The connectivity patterns of many neural circuits are highly ordered and often impressively complex. The intricate order and complexity of neuronal wiring remain not only a challenge for questions related to circuit functions but also for our understanding of how they develop with such an apparent precision. The chemotropic guidance of the growing axon by target‐derived cues represents a central paradigm for how neurons get connected with the correct target cells. However, many studies reveal a remarkable variety of important (...) target‐independent wiring mechanisms. These mechanisms include axonal sorting, axonal tiling, growth cone polarization, as well as cell‐intrinsic mechanisms underlying growth cone sprouting, and neurite branching. Our review focuses on target independent wiring mechanisms and in particular on recent progress emerging from studies on three different sensory systems: olfactory, visual, and somatosensory. We discuss molecular mechanisms that operate during axon‐axon interactions or constitute axon‐intrinsic functions and outline how they complement the well‐known target‐dependent wiring mechanisms. (shrink)

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

Evolutionary psychology and the selectionist theories of neural development are usually regarded as two unrelated theories addressing two logically distinct questions. The focus of evolutionary psychology is the phylogeny of the human mind, whereas the selectionist theories of neural development analyse the ontogeny of the mind. This paper will endeavour to combine these two approaches in the explanation of the human mind. Doing so might help in overcoming some of the criticisms of both theories. The first (...) part of the paper mentions three standard objections to evolutionary psychology and then outlines three philosophical problems evolutionary psychology has to offer a solution to. The second part will try to show that an approach combining evolutionary psychology and the selectionist theory of neural development might overcome some of these objections. (shrink)

The neuregulin gene encodes a series of polypeptide growth factors that can influence the growth state of target vertebrate cells in culture. Recently, three studies have explored the in vivo function of the neuregulin signaling system in mice by disrupting the genes encoding the neuregulin ligand(1) and two of its receptors, ErbB2(2) and ErbB4(3). Each of the genes is essential for development, and aberrations in cardiac and neural development are particularly prominent in mutant embryos. The observed defects, (...) together with the localization of expression of the neuregulin signaling components within these tissues, highlight a paracrine mechanism for neuregulin‐mediated intercellular communication. (shrink)

Since the growth cone was first described a century ago by Cajal, considerable effort has been directed towards understanding the mechanisms responsible for its guidance. Traditionally, attention has focussed on the role of adhesive molecules in determining neural development. Recently, it has become apparent that inhibitory interactions may play a crucial part in axonal navigation. A common feature of inhibition seen in three model systems (peripheral nerve segmentation, retinotectal mapping and CNS/PNS segregation) is a collapse of the motile (...) structures of the growth cone. It is increasingly clear that the identification of molecular mechanisms of inhibition, as well as those of adhesion, will be of fundamental importance to understanding neural development. (shrink)

How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with (...) recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

Helix–loop–helix (HLH) genes encode for transcription factors affecting a whole variety of developmental programs, including neurogenesis. At least seven functional classes (denoted I to VII) of HLH genes exist,1 with subclass members exhibiting homo‐ and heterodimerisation for proper DNA binding and transcriptional regulation of downstream target genes. In the developing nervous system, members of class II, V and VI have been most extensively studied concerning their roles in neural programming. In contrast, the function of class I proteins (such as (...) E12 and E47) is poorly defined and the orthodox view relegates them to general dimerisation duties that are necessary for the activity of the other classes. However, closer scrutiny of the spatiotemporal expression patterns of class I factors, combined with recent biochemical evidence, would suggest that class I proteins possess specific functions during early neural differentiation. This essay supports this possibility, in addition to putting forward the hypothesis that, outside their general dimerisation activity, class I genes have independent roles in regulating neurogenesis. BioEssays 25:709–716, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

This paper lays out some of the empirical evidence for the importance of neural reuse—the reuse of existing (inherited and/or early-developing) neural circuitry for multiple behavioral purposes—in defining the overall functional structure of the brain. We then discuss in some detail one particular instance of such reuse: the involvement of a local neural circuit in finger awareness, number representation, and other diverse functions. Finally, we consider whether and how the notion of a developmental homology can help us (...) understand the relationships between the cognitive functions that develop out of shared neural supports. (shrink)

