A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the (...) development of the neuromuscular junction is proposed and the basic selective aspect of learning emphasized. (shrink)

■ Evidence from functional neuroimaging studies on resting state suggests that there are two distinct anticorrelated cortical systems that mediate conscious awareness: an “extrinsic” system that encompasses lateral fronto-parietal areas and has been linked with processes of external input (external awareness), and an “intrinsic” system which encompasses mainly medial brain areas and..

AimResting state functional magnetic resonance imaging was used to analyze changes in functional connectivity within various brain networks and functional network connectivity among various brain regions in patients with high myopia.Methodsrs-fMRI was used to scan 82 patients with HM and 59 healthy control volunteers matched for age, sex, and education level. Fourteen resting state networks were extracted, of which 11 were positive. Then, the FCs and FNCs of RSNs in HM patients were examined by independent component analysis.ResultsCompared with the (...) HC group, FC in visual network 1, dorsal attention network, auditory network 2, visual network 3, and sensorimotor network significantly increased in the HM group. FC in default mode network 1 significantly decreased. Furthermore, some brain regions in default mode network 2, default mode network 3, auditory network 1, executive control network, and significance network increased while others decreased. FNC analysis also showed that the network connection between the default mode network and cerebellar network was enhanced in the HM group.ConclusionCompared with HCs, HM patients showed neural activity dysfunction within and between specific brain networks, particularly in the DMN and CER. Thus, HM patients may have deficits in visual, cognitive, and motor balance functions. (shrink)

Myelin is required for efficient nerve conduction, but not all axons are myelinated to the same extent. Here we review recent studies that have revealed distinct myelination patterns of different axonal paths, suggesting that myelination is not an all or none phenomenon and that its presence is finely regulated in central nervous system networks. Whereas powerful reductionist biology has led to important knowledge of how oligodendrocytes function by themselves, little is known about their role in neuronal networks. We still (...) do not understand how oligodendrocytes integrate information from neurons to adapt their function to the need of the system. An intricate cross talk between neurons and glia is likely to exist and to determine how neuronal circuits operate as a whole. Dissecting these mechanisms by using integrative systems biology approaches is one of the major challenges ahead. (shrink)

This book tackles the question of why the brain is so difficult to fully understand. In neuroscience, data are acquired and analyzed with astonishing techniques and accumulate rapidly. Nevertheless, try to explain how a person can think or why there is such a condition as schizophrenia, and it appears that we really know little. To approach these difficulties, the authors first present a number of case studies in which the operation of a neural circuit is worked out in some detail (...) and, at the same time, the functional significance of the operation is also understood. These examples are complicated in their biologic specifics but are conceptually straightforward. The examples are hoped to provoke an appreciation for what neuroscience can accomplish. The authors then develop some thoughts on how these issues can be addressed----instead of considering cognition in general, taking instead a subset of cognition that does lend itself to formal description. (shrink)

Recently, we proposed a fundamental subdivision of the human cortex into two complementary networks—an “extrinsic” one which deals with the external environment, and an “intrinsic” one which largely overlaps with the “default mode” system, and deals with internally oriented and endogenous mental processes. Here we tested this hypothesis by contrasting decision making under external and internally-derived conditions. Subjects were presented with an external cue, and were required to either follow an external instruction or to ignore it and follow a voluntary (...) decision process . Our results show that a well defined component of the intrinsic system—the right inferior parietal cortex—was preferentially activated during the “free-will” condition. Importantly, this activity was significantly higher than the base-line resting state. The results support a self-related role for the intrinsic system and provide clear evidence for both hemispheric and regional specialization in the human intrinsic system. (shrink)

