Researchers are enthusiastically concerned about neural stem cell (NSC) therapy in a wide array of diseases, including stroke, neurodegenerative disease, spinal cord injury (SCI) and depression. Although enormous evidences have demonstrated that neurobehavioral improvement may benefit from NSC-supporting regeneration in animal models, approaches to endogenous and transplanted NSCs are blocked by hurdles of migration, proliferation, maturation and integration of NSCs. Electrical stimulation (ES) may be a selective nondrug approach for mobilizing NSCs in the central nervous system (CNS). This (...) technique is suitable for clinic application, because it is well established and its potential complications are manageable. Here, we provide a comprehensive review of the emerging positive role of different electrical cues in regulating NSC biology in vitro and in vivo, as well as biomaterial-based and chemical stimulation of NSCs. In the future, ES combined with stem cell therapy or other cues probably becomes an approach for promoting brain repair. (shrink)

There is a growing recognition, stemming from work with both vertebrates and invertebrates, that the capacity for neuronal regeneration is critically dependent on the local microenvironment. That environment is largely created by the non‐neuronal elements of the nervous system, the neuroglia. Therefore an understanding of how glial cells respond to injury is crucial to understanding neuronal regeneration. Here we examine the process of repair in a relatively simple nervous system, that of the insect, in which it is possible (...) to define precisely the cellular events of the repair process. This repair is rapid and well organised; it involves the recruitment of blood cells, the division of endogenous glial elements and, possibly, migration from pre‐existing glial pools in adjacent ganglia. There are clear parallels between the events of repair and those of normal glial development. It seems likely that the ability of the insect central nervous system to repair resides in the retention of developmental capacities throughout its life and that damage results in the activation of this potential. (shrink)

Evolution exploits the physics of non‐neural bioelectricity to implement anatomical homeostasis: a process in which embryonic patterning, remodeling, and regeneration achieve invariant anatomical outcomes despite external interventions. Linear “developmental pathways” are often inadequate explanations for dynamic large‐scale pattern regulation, even when they accurately capture relationships between molecular components. Biophysical and computational aspects of collective cell activity toward a target morphology reveal interesting aspects of causation in biology. This is critical not only for unraveling evolutionary and developmental events, but (...) also for the design of effective strategies for biomedical intervention. Bioelectrical controls of growth and form, including stochastic behavior in such circuits, highlight the need for the formulation of nuanced views of pathways, drivers of system‐level outcomes, and modularity, borrowing from concepts in related disciplines such as cybernetics, control theory, computational neuroscience, and information theory. This approach has numerous practical implications for basic research and for applications in regenerative medicine and synthetic bioengineering. (shrink)

A prominent view on personal identity over time, Jeff McMahan’s ‘Embodied Mind Account’ (2002) holds that we cease to exist only once our brains can no longer sustain the basic capacity to uphold consciousness. One of the many implications of this view on identity persistence is that we continue to exist throughout even the most severe cases of dementia, until our consciousness irreversibly shuts down. In this paper, I argue that, while the most convincing of prominent accounts of personal identity (...) over time, McMahan’s account faces serious challenges in explanatory power of dementias and related neurodegenerative conditions. Particularly, this becomes visible in the face of emerging methods for neural tissue regeneration, and the possibility of ‘re-emerging patients’. I argue that medical professionals’ neglecting qualitative aspects of identity risks resulting in grave misunderstandings in decision-making processes, and ethically objectionable outcomes in future practices. Finally, I propose revisions which could potentially salvage the great benefits that Embodied Mind Theory still can bring to the field of dementia care in terms of understanding life, death, and identity across the lifespan. (shrink)

All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, molecular networks) that are themselves complex and competent in their own context. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger (...) organism. In this paper I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: “Scale-Free Cognition”. I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization, and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface – the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive “light cone” which defines the scale and limits of its cognition. I hypothesize that higher-level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress – the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggests a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology. (shrink)

Both blood vessels and nerves are guided to their target. Vascular endothelial growth factor (VEGF)A is a key signal in the induction of vessel growth (a process termed angiogenesis). Though initial studies, now a decade ago, indicated that VEGF is an endothelial cell‐specific factor, more recent findings revealed that VEGF also has direct effects on neural cells. Genetic studies showed that mice with reduced VEGF levels develop adult‐onset motor neuron degeneration, reminiscent of the human neurodegenerative disorder amyotrophic lateral sclerosis (...) (ALS). Additional genetic studies confirmed that VEGF is a modifier of motor neuron degeneration in humans and in SOD1G93A mice—a model of ALS. Reduced VEGF levels may promote motor neuron degeneration by limiting neural tissue perfusion and VEGF‐dependent neuroprotection. VEGF also affects neuron death after acute spinal cord or cerebral ischemia, and has also been implicated in other neurological disorders such as diabetic and ischemic neuropathy, nerve regeneration, Parkinson's disease, Alzheimer's disease and multiple sclerosis. These findings have raised growing interest in assessing the therapeutic potential of VEGF for neurodegenerative disorders. BioEssays 26:943–954, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Contemplative scholarship has recently reoriented attention towards the neuroscientific study of the soteriological ambition of Buddhist practice, 'awakening'. This article evaluates the project of seeking neural correlates for awakening. Key definitional and operational issues are identified demonstrating that: the nature of awakening is highly contested both within and across Buddhist traditions; the meaning of awakening is both context- and concept-dependent; and awakening may be non-conceptual and ineffable. It is demonstrated that operationalized secular conceptions of awakening, divorced from soteriological and (...) cultural factors, have little relationship to traditional Buddhist construct(s) of awakening. This article identifies methodological issues for secular conceptions of awakening concerning introspection and neuroimaging yet demonstrates also the value of recent advancements in empirical first-person phenomenology for attenuating introspective bias. Overall, it is contended that significant problems arise when decontextualizing awakening and placing it within a scientific naturalistic framework. Careful attention to the definitional, operational, and methodological neuroscientific obstacles identified herein is required in the responsible approach to the investigation of awakening states. (shrink)

In this book, Matej Kohar demonstrates how the new mechanistic account of explanation can be used to support a non-representationalist view of explanations in cognitive neuroscience, and therefore can bring new conceptual tools to the non-representationalist arsenal. Kohar focuses on the explanatory relevance of representational content in constitutive mechanistic explanations typical in cognitive neuroscience. The work significantly contributes to two areas of literature: 1) the debate between representationalism and non-representationalism, and 2) the literature on mechanistic explanation. Kohar begins with an (...) introduction to the mechanistic theory of explanation, focusing on the analysis of mechanistic constitution as the basis of explanatory relevance in constitutive mechanistic explanation. He argues that any viable analysis of representational contents implies that content is not constitutively relevant to cognitive phenomena. The author also addresses objections against his argument and concludes with an examination of the consequences of his account for both traditional cognitive neuroscience and non-representationalist alternatives. This book is of interest to readers in philosophy of mind, cognitive science and neuroscience. (shrink)

Based on the Zen philosophy about focusing away from the self, a guide to "neural Zen" meditative practices draws on recent findings in brain research to outline recommendations for various methods of pursuing a balanced, selfless state of ...

Neural language models (NLMs) are susceptible to producing inconsistent output. This paper proposes a new diagnosis as well as a novel remedy for NLMs' incoherence. We train NLMs on synthetic text corpora that are created by simulating text production in a society. For diagnostic purposes, we explicitly model the individual belief systems of artificial agents (authors) who produce corpus texts. NLMs, trained on those texts, can be shown to aggregate the judgments of individual authors during pre-training according to sentence-wise (...) vote ratios (roughly, reporting frequencies), which inevitably leads to so-called discursive dilemmas: aggregate judgments are inconsistent even though all individual belief states are consistent. As a remedy for such inconsistencies, we develop a self-training procedure—inspired by the concept of reflective equilibrium—that effectively reduces the extent of logical incoherence in a model's belief system, corrects global mis-confidence, and eventually allows the model to settle on a new, epistemically superior belief state. Thus, social choice theory helps to understand why NLMs are prone to produce inconsistencies; epistemology suggests how to get rid of them. (shrink)

A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network model. It includes a linear matrix, a sine function, and a chaotic neural network composed (...) of three chaotic neurons. One of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem. (shrink)

One major problem many hypotheses regarding the neural correlate of consciousness, face is what we might call “the why question”: why would this particular neural feature, rather than another, correlate with consciousness? The purpose of the present paper is to develop an NCC hypothesis that answers this question. The proposed hypothesis is inspired by the cross-order integration theory of consciousness, according to which consciousness arises from the functional integration of a first-order representation of an external stimulus and a (...) second-order representation of that first-order representation. The proposal comes in two steps. The first step concerns the “general shape” of the NCC and can be directly derived from COI theory. The second step is a concrete hypothesis that can be arrived at by combining the general shape with empirical considerations. (shrink)

In recent years regenerative agriculture has attracted growing attention as a means to improve soil health and farmer livelihoods while slowing climate change. With this attention has come increased policy support as well as the launch of private sector programs that promote regenerative agriculture as a form of carbon farming. In the United States many of these programs recruit primarily in regions where large-scale commodity production prevails, such as the Great Plains. There, a decades-old regenerative agriculture movement is growing rapidly, (...) but not due to the incentives offered by companies’ carbon programs. On the contrary, farmers are adopting regenerative practices to cut their dependence on corporate agrochemical inputs and expertise, and to thereby achieve technology sovereignty. These practice changes often strain farmers’ existing social relationships while drawing them into new and previously neglected ones, including the more-than-human relations necessary for building soil health. These new relationships and the knowledge they generate may in turn lead farmers to think differently about their own autonomy. These findings provide insight into farmers’ skepticism of private sector carbon farming programs, and highlight the value of attention to the multiple types of relationship change that accompany and facilitate regenerative transitions. (shrink)

Mental and neural states are related to one another by vertical interlevel relations and by horizontal intralevel relations. For particular choices of such relations, problems arise if causal efficacy is ascribed to mental states. In a series of influential papers and books, Kim has presented his much discussed “supervenience argument,” which ultimately amounts to the dilemma that mental states either are causally inefficacious or they hold the threat of overdetermining neural states. Forced by this disjunction, Kim votes in (...) favor of overdetermination and, ultimately, reduction. We propose a perspective on mental causation that dissolves the assumption of a tension between horizontal and vertical determination. For mental states to be causally efficacious, they must be dynamically stable. This important requirement can be implemented by combining a key idea of supervenience, multiple realization, with the recently introduced vertical interlevel relation of contextual emergence. Both together deflate Kim's dilemma and reflate the causal efficacy of mental states. 2012 APA, all rights reserved). (shrink)

The core of this paper is a discussion of how the physical signals of the nervous system acquire significance and meaning on the basis of relationships with each other and with the environment. Signal relations are discussed in terms of coherence , prediction, intentionality, inner reality and meaning. The original and most basic type of signal relation has the form of temporal correlations on coarser or finer time scales, and all other relations must ultimately be built up by the brain (...) from this basis. Consequently, consciousness is the state of coherence of the brain in terms of signal relationships within it and with the environment. In this view, consciousness is of critical functional importance and far from a superfluous epiphenomenon. Understanding the mechanisms by which meaningful signal relationships are established in the brain is of great importance for the future of information technology. (shrink)

This paper is devoted to computer modelling of the development and regeneration of multicellular biological structures. Some species are able to regenerate parts of their body after amputation damage, but the global rules governing cooperative cell behaviour during morphogenesis are not known. Here, we consider a simplified model organism, which consists of tissues formed around special cells that can be interpreted as stem cells. We assume that stem cells communicate with each other by a set of signals, and that (...) the values of these signals depend on the distance between cells. Thus the signal distribution characterizes location of stem cells. If the signal distribution is changed, then the difference between the initial and the current signal distribution affects the behaviour of stem cells—e.g. as a result of an amputation of a part of tissue the signal distribution changes which stimulates stem cells to migrate to new locations, appropriate for regeneration of the proper pattern. Moreover, as stem cells divide and form tissues around them, they control the form and the size of regenerating tissues. This two-level organization of the model organism, with global regulation of stem cells and local regulation of tissues, allows its reproducible development and regeneration. (shrink)

Development in animals is frequently characterized by periods of heightened capacity for both neural and behavioral change. So‐called sensitive periods of development are windows of opportunity in which brain and behavior are most susceptible to modification. Understanding what factors regulate sensitive periods constitutes one of the main goals of developmental neuroscience. Why is the ability to learn complex behavioral patterns often restricted to sensitive periods of development? Songbirds provide a model system for unraveling the mysteries of neural mechanisms (...) of learning during development. Like many songbirds, zebra finches (Taeniopygia guttata) learn a specific vocal pattern during a restricted period early in life. Young birds must hear songs produced by members of their species; this auditory experience is thought to engender specific changes in the brain to guide the process of vocal learning. Many studies of the songbird system have focused on examining relationships between brain development and learning. One goal of this work is to elucidate mechanisms that regulate basic processes of neural development, and in so doing to shed light on factors governing the emergence of a complex learned behavior. (shrink)

The brain-computer interface of steady-state visual evoked potential is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density, we perform zero-padding in the signal's time domain to improve its performance on the PSD and make (...) it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI. (shrink)

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year (...) 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels. (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)

After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a onedimensional continuum and then to sort them into 3 categories, CP arises as (...) a natural side-effect because of four factors: (1) Maximal interstimulus separation in hidden-unit space during autoassociation learning, (2) movement toward linear separability during categorization learning, (3) inverse-distance repulsive force exerted by the between-category boundary, and (4) the modulating effects of input iconicity, especially in interpolating CP to untrained regions of the continuum. Once similarity space has been "warped" in this way, the compressed and separated "chunks" have symbolic labels which could then be combined into symbol strings that constitute propositions about objects. The meanings of such symbolic representations would be "grounded" in the system's capacity to pick out from their sensory projections the object categories that the propositions were about. (shrink)

Associative memory is the ability to link together components of stimuli. Previous evidence suggests that prior familiarization with study items affects the nature of the association between stimuli. More specifically, novel stimuli are learned in a more context-dependent fashion than stimuli that have been encountered previously without the current context. In the current study, we first acquired behavioral data from 62 human participants to conceptually replicate this effect. Participants were instructed to memorize multiple object-scene pairs and were then tested on (...) their recognition memory for the objects. Importantly, 1 day prior, participants had been familiarized with half of the object stimuli. During the test phase, the objects were either matched to the same scene as during study or swapped with a different object’s scene. Our results conceptually replicated the context-dependency effect by showing that breaking up a studied object-context pairing is more detrimental to object recognition performance for non-familiarized objects than for familiarized objects. Second, we used functional magnetic resonance imaging to determine whether medial temporal lobe encoding-related activity patterns are reflective of this familiarity-related context effect. Data acquired from 25 human participants indicated a larger effect of familiarization on encoding-related hippocampal activity for objects presented within a scene context compared to objects presented alone. Our results showed that both retrieval-related accuracy patterns and hippocampal activation patterns were in line with a familiarization-mediated context-dependency effect. (shrink)

Infertility is a global problem affecting 48 to 186 million couples of reproductive age. In Poland, it concerns approx. 1.5 million couples, which amounts to 20% of the population capable of reproducing. One of the factors influencing the incidence of fertility disorders may be lifestyle, understood as a multi-disciplinary accumulation of everyday behaviours and habits. In the study, a group of 201 young adults, students of medical and related faculties, were surveyed in order to check the actual level of knowledge (...) about the impact of lifestyle on reproductive health. The Kohonen network, which is an example of a self-learning neural network, was used to find non-obvious connections between the data. The trained Kohonen neural network formed 4 clusters with different characteristics. Based on analyses of the structure of each cluster, it was found that 2nd year students of Medicine are internally divided into 3 fractions. The first fraction declared a high level of knowledge, but did not have real knowledge. The second fraction was aware of their ignorance, as confirmed by the knowledge test. The last fraction was characterized by a high level of self-confidence regarding their knowledge about reproductive health and obtained a high result in the knowledge test. It was confirmed that people studying at the Medical faculty know more than students of the same year at faculties other than Medicine. Interesting results were obtained for a group of 3rd year students of first-cycle studies in Dietetics. They did not obtain a significantly better result in the knowledge test concerning the influence of diet and lifestyle on reproductive health. It would seem that one could expect at least a few highly knowledgeable students in a group of 3rd year students, but this was not confirmed by the study. In view of the obtained results, it was concluded that the Kohonen neural network is applicable to the analysis of data on the actual state of knowledge about the impact of lifestyle on reproductive health. (shrink)