The Generalized Empirical Method as outlined by Henman initially seems a cogent approach that should be adopted by cognitive neuroscientists. However, some weaknesses in the presumptions of this method in light of modern neuroscience research may challenge its validity. As I am currently working on mapping cerebral-cerebellar networks using fMRI, I am intrigued by the practical utility of the GEM in experimental work.

In this paper, I examined whether evidence from the neuroscience of morality supports the standard models of phronesis, i.e., Jubilee and Aretai Centre Models. The standard models explain phronesis as a multifaceted construct based on interaction and coordination among functional components. I reviewed recent neuroscience studies focusing on brain networks associated with morality and their connectivity to examine the validity of the models. Simultaneously, I discussed whether the evidence helps the models address challenges, particularly those from the phronesis (...) eliminativism. Neuroscientific evidence supported the importance of brain networks, i.e., the default mode, salience, and central executive functioning networks, in moral functioning in general. The findings favorably supported the multifaceted and integrative nature of phronesis proposed by the standard models. Finally, I considered how the two models could explain the mechanisms of phronesis more integratively based on neuroscientific findings. At the end of this paper, with the evidence, I proposed several practical ideas to promote the cultivation of phronesis, e.g., the consideration of coordination among components for moral functioning and the use of moral exemplars. (shrink)

In the brain the relations between free neurons and the conditioned ones establish the constraints for the informational neural processes. These constraints reflect the systemenvironment state, i.e. the dynamics of homeocognitive activities. The constraints allow us to define the cost function in the phase space of free neurons so as to trace the trajectories of the possible configurations at minimal cost while respecting the constraints imposed. Since the space of the free states is a manifold or a non orthogonal (...) space, the minimum distance is not a straight line but a geodesic. The minimum condition is expressed by a set of ordinary differential equation ( ODE ) that in general are not linear. In the brain there is not an algorithm or a physical field that regulates the computation, then we must consider an emergent process coming out of the neural collective behavior triggered by synaptic variability. We define the neural computation as the study of the classes of trajectories on a manifold geometry defined under suitable constraints. The cost function supervises pseudo equilibrium thermodynamics effects that manage the computational activities from beginning to end and realizes an optimal control through constraints and geodetics. The task of this work is to establish a connection between the geometry of neural computation and cost functions. To illustrate the essential mathematical aspects we will use as toy model a Network Resistor with Adaptive Memory (Memristors).The information geometry here defined is an analog computation, therefore it does not suffer the limits of the Turing computation and it seems to respond to the demand for a greater biological plausibility. The model of brain optimal control proposed here can be a good foundation for implementing the concept of "intentionality",according to the suggestion of W. Freeman. Indeed, the geodesic in the brain states can produce suitable behavior to realize wanted functions and invariants as neural expressionsof cognitive intentions. (shrink)

The book is thought-provoking and informative, wide in scope while also being technically detailed, and still relevant to modem AI [at least as of 1998, the time of writing this review, but probably also at the time of posting this entry here, 2023] even though it was published in 1989. This relevance lies mainly in the book’s advocacy of distributed computation at multiple levels of description, its combining of neural networks and other techniques, its emphasis on the interplay between (...) action and perception, and its particular approach to natural language processing. The criticisms I have occasionally made above are relatively minor, and one of the major shortcomings of schema theory at least at the time of the book’s publication -- namely the lack of a detailed, preferably neural, mechanism for schema instantiation -- is one of which Arbib is well aware. The book is well produced, for the most part clearly written, and has useful summaries at the beginning of each chapter. Both general readers and AI researchers (mainly but not exclusively those working on robotics, vision and neural networks) could benefit from reading the book. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel capabilities (...) of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with (...) experimental findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

In biological terms, human consciousness appears as a feature associated with the func- tioning of the human brain. The corresponding activities of the neural network occur strictly in accord with physical laws; however, this fact does not necessarily imply that there can be a comprehensive scientific theory of conscious- ness, despite all the progress in neurobiology, neuropsychology and neurocomputation. Pre- dictions of the extent to which such a theory may become possible vary widely in the scien- tific community. There (...) are basic reasons - not only practical but also epistemological - why the brain-mind relation may never be fully “decod- able” by general finite procedures. In partic- ular self-referential features of consciousness, such as self-representations involved in strate- gic thought and dispositions, may not be resolv- able in all their essential aspects by brain analy- sis. Assuming that such limitations exist, objec- tive analysis by the methods of natural science cannot, in principle, fully encompass subjective, mental experience. (shrink)

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being (...) conscious. Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills — when conscious information processing is replaced by subconscious — is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human. (shrink)

A model of brain function is presented that is consistently based on the biological principle of equilibrium. The neuronal modules of the cerebral cortex are proposed as units in which equilibrium between incoming signals and the synaptic structure is determined or established. Because of the electromagnetic activity of the brain, the electromagnetic properties of thecells are brought into focus. Due to the synaptic changes of the modules -essentially during sleep -an electromagnetic resting balance between the modules is established. (...) Incoming signals during the day, disturb the electromagnetic resting equilibrium and are detected and understood by this. The connecting nodes within the neuronal network are given by the equilibrium modules. Incoming information is represented in the form of the specific pathway of the network, while recognized information is represented by the equilibrium states within the modules. The paper leads to an understanding of information storage and processing in the brain. It even provides a hypothesis for understanding the emergence of the "self". Finally, the consideration of electromagnetic wave properties of neurons opens up a biophysical starting point to understanding conscious perceptions. In neuroscience, we lack a unifying theory of the brain. The reason for this may be an important detail –a missing link –that we do not yet see. Following the "track of biological equilibrium" in this paper leads to the hypothesis that the electromagnetic properties of the neurons are potential candidates to fill that gap. A hypothesis is developed describing their physiological significance in the processing of neurological information. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained (...) using data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Biological evolution and technological innovation, while differing in many respects, also share common features. In particular, implementation of a new technology in the market is analogous to the spreading of a new genetic trait in a population. Technological innovation may occur either through the accumulation of quantitative changes, as in the development of the ocean clipper, or it may be initiated by a new combination of features or subsystems, as in the case of steamships. Other examples of the latter type (...) are electric networks that combine the generation, distribution, and use of electricity, and containerized transportation that combines standardized containers, logistics, and ships. Biological evolution proceeds, phenotypically, in many small steps, but at the genetic level novel features may arise not only through the accumulation of many small, common mutational changes, but also when distinct, relatively rare genetic changes are followed by many further mutations. In particular, capabilities of biologically modern man may have been initiated, perhaps some 150 000 years ago, by one or few accidental but distinct combinations of modules and subroutines of gene regulation which are involved in the generation of the neural network in the cerebral cortex. It is even conceivable that it was one primary genetic event that initiated the evolution of biologically modern man, introducing some novel but subtle feature of connectivity into the cerebral cortex which allowed for meta-levels of abstraction and upgraded modes of information processing. This may have set the stage for the evolution of integrated but diverse higher capabilities such as structured language, symbolic thought, strategic thought, and cognition based empathy. (shrink)

Objective: Alterations in psychomotor dimension cut across different psychiatric disorders, such as schizophrenia (SCZ) and bipolar disorder (BD). This preliminary study aimed to investigate the organization of intrinsic brain activity in the subcortical-cortical sensorimotor system in SCZ (and BD) as characterized according to psychomotor dimension. -/- Method: In this resting-state functional magnetic resonance imaging (fMRI) study, functional connectivity (FC) between thalamus and sensorimotor network (SMN), along with FC from substantia nigra (SN) and raphe nuclei (RN) to basal ganglia (BG) (...) and thalamic regions, were investigated by using an a-priori-driven and dimensional approach. This was done in two datasets: SCZ patients showing inhibited psychomotricity (n = 18) vs. controls (n = 19); SCZ patients showing excited psychomotricity (n = 20) vs. controls (n = 108). Data from a third dataset of BD in inhibited depressive or manic phases (reflecting inhibited or excited psychomotricity) were used as control. -/- Results: SCZ patients suffering from psychomotor inhibition showed decreased thalamus-SMN FC toward around-zero values paralleled by a concomitant reduction of SN-BG/thalamus FC and RN-BG/thalamus FC (as BD patients in inhibited depression). By contrast, SCZ patients suffering from psychomotor excitation exhibited increased thalamus-SMN FC toward positive values paralleled by a concomitant reduction of RN-BG/thalamus FC (as BD patients in mania). -/- Conclusions: These findings suggest that patients exhibiting low or high levels of psychomotor activity show distinct patterns of thalamus-SMN coupling, which could be traced to specific deficit in SN- or RN-related connectivity. Notably, this was independent from the diagnosis of SCZ or BD, supporting an RDoC-like dimensional approach to psychomotricity. (shrink)

Psychiatric and neurological disorders have historically provided key insights into the structure-function rela- tionships that subserve human social cognition and behavior, informing the concept of the ‘social brain’. In this review, we take stock of the current status of this concept, retaining a focus on disorders that impact social behavior. We discuss how the social brain, social cognition, and social behavior are interdependent, and emphasize the important role of development and com- pensation. We suggest that the social (...) class='Hi'>brain, and its dysfunction and recovery, must be understood not in terms of specific structures, but rather in terms of their interaction in large-scale networks. (shrink)

[This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide (...) a nutshell explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

Scientific studies have shown that non-conscious stimuli and représentations influence information processing during conscious experience. In the light of such evidence, questions about potential functional links between non-conscious brain representations and conscious experience arise. This article discusses models capable of explaining how statistical learning mechanisms in dedicated resonant circuits could generate specific temporal activity traces of non-conscious representations in the brain. How reentrant signaling, top-down matching, and statistical coincidence of such activity traces may lead to the progressive consolidation (...) of neural signatures of conscious experience in networks extending across large distances beyond functionally specialized brain regions is then explained. (shrink)

Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect-a "malignant sadness". Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naïve MDD (...) patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naïve MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DNMTL) and mood regulation pathway-the fronto-parietal control network (FPCN), the thalamus, and the salience network (SN)-were restored to normal connectivity, relative to the pre-treatment condition; however, the altered connections of FPCN-core DN (DNCORE), FPCN-SN, and intra-FPCN among MDD patients remained impaired; (2) thalamo-prefrontal connectivity provides moderate predictive power (r2 = 0.63) for the effectiveness of sertraline treatment. In summary, our findings contribute to a body of evidence that suggests salubrious effects of sertraline treatment primarily involve the FPCN-thalamus-SN pathway. The pre-treatment rsFC in this pathway could serve as a predictor of sertraline treatment outcome. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

I go deep into the biology of the human organism to argue that the psychological features and functions of persons are realized by cellular and molecular parallel distributed processing networks dispersed throughout the whole body. Persons supervene on the computational processes of nervous, endocrine, immune, and genetic networks. Persons do not go with brains.

Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are (...) re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. (shrink)

Capacity of conscious agents to perform genuine choices among future alternatives is a prerequisite for moral responsibility. Determinism that pervades classical physics, however, forbids free will, undermines the foundations of ethics, and precludes meaningful quantification of personal biases. To resolve that impasse, we utilize the characteristic indeterminism of quantum physics and derive a quantitative measure for the amount of free will manifested by the brain cortical network. The interaction between the central nervous system and the surrounding environment is shown (...) to perform a quantum measurement upon the neural constituents, which actualize a single measurement outcome selected from the resulting quantum probability distribution. Inherent biases in the quantum propensities for alternative physical outcomes provide varying amounts of free will, which can be quantified with the expected information gain from learning the actual course of action chosen by the nervous system. For example, neuronal electric spikes evoke deterministic synaptic vesicle release in the synapses of sensory or somatomotor pathways, with no free will manifested. In cortical synapses, however, vesicle release is triggered indeterministically with probability of 0.35 per spike. This grants the brain cortex, with its over 100 trillion synapses, an amount of free will exceeding 96 terabytes per second. Although reliable deterministic transmission of sensory or somatomotor information ensures robust adaptation of animals to their physical environment, unpredictability of behavioral responses initiated by decisions made by the brain cortex is evolutionary advantageous for avoiding predators. Thus, free will may have a survival value and could be optimized through natural selection. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing (...) to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it (...) is important to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

The brain is composed of electrically excitable neuronal networks regulated by the activity of voltage-gated ion channels. Further portraying the molecular composition of the brain, however, will not reveal anything remotely reminiscent of a feeling, a sensation or a conscious experience. In classical physics, addressing the mind–brain problem is a formidable task because no physical mechanism is able to explain how the brain generates the unobservable, inner psychological world of conscious experiences and how in turn (...) those conscious experiences steer the underlying brain processes toward desired behavior. Yet, this setback does not establish that consciousness is non-physical. Modern quantum physics affirms the interplay between two types of physical entities in Hilbert space: unobservable quantum states, which are vectors describing what exists in the physical world, and quantum observables, which are operators describing what can be observed in quantum measurements. Quantum no-go theorems further provide a framework for studying quantum brain dynamics, which has to be governed by a physically admissible Hamiltonian. Comprising consciousness of unobservable quantum information integrated in quantum brain states explains the origin of the inner privacy of conscious experiences and revisits the dynamic timescale of conscious processes to picosecond conformational transitions of neural biomolecules. The observable brain is then an objective construction created from classical bits of information, which are bound by Holevo’s theorem, and obtained through the measurement of quantum brain observables. Thus, quantum information theory clarifies the distinction between the unobservable mind and the observable brain, and supports a solid physical foundation for consciousness research. (shrink)

Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that (...) it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

Mathematical cognition is one of the most important cognitive functions of human beings. The latest brain and cognitive research have shown that mathematical cognition is a system with multiple components and subsystems. It has phylogenetic root, also is related to ontogenetic development and learning, relying on a large-scale cerebral network including parietal, frontal and temporal regions. Especially, the parietal cortex plays an important role during mathematical cognitive processes. This indicates that language and visuospatial functions are both key to mathematical (...) cognition. All of those advances have important implications for basic mathematical education. (shrink)

Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, (...) we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean) and variance dFC (dFCvar). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar). (shrink)

It is possible today to determine, with some precision (according to the most recent studies in neuroscience and evolutionary psychology), the areas of the brain and the neural networks involved when an individual contemplates art, when feeling pleasure, or when judging about aesthetic experience. However, many questions remain open. First, the philosophical question about the subjective nature of this kind of judgments. Then, what happens in the mind (or should it be said, in the brain?) of the (...) beholder when contemplating art or judging in favor (or not) of the beauty of an object. And the ultimate question, if we have an aesthetic brain. Another issue that must be addressed is if bioart and especially neuroart can contribute to this analysis and if they can be effectively quantified as art. Thus, this brief essay seeks to provide some understanding about this questions but most importantly about the existence of an aesthetic brain, which may ultimately contribute to open doors to other problems of philosophy such as the hard brain-mind problem. (shrink)

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...) anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem. (shrink)

Two universal functional principles of Grossberg’s Adaptive Resonance Theory [19] decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They (...) are re-visited in this concept paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. (shrink)

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional (...) plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. (shrink)

While the past century of neuroscientific research has brought considerable progress in defining the boundaries of the human cerebral cortex, there are cases in which the demarcation of one area from another remains fuzzy. Despite the existence of clearly demarcated areas, examples of gradual transitions between areas are known since early cytoarchitectonic studies. Since multi-modal anatomical approaches and functional connectivity studies brought renewed attention to the topic, a better understanding of the theoretical and methodological implications of fuzzy boundaries in (...) class='Hi'>brain science can be conceptually useful. This article provides a preliminary conceptual framework to understand this problem by applying philosophical theories of vagueness to three levels of neuroanatomical research. For the first two levels (cytoarchitectonics and fMRI studies), vagueness will be distinguished from other forms of uncertainty, such as imprecise measurement or ambiguous causal sources of activation. The article proceeds to discuss the implications of these levels for the anatomical study of connectivity between cortical areas. There, vagueness gets imported into connectivity studies since the network structure is dependent on the parcellation scheme and thresholds have to be used to delineate functional boundaries. Functional connectivity may introduce an additional form of vagueness, as it is an organizational principle of the brain. The article concludes by discussing what steps are appropriate to define areal boundaries more precisely. (shrink)

Recent evidence points to widespread underconnectivity in autistic brains owing to deviant white matter, the fibers that make long connections between areas of the cortex. Subjects with autism show measurably fewer long-range connections between the parietal and prefrontal cortices. These findings may help shed light on the current debate in the consciousness literature about whether conscious states require both prefrontal and parietal/temporal components. If it can be shown that people with autism have conscious states despite such underconnectivity, this would constitute (...) an argument for the claim that conscious states can exist in posterior cortex without associated prefrontal activity. This in turn lends support to a class of theories according to which microconsciousness is possible—consciousness in small areas of cortex without active connections to the prefrontal cortex, as opposed to the higher-order thought (HOT) theory of consciousness, according to which conscious states can only occur when posterior cortical areas (in the parietal or temporal lobes) have active connections to the prefrontal cortex. In this chapter, after listing several candidate examples of consciousness without accompanying prefrontal connections, I will argue that autism provides yet another such example. I will also examine a recent version of the higher-order theory that acknowledges these cases of consciousness without prefrontal activity and, instead depicts consciousness as requiring higher-order thoughts located in posterior cortex. In the final section, I will examine the consequences of these views for our understanding of the metaphysical nature of consciousness itself—the classic mind-body problem. (shrink)

Molecular developmental biology has expanded our conceptions of gene actions, underpinning that embryonic development is not only governed by a set of specific genes, but as much by space–time conditions of its developing modules. Typically, formation of cellular spheres, their transformation into planar epithelia, followed by tube formations and laminations are modular steps leading to the development of nervous tissues. Thereby, actions of organising centres, morphogenetic movements, inductive events between epithelia, tissue polarity reversal, widening of epithelia, and all these occurring (...) orderly in space and time, are driving forces of emergent laminar neural tissues, e.g. the vertebrate retina. Analyses of self-organisational formation of retina-like 3D structures from dispersed cells under defined cell culture conditions demonstrate that not only particular genetic networks, but—at least as important—the applied culture conditions define phenotypes of emergent tissues. Such in vitro approaches allow assigning emerging tissue formation to ground-laying genetic networks separately from contributions by conditional constraints. (shrink)

We explicate representational content by addressing how representations that ex- plain intelligent behavior might be acquired through processes of Darwinian evo- lution. We present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real-world examples of neural network control of animal behavior. We argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about the creature’s environ- ments (...) and further can do so only if their neural states are appropriately isomor- phic to environmental states. Further, these informational and isomorphism rela- tions are what are tracked by content attributions in folk-psychological and cognitive scientific explanations of these intelligent behaviors. (shrink)

Spontaneous activity levels prior to stimulus presentation can determine how that stimulus will be perceived. It has also been proposed that such spontaneous activity, particularly in the default-mode network (DMN), is involved in self-related processing. We therefore hypothesised that pre-stimulus activity levels in the DMN predict whether a stimulus is judged as self-related or not. Method: Participants were presented in the MRI scanner with a white noise stimulus that they were instructed contained their name or another. They then had to (...) respond with which name they thought they heard. Regions where there was an activity level difference between self and other response trials two seconds prior to the stimulus being presented were identified. Results: Pre-stimulus activity levels were higher in the right temporoparietal junction (RTPJ), the right temporal pole (RTP), and the left superior temporal gyrus in trials where the participant responded that they heard their own name than trials where they responded that they heard another. Conclusion: Pre-stimulus spontaneous activity levels in particular brain regions, largely overlapping with the DMN, predict the subsequent judgement of stimuli as self-related. This extends our current knowledge of self-related processing and its apparent relationship with intrinsic brain activity in what can be termed a rest-self overlap. (shrink)

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal (...) attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC. (shrink)

Neuroscience can relate to ethics and normative issues via the brain’s cognitive control network. This network accomplishes several executive processes, such as planning, task-switching, monitoring, and inhibiting. These processes allow us to increase the accuracy of our perceptions and our memory recall. They also allow us to plan much farther into the future, and with much more detail than any of our fellow mammals. These abilities also make us fitting subjects for responsibility claims. Their activity, or lack thereof, is (...) at the heart of culpability. For instance, planning to kill someone is strong evidence of what the law calls mens rea—a guilty mind. Claims about norms, or ethical “should” claims, express two-level propositions, directed at the behaving person at one level, and at that person’s mind and cognitive control network at another level. Thus, “People should stop themselves from hurting others,” is a claim about how people should behave and also a claim about how their cognitive control networks should behave—i.e., they should inhibit harmful behavior, or the intentions leading up to it. Planning is both an ability of the full person, and of that person’s mind. Neuroscience affirms the common notion, seen both in law and folk psychology, that what makes us guilty or culpable are certain events and states that exist in our minds. Overt behavior, including speech, is fallible evidence of these states and processes. Cases of negligence still involve the executive processes, but “negatively,” in that negligence results when certain types of executive activity fail to take place. (shrink)

Evidence that minds are neural electromagnetic fields comes from research into how separate brain activities bind to form unified percepts and unified minds. Explanations of binding using synchrony, attention, and convergence are all problematic. But the unity of EM fields explains binding without these problems. These unified fields neatly explain correlations and divergences between synchrony, attention, convergence, and unified minds. The simplest explanation for the unity of both minds and fields is that minds are fields. Treating minds as the (...) fields' underlying, intrinsic nature has the further virtue of avoiding mind-body problems. There's also rising evidence that EM fields interact with brains. McFadden argues here that fields help shift the focus of attention by initiating synchrony in different neural networks, which boosts their own fields and thereby binds their activity. This evidence that minds are fields that bind and guide brain activities supports free will over epiphenomenalism and supervenience. (shrink)

Buddhist philosophy asserts that human suffering is caused by ignorance regarding the true nature of reality. According to this, perceptions and thoughts are largely fabrications of our own minds, based on conditioned tendencies which often involve problematic fears, aversions, compulsions, etc. In Buddhist psychology, these tendencies reside in a portion of mind known as Store consciousness. Here, I suggest a correspondence between this Buddhist Store consciousness and the neuroscientific idea of stored synaptic weights. These weights are strong synaptic connections built (...) in through experience. Buddhist philosophy claims that humans can find relief from suffering through a process in which the Store consciousness is transformed. Here, I argue that this Buddhist 'transformation at the base' corresponds to a loosening of the learned synaptic connections. I will argue that Buddhist meditation practices create conditions in the brain which are optimal for diminishing the strength of our conditioned perceptual and behavioural tendencies. (shrink)

It is not likely that we will ever convincingly know how and why we came to be on this planet; of course, this has never prevented inquisitive minds from pushing the frontiers of understanding and discovery further. Our origin is the subject of scientific theories and continuous inquiries with no end in sight, as the shells of related complexities are getting much harder to crack. Paraphrasing philosopher and historian Will Durant, a very few people are getting to know more and (...) more about less and less. This knowledge and the scientific language used to express it are now becoming more and more incomprehensible to most humans; therefore, despite the sincere efforts of communication media’s countless “talking heads,” we may be inevitably driven by default to a new religion of worshiping the church of science, along with its scientist “priests,” and to a life filled with the unequivocal dangers that could befall us from such devotions. Humanity and the planet are faced with dangers that have and can be issued from the absence or deficiency of proper public knowledge and guiding ethical principles for handling many of the new scientific and engineering discoveries and innovations, such as artificial intelligence, nanotechnology, and plant, animal, and human gene editing, etc. To avoid inadvertent adverse impacts, which could seriously endanger our normal ways of life and even existence, knowledge has to be humanized and massively disseminated. Even at the expense of some mistakes, the benefits certainly outweigh the loss. -/- This book is a small effort along this path. -/- . (shrink)

The concept of 'social brain‘ is a hybrid, located somewhere in between politically motivated philosophical speculation about the mind and its place in the social world, and recently emerged inquiries into cognition, selfhood, development, etc., returning to some of the founding insights of social psychology but embedding them in a neuroscientific framework. In this paper I try to reconstruct a philosophical tradition for the social brain, a ‗Spinozist‘ tradition which locates the brain within the broader network of (...) relations, including social relations. This tradition runs from Spinoza to Lev Vygotsky in the early 20th century, and on to Gilles Deleuze, Toni Negri and Paolo Virno in recent European philosophy, as a new perspective on the brain. The concept of social brain that is articulated in this reconstruction – some early-20th century Soviet neuropsychologists spoke of socialism and the cortex as being ―on the same path‖ – overcomes distinctions between Continental thought and the philosophy of mind, and possibly gives a new metaphysical framework for social cognition. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is (...) with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

It is common to distinguish between “holist” and “reductionist” views of brain function, where the former envisions the brain as functioning as an indivisible unit and the latter as a collection of distinct units that serve different functions. Opposing reductionism, a number of researchers have pointed out that cortical network architecture does not respect functional boundaries, and the neuroanatomist V. Braitenberg proposed to understand the cerebral cortex as a “great mixing machine” of neuronal activity from sensory inputs, motor (...) commands, and intrinsically generated processes. In this paper, we offer a contextualization of Braitenberg’s point, and we review evidence for the interactions of neuronal activity from multiple sensory inputs and intrinsic neuronal processes in the cerebral cortex. We focus on new insights from studies on audiovisual interactions and on the influence of respiration on brain functions, which do not seem to align well with “reductionist” views of areal functional boundaries. Instead, they indicate that functional boundaries are fuzzy and context dependent. In addition, we discuss the relevance of the influence of sensory, proprioceptive, and interoceptive signals on cortical activity for understanding brain-body interactions, highlight some of the consequences of these new insights for debates on embodied cognition, and offer some suggestions for future studies. (shrink)

Is it not surprising that we look with so much pleasure and emotion at works of art that were made thousands of years ago? Works depicting people we do not know, people whose backgrounds are usually a mystery to us, who lived in a very different society and time and who, moreover, have been ‘frozen’ by the artist in a very deliberate pose. It was the Classical Greek philosopher Aristotle who observed in his Poetics that people could apparently be moved (...) even by the imitation of a person or an act. And although we are usually well aware that it is a simulacrum, not a real situation, it nevertheless sometimes seems as if we ourselves are standing there on the stage or in the painting, so intense and emotional is our response, even though we are just spectators. Aristotle concludes from this that we have intellectual capacities which allow us put ourselves in another’s place and consequently to react to simulated situations as though they are actually happening to us, here and now. In this process, he contends, observation, memory, imagination and emotions are crucial elements. In the past it was not customary to invoke human mental faculties to explain our response to works of art. The Ancient Greeks, after all, knew little about the human body or brain and usually referred to the extended world of the gods in their endeavours to comprehend the ‘inner world’ of human beings. In our time the situation is completely different—such an allusion to the brain no longer surprises us. Whether it is about the mystery of the consciousness, the question of free will or accounts of bizarre psychological aberrations or disorders, we have become accustomed to references to parts of the brain, to images of brain scans, to reports about neural networks and the like. However, because there are so many factors that play a part in our appreciation of works of art we need a complex explanation for it, and it is not enough to look only at certain properties of the brain that are determined by evolution. Those properties are shared by every human being, and so are rather useless in explaining people’s different reactions to the same work of art. Evidently the brains of individuals differ so much that they make it possible for people to respond differently to one and the same artwork. This, of course, raises questions concerning the painted emotions that can be seen in this exhibition. Virtually everyone, after all, is fascinated by such paintings and usually recognizes the emotions they represent. The reactions to these painted emotions are also often similar. This is probably why artworks like this are generally highly valued, then and now, here and elsewhere: from the enigmatically smiling Egyptian Queen Nefertiti and the startled Rembrandt to a seemingly despairing African mask. Aristotle observed that in the theatre players imitate actions that are associated with emotions in a number of ways and that these emotions are shared in a particular fashion by the playwright, the actors and the audience. The audience may even be carried away by these emotions to such an extent that they are in a sense purged of them and can subsequently leave the theatre relieved. Are such emotional reactions perhaps related to the fact that emotions are universal and that brains respond similarly to them? Is this why we can so readily identify painted emotions? May we therefore also assume that the properties of the brain determined by evolution help us to explain these emotions? In answering these questions we shall discuss a number of insights into emotions in psychology and brain science and explore some theories about the possible function of emotions and their expression. (shrink)

Recent research suggests that human memories are stored not between neurons as synaptic weights, but within individual neurons themselves. This opens the possibility to replace the dominant paradigm of brain function – neural networks – with a new one. In this article, I explore how “identified neurons” could explain how memories are stored, and how human traits are implemented in the brain.

Despite the huge and constant progress in the molecular and cellular neuroscience fields, our capability to understand brain alterations and treat mental illness is still limited. Therefore, a paradigm shift able to overcome such limitation is warranted. Behavior and the associated mental states are the interface between the central nervous system and the living environment. Since, in any system, the interface is a key regulator of system organization, behavior is proposed here as a unique and privileged level of control (...) and orchestration of brain structure and activity. This view has relevant scientific and clinical implications. First, the study of behavior represents a singular starting point for the investigation of neural activity in an integrated and comprehensive fashion. Second, behavioral changes, accomplished through psychotherapy or environmental interventions, are expected to have the highest impact to specifically reorganize the complexity of the human mind and thus achieve a solid and long-lasting improvement in mental health. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I (...) differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

The nucleus accumbens orchestrates processes related to reward and pleasure, including the addictive consequences of repeated reward (e.g., drug addiction and compulsive gambling) and the accompanying feelings of craving and anhedonia. The neurotransmitters dopamine and endogenous opiates play interactive roles in these processes. They are released by natural rewards (i.e., food, water, sex, money, play, etc.) and are released or mimicked by drugs of abuse. Repeated drug use induces conditioned down-regulation of these neurotransmitters, thus causing painful suppression of everyday pleasure. (...) As with many spiritual traditions, Buddhism provides strong advice against the pursuit of worldly pleasures to attain the ‘‘good life.’’ In contrast, many forms of meditation give rise to an immense and abiding joy. Most of these practices involve ‘‘stilling the mind,’’ whereby all content-laden thought (e.g., fantasies, daydreams, plans) ceases, and the mind enters a state of openness, formlessness, clarity, and bliss. This can be explained by the Buddhist suggestion that almost all of our everyday thoughts are a form of addiction. It follows that if we turn off this internal ‘‘gossip of ego,’’ we will find relief from the biochemical dopamine/opiate down-regulation, which is, perhaps, the perpetual concomitant of our daily rumination. (shrink)

What does it mean to be an aesthetic beholder? Is it different than simply being a perceiver? Most theories of aesthetic perception focus on 1) features of the perceived object and its presentation or 2) on psychological evaluative or emotional responses and intentions of perceiver and artist. In this chapter I propose that we need to look at the process of engaged perception itself, and further that this temporal process of be- coming a beholder must be understood in its embodied, (...) contextual and dynamic speci- ficity. Through both phenomenological and neuroscientific explorations I analyze what is characteristic about a more “aesthetic stance” and argue that there is a certain asym- metry between beholder and beheld, which has to do with a disengagement of goal- directed action, and which allows for other kinds of perceptual involvement than in a more “practical stance”. It is a multi-disciplinary project integrating a sensorimotor no- tion of aesthetic affordances, 18th century philosophy, and large-scale brain network findings. What ensues is a new dynamic framework for future empirical and theoretical research on aesthetic perception. (shrink)

In The Entangled Brain, Pessoa criticizes standard approaches in cognitive neuroscience in which the brain is seen as a functionally decomposable, modular system with causal operations built up hierarchically. Instead, he advocates for an emergentist perspective whereby dynamic brain networks are associated, not with traditional psychological categories, but with behavioral functions characterized in evolutionary terms. Here, we raise a number of concerns with such a radical approach. We ultimately believe that while much revision to cognitive neuroscience (...) is welcome and needed, Pessoa’s more radical proposals may be counterproductive. (shrink)