■ 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..

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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.

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.

The primate brain is able to guide complex decisions that can rapidly be adapted to changing constraints. Unfortunately, the vast numbers of highly interconnected neurons that seem to be needed make it difficult to study the cellular mechanisms that underlie the flexible combination of stored and acute information during a decision. Established simpler networks, particularly with few and identified neurons, would lend themselves more readily to such a dissection. But can simple networks implement complex and flexible decisions similarly? (...) After a brief overview of complex decisions in primates and of decision‐making in simple networks, I argue that simpler systems can combine complexity with accessibility at the cellular level. Indeed, examination of a network in fish may help in dissecting key mechanisms of complex and flexible decision‐making in an established model of synaptic plasticity at the level of identified neurons. (shrink)

Neurons encode and transmit signals through chemical synaptic or electrical synaptic connections in the actual nervous system. Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. From the perspective of biophysics, the activation of magnetic field coupling is the result of the continuous release and propagation of intracellular and extracellular ions, which is very similar to the activation of chemical synaptic coupling through (...) the continuous release of neurotransmitters. In this article, an induction coil is used to connect two HR neurons to stimulate the effect of magnetic field coupling. It is inevitable that time delays can affect the coupling process in the transmission of information, and it should be considered in the coupled model. Firstly, the firing characteristics and bifurcation modes of two coupled HR neurons are studied by using one parameter and two parameters bifurcation. With the increase of propagation delay and coupling gain, the chaotic state of neurons disappears and the high-period window decreases due to the influence of energy transfer between neurons. Then, the synchronization patterns of two HR neurons with different stimulation are analyzed by error diagrams and time series diagrams. It is confirmed that the synchronous pattern has certain regularity and is related not only to the neurons with large stimulation current but also to the time delay and coupling gain. The research conclusions of this article will provide the corresponding theoretical basis for medical experiments. (shrink)

While univariate functional magnetic resonance imaging data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal (...) class='Hi'>networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models. (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)

This article proposes a neurobiologically motivated theory of meaning as internal representation that holds on to the principle of compositionality, but negates the principle of semantic constituency. The approach builds on neurobiological findings regarding topologically structured cortical feature maps and the mechanism of object-related binding by neuronal synchronization. It incorporates the Gestalt principles of psychology and is implemented by recurrent neural networks. The semantics to be developed is structurally analogous to some variant of model-theoretical semantics. The semantics to (...) be developed is a neuro-emulative model-theoretical semantics of a first-order language. The model-theoretical semantics is merely denotational and does not imply anything about the structures of the mind or the underlying neural mechanisms that enable producing and comprehend meaningful expressions. The relation between a mental representation and its content is some form of causal-informational covariation. (shrink)

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses (...) spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

In their account of learning and behavior, the authors define an interactor as emitted behavior that operates on the environment, which excludes Pavlovian learning. A unified neural-network account of the operant-Pavlovian dichotomy favors interpreting neurons as interactors and synaptic efficacies as replicators. The latter interpretation implies that single-synapse change is inherently Lamarckian.

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of (...) recent reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE. The network core triggers fast responses to previously experienced stimuli. Innovations require the network periphery that encodes a novel attractor by core-remodeling. This core-periphery learning theory is introduced as a general adaptation, learning, decision-making, and forgetting mechanism of all complex systems including protein, metabolic, signaling, neuronal, social networks, and ecosystems. (shrink)

Neuronal signalling involves multiple neuropeptides that are diverse in structure and function. Complex patterns of tissue‐specific expression arise from alternate RNA splicing of neuropeptide‐encoding gene transcripts. The pattern of expression and its role in cell signalling is diffecult to study at the level of single neurons in the complex vertebrate brain. However, in the model molluscan system, Lymnaea, it is possible to show that alternate mRNA expression of the FMRFamide gene is specific to single identified neurons. Two different transcripts (...) are expressed in a mutually exclusive manner in different neurons. Post‐translational processing of the two precursor proteins leads to completely distinct sets of neuropeptide transmitters. The function of these transmitter cocktails, resulting from alternate mRNA splicing, was studied physiologically in identified neurons forming part of a behaviourally important network regulating heartbeat. (shrink)