This study investigated the neuromodulatory effects of transspinal stimulation on soleus H-reflex excitability and electromyographic activity during stepping in humans with and without spinal cord injury. Thirteen able-bodied adults and 5 individuals with SCI participated in the study. EMG activity from both legs was determined for steps without, during, and after a single-pulse or pulse train transspinal stimulation delivered during stepping randomly at different phases of the step cycle. The soleus H-reflex was recorded in both subject groups under control conditions (...) and following single-pulse transspinal stimulation at an individualized exactly similar positive and negative conditioning-test interval. The EMG activity was decreased in both subject groups at the steps during transspinal stimulation, while intralimb and interlimb coordination were altered only in SCI subjects. At the steps immediately after transspinal stimulation, the physiological phase-dependent EMG modulation pattern remained unaffected in able-bodied subjects. The conditioned soleus H-reflex was depressed throughout the step cycle in both subject groups. Transspinal stimulation modulated depolarization of motoneurons over multiple segments, limb coordination, and soleus H-reflex excitability during assisted stepping. The soleus H-reflex depression may be the result of complex spinal inhibitory interneuronal circuits activated by transspinal stimulation and collision between orthodromic and antidromic volleys in the peripheral mixed nerve. The soleus H-reflex depression by transspinal stimulation suggests a potential application for normalization of spinal reflex excitability after SCI. (shrink)

A new strategy for moving forward in the characterization of the Global Neuronal Workspace (GNW) is proposed. According to Dehaene, Changeux and colleagues, broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph-theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph-theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern (...) of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network. (shrink)

A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to a Łukasiewicz–Moisil (...) Topos with an n-valued Łukasiewicz–Moisil Algebraic Logic subobject classifier description that represents non-random and non-linear network activities as well as their transformations in developmental processes and carcinogenesis. The unification of the theories of organismic sets, molecular sets and Robert Rosen’s (M,R)-systems is also considered here in terms of natural transformations of organismal structures which generate higher dimensional algebras based on consistent axioms, thus avoiding well known logical paradoxes occurring with sets. Quantum bionetworks, such as quantum neural nets and quantum genetic networks, are also discussed and their underlying, non-commutative quantum logics are considered in the context of an emerging Quantum Relational Biology. (shrink)

A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to a Łukasiewicz–Moisil (...) Topos with an n-valued Łukasiewicz–Moisil Algebraic Logic subobject classifier description that represents non-random and non-linear network activities as well as their transformations in developmental processes and carcinogenesis. The unification of the theories of organismic sets, molecular sets and Robert Rosen’s (M,R)-systems is also considered here in terms of natural transformations of organismal structures which generate higher dimensional algebras based on consistent axioms, thus avoiding well known logical paradoxes occurring with sets. Quantum bionetworks, such as quantum neural nets and quantum genetic networks, are also discussed and their underlying, non-commutative quantum logics are considered in the context of an emerging Quantum Relational Biology. (shrink)

The main idea -- The functioning of a neuron -- Brain structure and function -- The general structure of the neural network -- Instincts, emotions, free will -- The nature of mental objects -- The rise and essence of (self-)consciousness -- Artificial intelligence -- Cognitive limitations of man.

This paper reports on the investigation of the effects of neuronal shape, at both individual cell and network level, on the behavior of neuronal systems. More specifically, two-dimensional biologically realistic neuronal networks are obtained that take explicity into account the position and morphology of neuronal cells, with the respective behavior for associative recall being simulated through a diluted version of Hopfield's model. While a specific probability density function is used for the placement of the cell (...) bodies, images of real neuronal cells (namely alpha and beta ganglion cells from the cat retina) are used to obtain biologically realistic models. Several morphological measures – including fractal dimension, the excluded volume, and integral geometry functionals–are estimated for the considered cells, and their values are correlated with the potential of the network for associative recall, which is quantified in terms of the overlap between distorted version of the trained patterns and their original version. Such an approach allows the quantitative and objective characterization of the relationship between neuronal shape and function, an important issue in neuroscience. The obtained results substantiate interesting relationships between the neural morphology and function as determined by the performance of the network. (shrink)

Impossible Minds: My Neurons, My Consciousness has been written to satisfy the curiosity each and every one of us has about our own consciousness. It takes the view that the neurons in our heads are the source of consciousness and attempts to explain how this happens. Although it talks of neural networks, it explains what they are and what they do in such a way that anyone may understand. While the topic is partly philosophical, the text makes no assumptions of (...) prior knowledge of philosophy; and so contains easy excursions into the important ideas of philosophy that may be missing in the education of a computer scientist. The approach is pragmatic throughout; there are many references to material on experiments that were done in our laboratories. The first edition of the book was written to introduce curious readers to the way that the consciousness we all enjoy might depend on the networks of neurons that make up the brain. In this second edition, it is recognized that these arguments still stand, but that they have been taken much further by an increasing number of researchers. A post-script has now been written for each chapter to inform the reader of these developments and provide an up-to-date bibliography. A new epilogue has been written to summarize the state-of-the art of the search for consciousness in neural automata, for researchers in computation, students of philosophy, and anyone who is fascinated by what is one of the most engaging scientific endeavours of the day. This book also tells a story. A story of a land where people think that they are automata without much in the way of consciousness, a story of cormorants and cliffs by the sea, a story of what it might be like to be a conscious machine... (shrink)

Traditionally, the impairment of cognitive functions in Alzheimeŕs disease (AD) is thought to result from a reduction in neuronal and synaptic activities, and ultimately cell death. Here, we review recent in vivo evidence from mouse models and human patients indicating that, particularly in early stages of AD, neuronal circuits are hyperactive instead of hypoactive. Functional analyses at many levels, from single neurons to neuronal populations to large‐scale networks, with a variety of electrophysiological and imaging techniques have revealed (...) two forms of AD‐related hyperactivity and provided first insights into the synaptic mechanisms. The unexpected finding that hyperactivity is an early neuronal dysfunction represents a major conceptual shift in our understanding of AD that may have important implications for the development of therapeutic approaches. (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.

Registration of a series of the two-dimensional electron microscope images of the brain tissue into volumetric form is an important technique that can be used for neuronal circuit reconstruction. However, complex appearance changes of neuronal morphology in adjacent sections bring difficulty in finding correct correspondences, making serial section neural image registration challenging. To solve this problem, we consider whether there are such stable "markers" in the neural images to alleviate registration difficulty. In this paper, we employ the spherical (...) deformation model to simulate the local neuron structure and analyze the relationship between registration accuracy and neuronal structure shapes in two adjacent sections. The relevant analysis proves that regular circular structures in the section images are instrumental in seeking robust corresponding relationships. Then, we design a new serial section image registration framework driven by this neuronal morphological model, fully utilizing the characteristics of the anatomical structure of nerve tissue and obtaining more reasonable corresponding relationships. Specifically, we leverage a deep membrane segmentation network and neural morphological physical selection model to select the stable rounded regions in neural images. Then, we combine feature extraction and global optimization of correspondence position to obtain the deformation field of multiple images. Experiments on real and synthetic serial EM section neural image datasets have demonstrated that our proposed method could achieve more reasonable and reliable registration results, outperforming the state-of-the-art approaches in qualitative and quantitative analysis. (shrink)

This paper reports on the investigation of the effects of neuronal shape, at both individual cell and network level, on the behavior of neuronal systems. More specifically, two-dimensional biologically realistic neuronal networks are obtained that take explicity into account the position and morphology of neuronal cells, with the respective behavior for associative recall being simulated through a diluted version of Hopfield's model. While a specific probability density function is used for the placement of the cell (...) bodies, images of real neuronal cells (namely alpha and beta ganglion cells from the cat retina) are used to obtain biologically realistic models. Several morphological measures – including fractal dimension, the excluded volume, and integral geometry functionals–are estimated for the considered cells, and their values are correlated with the potential of the network for associative recall, which is quantified in terms of the overlap between distorted version of the trained patterns and their original version. Such an approach allows the quantitative and objective characterization of the relationship between neuronal shape and function, an important issue in neuroscience. The obtained results substantiate interesting relationships between the neural morphology and function as determined by the performance of the network. (shrink)

The fan effect says that “activation” spreading from a concept is divided among the concepts it spreads to. Because this activation is not a physical entity, but an abstraction of unknown lower‐level processes, the spreading activation model has predictive but not explanatory power. We provide one explanation of the fan effect by showing that distributed neuronal memory networks (specifically, Hopfield networks) reproduce four qualitative aspects of the fan effect: faster recognition of sentences containing lower‐fan words, faster recognition of sentences (...) when more cues are provided, faster acceptance of studied sentences than rejection of probes, and faster recognition of sentences studied more frequently. These are all a natural result of the dynamics of distributed associative memory. (shrink)

The recent emergence of reprogramming technologies to convert brain cell types or epigenetically alter neurons and neural progenitors in vivo and in situ hold significant promises in brain repair and neuronal aging reversal. However, given the significant epigenetic and transcriptomic changes to components of the existing neuronal cells and network, we question if these reprogramming technology might inadvertently alter or erase memory engrams, conceivably resulting in changes in narrative identity or personality. We suggest that the nature of (...) these alterations might be less predictable compared to memory and personality changes known to be associated with diseases, drugs or brain stimulation therapies. While research in applying reprogramming technologies to neurological ailments and aging should continue, more targeted analyses should be put in place in animal experiments to gauge the severity and degree of memory alterations, and appropriate risk and benefit analyses should be conducted before these technologies move into human trials. (shrink)

This paper reviews the evidence, from studies of acute denervation plasticity, that NCCs in the sensory cortex are composed of particular patterns of intracolumnar excitation in a certain type of neuron, and not of specific anatomically identified neurons. This leads to an enquiry as to what the microneurological basis of NCCs in general may be. Further evidence is examined as to the possible NCCs of the stroboscopic patterns. The hypotheses are presented that the geometrical bright phase patterns arise as dissipative (...) structures generated by interacting cortical rhythms; whereas the non-geometrical dark phase patterns have as their NCCs dendritic potentials in the GABAergic gap-junction linked network in layers II and III of the visual cortex. (shrink)

A wide range of systems appear to perform computation: what common features do they share? I consider three examples, a digital computer, a neural network and an analogue route finding system based on soap-bubbles. The common feature of these systems is that they have autonomous dynamics — their states will change over time without additional external influence. We can take advantage of these dynamics if we understand them well enough to map a problem we want to solve onto them. (...) Programming consists of arranging the starting state of a system so that the effects of the system''s dynamics on some of its variables corresponds to the effects of the equations which describe the problem to be solved on their variables. The measured dynamics of a system, and hence the computation it may be performing, depend on the variables of the system we choose to attend to. Although we cannot determine which are the appropriate variables to measure in a system whose computation basis is unknown to us I go on to discuss how grammatical classifications of computational tasks and symbolic machine reconstruction techniques may allow us to rule out some measurements of a system from contributing to computation of particular tasks. Finally I suggest that these arguments and techniques imply that symbolic descriptions of the computation underlying cognition should be stochastic and that symbols in these descriptions may not be atomic but may have contents in alternative descriptions. (shrink)

Time is an essential feature in bipolar disorder (BP). Manic and depressed BP patients perceive the speed of time as either too fast or too slow. The present article combines theoretical and empirical approaches to integrate phenomenological, psychological, and neuroscientific accounts of abnormal time perception in BP. Phenomenology distinguishes between perception of inner time, ie, self-time, and outer time, ie, world-time, that desynchronize or dissociate from each other in BP: inner time speed is abnormally slow (as in depression) or fast (...) (as in mania) and, by taking on the role as default-mode function, impacts and modulates the perception of outer time speed in an opposite way, ie, as too fast in depression and too slow in mania. Complementing, psychological investigation show opposite results in time perception, ie, time estimation and reproduction, in manic and depressed BP. Neuronally, time speed can be indexed by neuronal variability, ie, SD. Our own empirical data show opposite changes in manic and depressed BP (and major depressive disorder [MDD]) with abnormal SD balance, ie, SD ratio, between somatomotor and sensory networks that can be associated with inner and outer time. Taken together, our combined theoretical-empirical approach demonstrates that desynchronization or dissociation between inner and outer time in BP can be traced to opposite neuronal variability patterns in somatomotor and sensory networks. This opens the door for individualized therapeutic “normalization” of neuronal variability pattern in somatomotor and sensory networks by stimulation with TMS and/or tDCS. (shrink)

This book is a colorful journey into the fascinatingly diverse world of interneurons, an important class of highly heterogeneous cells found in all cortical neuronal networks. Interneurons are known to play key roles in many brain functions, from sensory processing to neuronal oscillations linked to learning and memory. The central aim of the volume is to provide new insights into the striking degree of cellular diversity found in interneuronal microcircuits. The book discusses the history of research into interneuronal (...) variability, the developmental origins of interneuronal diversity, the functional roles of heterogeneity in neuronal circuits, contemporary interneuronal classification systems, and the genetic and homeostatic mechanisms that shape the degree of cell to cell variability within interneuronal populations. It elaborates on new ideas about interneuronal diversity that rest upon theoretical and experimental results, with arguments touching upon evolution, animal behavior, and the mathematical theory of small world networks. This engaging volume is invaluable to neuroscientists and others interested in how neuronal newtworks function; electrical engineers, computational modelers, and physicists interested in neuronal network theory; neurologists and psychiatrists working on mechanisms of neurological and psychiatric disorders; and students and trainees in all of these fields. (shrink)

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

Some properties of cognitive networks are discussed in the article in the context of the modern achievements of the network science. It is the study in network structures and their surprising properties that gives a new impetus to the development of the theory of complex systems. The analysis of cognitive processes from the point of view of the network structures that arise in them not only fits with such concepts already existing in cognitive science and epistemology, as (...) cognitive niches, cognitive maps, cognitive coherence, etc.), but also brings some new aspects to the understanding of interactivity, intersubjectivity, synergy in cognition and creative activities, empathy. (shrink)

Objective Manic and depressive phases of bipolar disorder (BD) show opposite psychomotor symptoms. Neuronally, these may depend on altered relationships between sensorimotor network (SMN) and subcortical structures. The study aimed to investigate the functional relationships of SMN with substantia nigra (SN) and raphe nuclei (RN) via subcortical-cortical loops, and their alteration in bipolar mania and depression, as characterized by psychomotor excitation and inhibition. -/- Method In this resting-state functional magnetic resonance imaging (fMRI) study on healthy (n = 67) and (...) BD patients (n = 100), (1) functional connectivity (FC) between thalamus and SMN was calculated and correlated with FC from SN or RN to basal ganglia (BG)/thalamus in healthy; (2) using an a-priori-driven approach, thalamus-SMN FC, SN-BG/thalamus FC, and RN-BG/thalamus FC were compared between healthy and BD, focusing on manic (n = 34) and inhibited depressed (n = 21) patients. -/- Results (1) In healthy, the thalamus-SMN FC showed a quadratic correlation with SN-BG/thalamus FC and a linear negative correlation with RN-BG/thalamus FC. Accordingly, the SN-related FC appears to enable the thalamus-SMN coupling, while the RN-related FC affects it favoring anti-correlation. (2) In BD, mania showed an increase in thalamus-SMN FC toward positive values (ie, thalamus-SMN abnormal coupling) paralleled by reduction of RN-BG/thalamus FC. By contrast, inhibited depression showed a decrease in thalamus-SMN FC toward around-zero values (ie, thalamus-SMN disconnection) paralleled by reduction of SN-BG/thalamus FC (and RN-BG/thalamus FC). The results were replicated in independent HC and BD datasets. -/- Conclusions These findings suggest an abnormal relationship of SMN with neurotransmitters-related areas via subcortical-cortical loops in mania and inhibited depression, finally resulting in psychomotor alterations. (shrink)

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or (...) not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements. We propose that the patterns of presence or absence of axons and dendrites within known anatomical parcels may serve as the key principle to define neuron types. Just as the positions (...) of the elements in the periodic table indicate their potential to combine into molecules, axonal and dendritic distributions provide the blueprint for network connectivity. Furthermore, among the features commonly employed to describe neurons, morphology is considerably robust to experimental conditions. At the same time, this core classification scheme is suitable for aggregating biochemical, physiological, and synaptic information. (shrink)

According to the global neuronal workspace model of consciousness, consciousness results from the global broadcast of information throughout the brain. The global neuronal workspace is mainly constituted by a fronto-parietal network. The anterior insular cortex is part of this global neuronal workspace, but the function of this region has not yet been defined within the global neuronal workspace model of consciousness. In this review, I hypothesize that the anterior insular cortex implements a cross-modal priority map, (...) the function of which is to determine priorities for the processing of information and subsequent entrance in the global neuronal workspace. (shrink)