This paper first extracted 11 indicators from four aspects of infrastructure, educational equity, teaching quality, and scientific research level and established a multidimensional higher education evaluation system. After that, according to TOPSIS and the entropy method, a comprehensive score of the development of higher education was obtained, and a comprehensive index of higher education was proposed. According to the level of the score, we divide the development status into 5 categories, and use discrete Hopfield neural network for (...) verification. In addition, we applied the model to many countries and chose Vietnam to conduct an in-depth analysis of the model, including reforming policies and evaluating policy effects based on cobweb model. Finally, we found that the application of the model is very universal, but in reality the reform is very difficult. (shrink)

Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.

Cultural similarities and differences in facial expressions have been a controversial issue in the field of facial communications. A key step in addressing the debate regarding the cultural dependency of emotional expression is to characterize the visual features of specific facial expressions in individual cultures. Here we developed an image analysis framework for this purpose using convolutional neural networks that through training learned visual features critical for classification. We analyzed photographs of facial expressions derived from two databases, each developed (...) in a different country, in which corresponding emotion labels were available. While the CNNs reached high rates of correct results that were far above chance after training with each database, they showed many misclassifications when they analyzed faces from the database that was not used for training. These results suggest that facial features useful for classifying facial expressions differed between the databases. The selectivity of computational units in the CNNs to action units of the face varied across the facial expressions. Importantly, the AU selectivity often differed drastically between the CNNs trained with the different databases. Similarity and dissimilarity of these tuning profiles partly explained the pattern of misclassifications, suggesting that the AUs are important for characterizing the facial features and differ between the two countries. The AU tuning profiles, especially those reduced by principal component analysis, are compact summaries useful for comparisons across different databases, and thus might advance our understanding of universality vs. specificity of facial expressions across cultures. (shrink)

I agree with Quartz & Sejnowski's points, which are familiar to many scientists. A number of models with the sought-after properties, however, are overlooked, while models without them are highlighted. I will review nonstationary learning, links between development and learning, locality, stability, learning throughout life, hypothesis testing that models the learner's problem domain, and active dendritic processes.

Recent work on the application of neural networks to language modeling has shown that models based on certain neural architectures can capture syntactic information from utterances and sentences even when not given an explicitly syntactic objective. We examine whether a fully data-driven model of language development that uses a recurrent neural network encoder for utterances can track how child language utterances change over the course of language development in a way that is comparable to what (...) is achieved using established language assessment metrics that use language-specific information carefully designed by experts. Given only transcripts of child language utterances from the CHILDES Database and no pre-specified information about language, our model captures not just the structural characteristics of child language utterances, but how these structures reflect language development over time. We establish an evaluation methodology with which we can examine how well our model tracks language development compared to three known approaches: Mean Length of Utterance, the Developmental Sentence Score, and the Index of Productive Syntax. We discuss the applicability of our model to data-driven assessment of child language development, including how a fully data-driven approach supports the possibility of increased research in multilingual and cross-lingual issues. (shrink)

The sophisticated function of the central nervous system (CNS) is largely supported by proper interactions between neural cells and blood vessels. Accumulating evidence has demonstrated that neurons and glial cells support the formation of blood vessels, which in turn, act as migratory scaffolds for these cell types. Neural progenitors are also involved in the regulation of blood vessel formation. This mutual interaction between neural cells and blood vessels is elegantly controlled by several chemokines, growth factors, extracellular matrix, (...) and adhesion molecules such as integrins. Recent research has revealed that newly migrating cell types along blood vessels repel other preexisting migrating cell types, causing them to detach from the blood vessels. In this review, we discuss vascular formation and cell migration, particularly during development. Moreover, we discuss how the crosstalk between blood vessels and neurons and glial cells could be related to neurodevelopmental disorders. (shrink)

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning is an algorithm based on artificial neural networks, designed to compensate the atmospheric (...) turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks. (shrink)

The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: a (...) slower basal ganglia loop and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, whereas the fast network gradually “memorizes” the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behavior of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus, and various cortical areas. Consequently, the model incorporates various neuroanatomical data, in terms of brain areas used for calculation and makes psychologically testable predictions related to frequency of rehearsal. Furthermore, the model replicates developmental progression through addition strategies in terms of reaction times and accuracy, and naturally explains observed symptoms of dyscalculia. (shrink)

This paper discusses the evidence for the role of CREB in neural stem/progenitor cell (NSPC) function and oncogenesis and how these functions may be important for the development and growth of brain tumours. The cyclic‐AMP response element binding (CREB) protein has many roles in neurons, ranging from neuronal survival to higher order brain functions such as memory and drug addiction behaviours. Recent studies have revealed that CREB also has a role in NSPC survival, differentiation and proliferation. Recent work (...) has shown that over‐expression of CREB in transgenic animals can impart oncogenic properties on cells in various tissues and that aberrant CREB expression is associated with tumours in patients. It is the central position of CREB, downstream of key developmental and growth signalling pathways, which give CREB the ability to influence a spectrum of cell activities, such as cell survival, growth and differentiation in both normal and cancer cells. (shrink)

Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the ‘dynamic core’). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 (...) widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well. (shrink)

The FEBS meeting titled “Generating neural diversity in the brain” took place on the island of Capri, from October 13–16. This high‐level workshop was the 20th in a symposium series organized by the IGB (Instituto Genetica et Biophysica) of Naples funded by international agencies including FEBS, EMBO, European commission. The series is unusual in featuring first‐rank international scientist speakers for a meeting whose audience consists primarily of students and post‐docs. The endeavour is thus more explicitly educational than many major (...) meetings and the young audience helps create a lively atmosphere. With intense morning and evening sessions, the afternoons are left free for the participants to explore the island, go swimming or relax. BioEssays 30:397–399, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

An attractive feature of Neuroconstructivism, Vol. I: How the Brain Constructs Cognition is its emphasis on the active role of the individual in neural and behavioural development and the importance of the interplay with the environment. Certain aspects of development are omitted, however, such as specializations for the distinctive ecologies of infancy and childhood and the scaffolding-like features of behaviour seen during development. It was also a pity that so little credit was given to many scientists (...) who have contributed to just those aspects of development on which the authors focus. (shrink)

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure which is physically related to rainfall on land and thus able to predict unseen rainfall (...) events. Daily rainfall of east coast of Peninsular Malaysia was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine, Bayesian regularized neural networks, Bayesian additive regression trees, extreme gradient boosting, and hybrid neural fuzzy inference system were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error of 0.678 compared with HNFIS, BART, xgBoost, and ELM. Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably. (shrink)

One of the most important recent developments in the study of concepts has been the resurgence of interest in nativist accounts of the human conceptual system. However, many theorists suppose that a key feature of neural organization—the brain’s plasticity—undermines the nativist approach to concept acquisition. We argue that, on the contrary, not only does the brain’s plasticity fail to undermine concept nativism, but a detailed examination of the neurological evidence actually provides powerful support for concept nativism.

An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the (...) role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design. (shrink)

Neural correlates exist for a basic component of logical formulae, PREDICATE(x). Vision and audition research in primates and humans shows two independent neural pathways; one locates objects in body-centered space, the other attributes properties, such as colour, to objects. In vision these are the dorsal and ventral pathways. In audition, similarly separable “where” and “what” pathways exist. PREDICATE(x) is a schematic representation of the brain's integration of the two processes of delivery by the senses of the location of (...) an arbitrary referent object, mapped in parietal cortex, and analysis of the properties of the referent by perceptual subsystems. The brain computes actions using a few “deictic” variables pointing to objects. Parallels exist between such nonlinguistic variables and linguistic deictic devices. Indexicality and reference have linguistic and nonlinguistic (e.g., visual) versions, sharing the concept of attention. The individual variables of logical formulae are interpreted as corresponding to these mental variables. In computing action, the deictic variables are linked with “semantic” information about the objects, corresponding to logical predicates. Mental scene descriptions are necessary for practical tasks of primates, and preexist language phylogenetically. The type of scene descriptions used by nonhuman primates would be reused for more complex cognitive, ultimately linguistic, purposes. The provision by the brain's sensory/perceptual systems of about four variables for temporary assignment to objects, and the separate processes of perceptual categorization of the objects so identified, constitute a pre-adaptive platform on which an early system for the linguistic description of scenes developed. Key Words: argument; attention; deictic; dorsal; logic; neural; object; predicate; reference; ventral. (shrink)

With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of (...) Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, (...) non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks. (shrink)

In Reading in the Brain, Stanislas Dehaene presents a compelling account of how the brain learns to read. Central to this account is his neuronal recycling hypothesis: neural circuitry is capable of being ‘recycled’ or converted to a different function that is cultural in nature. The original function of the circuitry is not entirely lost and constrains what the brain can learn. It is argued that the neural niche co-evolves with the environmental niche in a way that does (...) not undermine the core ideas of neuronal recycling, but which is quite different from the models of cognitive and cultural evolution provided by evolutionary psychology and epidemiology. Dehaene contrasts neuronal recycling with a naïve model of the brain as a general learning device that is unconstrained in what it can learn. Consequently a tension develops in Dehaene's account of the role of plasticity in the acquisition of language. It is argued that the functional and structural changes in the brain that Dehaene documents in great detail are driven by learning and that this learning-driven plasticity does not commit us to a naïve model of the brain. (shrink)

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all (...) those convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

Under the background that the development of low-carbon circular economy is the objective requirement for the in-depth implementation of scientific development and the inevitable choice for promoting the sustainable development of economy and society, it is not only the requirement of corporate social responsibility but also the path to realize corporate social responsibility. Enterprises should become the representative and model of social responsibility practice in the development of low-carbon circular economy, in order to promote the fulfilment (...) and development of corporate social responsibility in the whole society. Therefore, it is of great theoretical and practical significance to study the realization of corporate social responsibility in the context of low-carbon circular economy. This paper introduces the connotation of low-carbon circular economy and corporate social responsibility, analyses the reality and theoretical basis of realizing corporate social responsibility in low-carbon circular economy, analyses the interactive relationship between the development of low-carbon circular economy and the realization of corporate social responsibility, and puts forward the construction of enterprise low-carbon operation mechanism. This paper uses the research of corporate social responsibility based on radial basis function neural network to build a low-carbon circular economy. The evaluation model of environment economy coupling development is verified by an example, which provides useful guidance for the evaluation and development of corporate social responsibility. (shrink)

Recent studies on neural pathways in a broad spectrum of vertebrates suggest that, in addition to migration and an increase in the number of certain select neurons, a significant aspect of neural evolution is a “parcellation” (segregation-isolation) process that involves the loss of selected connections by the new aggregates. A similar process occurs during ontogenetic development. These findings suggest that in many neuronal systems axons do not invade unknown territories during evolutionary or ontogenetic development but follow (...) in their ancestors' paths to their ancestral targets; if the connection is later lost, it reflects the specialization of the circuitry.The pattern of interspecific variability suggests (1) that overlap of circuits is a more common feature in primitive (generalized) than in specialized brain organizations and (2) that most projections, such as the retinal, thalamotelencephalic, corticotectal, and tectal efferent ones, were bilateral in the primitive condition. Specialization of these systems in some vertebrate groups has involved the selective loss of connections, resulting in greater isolation of functions. The parcellation process may also play an important role in cell diversification.The parcellation process as described here is thought to be one of several underlying mechanisms of evolutionary and ontogenetic differentiation. (shrink)

While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop one illustrative (...) example involving research on the information-processing mechanisms mammals employ in navigating their environments. This research was galvanized by the discovery in the 1970s of place cells in the hippocampus. This discovery prompted research in what the activity of these cells represents and how place representations figure in navigation. It also led to the discovery of a host of other types of neurons—grid cells, head-direction cells, boundary cells—that carry other types of spatial information and interact with place cells in the mechanism underlying spatial navigation. As I will try to make clear, the research is explicitly devoted to identifying representations and determining how they are constructed and used in an information processing mechanism. Construals of neural activity as representations are not mere glosses but are characterizations to which neuroscientists are committed in the development of their explanatory accounts. (shrink)