elements can substantially contribute to this picture, as well as to life processes in general (Berezin, 1988). Since gravity is a mass-sensitive effect, the fact that different stable isotopes of the same element (e.g., carbon, oxygen, nitrogen) have different atomic masses may affect the path of mental processing.
There have been numerous neurobiological theories positing that quantum mechanics is importantly responsible for certain psychological phenomena, such as consciousness and conscious decision-making. In this respect, such theories understand the relevant aspects of the brain to be “quantum mechanical.” Although it is highly questionable whether any of these theories have the requisite or sufficient empirical evidence strictly within the domain of neuroscience to appropriately justify their views, a different approach to arguing that quantum mechanics is importantly relevant to human decisionmaking will be discussed. By putting various puzzle pieces together of empirical evidence in psychology that quantum probability models describe certain human cognitive decision-making processes, I contend that human decision-making overall is best described by quantum mechanical processes. Finally, I at times provide a novel defense of this view from a number of objections.
It is well known that the field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. There are many algorithms based on different theoretical backgrounds that could be used for pattern recognition in practical applications. Generally, most of the algorithms are applied in areas like classification, regression or change point detection.Recently, it has been shown that a probabilistic model based on two of the main concepts in quantum physics – a density matrix and the Born rule, can be suitable for the modeling of learning algorithms in biologically plausible artificial neural networks framework. It has been shown that the proposed probabilistic interpretation is suitable for modeling on-line learning algorithms for Independent/Principal/Minor Component Analysis, which could be realized on parallel hardware based on very simple computational units. Also, it has been shown that the quantum entropy of the system, related to that model, can be successfully used in the problems like change point or anomalies detection as well as simple classification problems. Here another application of the proposed quantum probabilistic model is going to be presented. A general paradigm called QLEAR learning (Quantum Low Entropy based Associative Reasoning) would be presented and tested in classification context. Proposed method potentially can overcome the problem that classifier performance depends greatly on the characteristics of the data to be classified. It is known that until now, there is no single classifier that works best on all given problems (a phenomenon that may be explained by the no-free-lunch theorem). Here we will try to propose a classification algorithm that, actually, automatically adjusts its performance according to characteristics of the data on which it is applied. An interesting aspect is that proposed method inherently solves the problem of unbalanced classes (classes that have significantly different size). The proposed paradigm can be applied in any area in which standard classification techniques are applied. The method is going to be analyzed in the context of classification, prediction and solving some mathematical problems.We’ll analyze, mainly, the case in which data is represented by vectors. Generalization toward multiway data would be discussed only on one example. The approach is based on the idea that classification can be understood as supervised clustering, where quantum entropy, in the context of the quantum probabilistic model, will be used as a “capturer” (measure, or external index) of the “natural structure” of the data. By using quantum entropy we don’t make any assumption about linear separability of the data that are going to be classified. The basic idea is to find close neighbours to a query sample and then use relative change in the quantum entropy as a measure of similarity of the newly arrived sample with the representatives of interest. In other words, method is based on calculation of quantum entropy of the referent system and its relative change with the addition of the newly arrived sample. Referent system consists of vectors/matrices that represent individual classes and that are the most similar, in Euclidean distance sense, to the vector that is analyzed. The classification problem is analysed in the context of measuring similarities to prototype examples of categories. The proposed method could be seen as a hybrid of nearest neighbor and optimization machine learning technique which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice.