This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by the hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the model, the hypothesised workspace is realised by a collection of recurrently inter-connected regions capable of sustaining and disseminating a reverberating spatial pattern of activation. At the same time, the workspace remains susceptible to new patterns arriving from outlying cortical populations. Competition among these cortical (...) populations for influence on the workspace is effected by a combination of mutual inhibition and top-down amplification. (shrink)

We use a spiking neural network model of working memory capable of performing the spatial delayed response task to investigate two drugs that affect WM: guanfacine and phenylephrine. In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties (...) of the model. In particular, applying GFC causes increased activity in neurons that are sensitive to the information currently being remembered, while applying PHE leads to decreased activity in these same neurons. Interestingly, these differential effects emerge from network-level interactions because GFC and PHE affect all neurons equally. We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT. (shrink)

We focus on the thesis that action understanding is a function of the mirror neuron system. According to our opinion, understanding is a process that runs through hermeneutic circles from the “Vorverständnis” (“previous understanding”) to steps of deeper understanding. Our critique relates to the narrow neuroscientific definition of action understanding as the capacity to recognize several movements as belonging to one action. After a reconstruction of the model's developments, we will challenge the claims of the model by (...) Rizzolatti and Sinigaglia (2010). By analyzing the relation between the experimental results and its interpretation, we will conclude that there is no proof that mirror neuron activity leads to action understanding. (shrink)

This chapter offers a brief review of theories on mirror neuron development, highlighting different models. These models focus on either the role of genetic mechanisms or the contributions of experience and of learning processes in shaping the brain circuits involved in action–perception coupling. As an alternative, the chapter proposes an epigenetic model for mirror neuron development, explaining how such a model can help to elucidate, within a unifying explanatory framework, the emergence, diversity, and functional reuse of (...) mirror neurons. Lastly, a related evolutionary scenario is sketched in which epigenetic regulation, genetic assimilation/the Baldwin effect, and niche construction play crucial roles in mirror neuron evolution. (shrink)

The concept of the receptive field, first articulated by Hartline, is central to visual neuroscience. The receptive field of a neuron encompasses the spatial and temporal properties of stimuli that activate the neuron, and, as Hubel and Wiesel conceived of it, a neuron’s receptive field is static. This makes it possible to build models of neural circuits and to build up more complex receptive fields out of simpler ones. Recent work in visual neurophysiology is providing evidence that (...) the classical receptive field is an inaccurate picture. The receptive field seems to be a dynamic feature of the neuron. In particular, the receptive field of neurons in V1 seems to be dependent on the properties of the stimulus. In this paper, we review the history of the concept of the receptive field and the problematic data. We then consider a number of possible theoretical responses to these data. (shrink)

At the global as well as local scales, some of the geometry of types of neuron arbors—both dendrites and axons—appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform significantly to this model. The result is that such neuron tree samples globally minimize their total volume—rather than, (...) for example, surface area or branch length. In addition, the arbors perform well at generating the cheapest topology interconnecting their terminals: their large-scale layouts are among the best of all such possible connecting patterns, approaching 5% of optimum. This model also applies comparably to arterial and river networks. S1063-651X 99 16205-6.. (shrink)

This review article discusses Andrzej Bielecki’s book _Models of Neurons and Perceptrons: Selected Problems and Challenges_, as published by Springer International Publishing. This work exemplifies “philosophy in science” by adopting a broad, multidisciplinary perspective for the issues related to the simulation of neurons and neural networks, and the author has addressed many of the important philosophical assumptions that are entangled in this area of modeling. Bielecki also raises several important methodological issues about modeling. This book is recommended for any philosophers (...) who wish to learn more about the current state of neural modeling and find inspiration for a deeper philosophical reflection on the subject. (shrink)

Neuron diagrams are heavily employed in academic discussions of causation. Stephen Mumford and Rani Lill Anjum, however, offer an alternative approach employing vector diagrams, which this paper attempts to develop further. I identify three ways in which dispositionalists have taken the activities of powers to be related: stimulation, mutual manifestation, and contribution combination. While Mumford and Anjum do provide resources for representing contribution combination, which might be sufficient for their particular brand of dispositionalism, I argue that those resources are (...) not flexible enough to further accommodate either stimulation or mutual manifestation. Representational tools are provided to address these limitations, improving the general value of the vector model for dispositionalist approaches to causation. (shrink)

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The (...) model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects. (shrink)

Lehar argues that a simple Neuron Doctrine cannot explain perceptual phenomena such as size constancy but he fails to discuss existing, more complex neurological models. Size models that rely purely on scaling for distance are sparse, but several models are also concerned with other aspects of size perception such as geometrical illusions, relative size, adaptation, perceptual learning, and size discrimination.

Mirror neurons form a poor basis for Arbib's account of language evolution, failing to explain the creativity that must precede imitation, and requiring capacities (improbable in hominids) for categorizing situations and unambiguously miming them. They also commit Arbib to an implausible holophrastic protolanguage. His model is further vitiated by failure to address the origins of symbolization and the real nature of syntax.

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal (...) was converted into a binary pulse amplitude modulation signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance. Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment. (shrink)

The hereditary neurodegenerative disease spinal muscular atrophy (SMA) with childhood onset is one of the most common genetic causes of infant mortality. The disease is characterized by selective loss of spinal cord motor neurons leading to muscle atrophy and is the result of mutations in the survival motor neuron (SMN) gene. The SMN protein has been implicated in diverse nuclear processes including splicing, ribosome formation and gene transcription. Even though the genetic basis of SMA is well understood, it is (...) not clear how defects in these ubiquitous processes result in motor neuron degeneration leaving other tissues unaffected. Recent evidence from animal and cell culture models of SMA points to roles for SMN in neurite outgrowth and axonal transport. Disruption of these functions might be particularly detrimental to motor neurons given their high metabolic demands and precise connectivity requirements, thus providing a possible explanation for the specificity of motor neuron susceptibility in SMA. Understanding the molecular mechanisms of SMN activity in neuronal processes may generate new targets for future therapeutic strategies. BioEssays 27:946–957, 2005. © 2005 Wiley Periodicals, Inc. (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 (...) class='Hi'>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)

본고의 논의는 신경이미지 실험이 윤리학적 논의에 어떻게 연관될 수 있으며 어떤 영향들을 미칠 수 있는가를 보여주는 사례를 다룬다. 본고의 목표는 그린(J. Greene)의 이중과정 모델(Dual Process Model)을 뒷받침하는 신경이미지 실험에 대한 비판적 고찰을 통해 그린의 입장이 가지고 있는 문제점을 제시하는 것이다. 이 목표를 이루기 위해 우선 그린의 이중과정 모델을 지지하는 신경이미지 실험에 대해 설명할 것이다. 본고에서 다루는 신경 실험은 인간이 도덕적 판단을 할 때 어떤 경우에는 인지적 판단을 하고 어떤 경우에는 감정적 판단을 한다는 것을 보여준다. 그린의 주장에 따르면, 인지적 판단은 (...) 공리주의적 판단이고 감정적 판단은 의무론적 판단이다. 감정적 판단은 집단 내의 도덕적 문제를 해결하는 데에는 유용할 수 있지만 상식을 공유하지 않는 집단들 사이에서 일어나는 도덕적 갈등을 해결하는 데에는 도움이 되지 못한다. 이런 문제들을 해결하기 위해선 인지적 판단을 기반으로 한 도덕 이론을 가져야 한다고 그린은 주장한다. 논자는 그린의 주장이 가진 몇 가지 문제점을 제시할 것이다. 우선, 감정적 판단이 집단과 집단 사이의 도덕적 갈등을 해결하는데 적절하지 않으며 이런 종류의 문제를 해결하기 위해서는 공리주의적 사고를 추구해야 한다는 그린의 주장에 반대할 것이다. 집단 사이의 문제를 해결하기 위해 공리주의적 사고를 적용할 때 나타날 수 있는 문제점을 제시할 것이다. 또한 감정에 기반을 둔 판단에 의존하는 것이 때로는 이런 도덕적 문제를 해결하는 데 도움이 될 수 있다고 주장할 것이다. 다음으로, 그린의 신경이미지 실험에서의 감정적 판단이 의무론적 판단이라는 입장에 문제가 있다고 주장할 것이다. 이어서 그린이 말하는 감정적 판단이 의무론적 판단이 아닌 다른 종류의 판단일 수 있다는 것을 주장할 것이다. 그러한 판단은 유덕한 사람이 내리는 판단 혹은 의지적 필연성(volitional necessity)에 의해 내려진 판단일 수 있다고 주장할 것이다. 마지막으로 그 판단은 자기충실성을 가진 사람이 내리는 판단일 수 있다고 주장할 것이다. (shrink)

The vertebrate nervous system performs the most complex functions of any organ system. This feat is mediated by dedicated assemblies of neurons that must be precisely connected to one another and to peripheral tissues during embryonic development. Motor neurons, which innervate muscle and regulate autonomic functions, form an integral part of this neural circuitry. The first part of this review describes the remarkable progress in our understanding of motor neuron differentiation, which is arguably the best understood model of (...) neuronal differentiation to date. During development, the coordinate actions of inductive signals from adjacent non‐neural tissues initiate the differentiation of distinct motor neuron subclasses, with specific projection patterns, at stereotypical locations within the neural tube. Underlying this specialisation is the expression of specific homeodomain proteins, which act combinatorially to confer motor neurons with both their generic and subtype‐specific properties. Ensuring that specific motor neuron subtypes innervate the correct target structure, however, requires precise motor axon guidance mechanisms. The second half of this review focuses on how distinct motor neuron subtypes pursue highly specific projection patterns by responding differentially to spatially discrete attractive and repulsive molecular cues. The tight link between motor neuron specification and axon pathfinding appears to be established by the dominant role of homeodomain proteins in dictating the ways that navigating motor axons interpret the plethora of guidance cues impinging on growth cones. BioEssays 23:582–595, 2001. © 2001 John Wiley & Sons, Inc. (shrink)

A formal neuron has been studied mathematically. The spiking behaviour of a single neuron has been considered and the influence of the other neurons has been replaced by an average activity level. Four different kinds of spiking behaviour are predicted by the model: B (bursts), C (continuous), P (periodic) and S (silent) neurons and several real neurons can be classified within these four categories. Some properties of the spiking neuron are calculated: 1) the time between spikes, (...) 2) the spike train length and 3) the silent time. Because these magnitudes can be measured in the laboratory, an experimental validation of the model is proposed. (shrink)

Invertebrate genetic models with their tractable neuromuscular systems are effective vehicles for the study of human nerve and muscle disorders. This is exemplified by insights made into spinal muscular atrophy (SMA) using the fruit fly Drosophila melanogaster and the nematode worm Caenorhabditis elegans. For speed and economy, these invertebrates offer convenient, whole‐organism platforms for genetic screening as well as RNA interference (RNAi) and chemical library screens, permitting the rapid testing of hypotheses related to disease mechanisms and the exploration of new (...) therapeutic routes and drug candidates. Here, we discuss recent developments encompassing synaptic physiology, RNA processing, and screening of compound and genome‐scale RNAi libraries, showcasing the importance of invertebrate SMA models.Editor's suggested further reading in BioEssays: SMN and Gemins: ‘We are family’... or are we? Abstract. (shrink)

A serious crisis is identified in theories of neurocomputation, marked by a persistent disparity between the phenomenological or experiential account of visual perception and the neurophysiological level of description of the visual system. In particular, conventional concepts of neural processing offer no explanation for the holistic global aspects of perception identified by Gestalt theory. The problem is paradigmatic and can be traced to contemporary concepts of the functional role of the neural cell, known as the Neuron Doctrine. In the (...) absence of an alternative neurophysiologically plausible model, I propose a perceptual modeling approach, to model the percept as experienced subjectively, rather than modeling the objective neurophysiological state of the visual system that supposedly subserves that experience. A Gestalt Bubble model is presented to demonstrate how the elusive Gestalt principles of emergence, reification, and invariance can be expressed in a quantitative model of the subjective experience of visual consciousness. That model in turn reveals a unique computational strategy underlying visual processing, which is unlike any algorithm devised by man, and certainly unlike the atomistic feed-forward model of neurocomputation offered by the Neuron Doctrine paradigm. The perceptual modeling approach reveals the primary function of perception as that of generating a fully spatial virtual-reality replica of the external world in an internal representation. The common objections to this picture-in-the-head concept of perceptual representation are shown to be ill founded. Key Words: brain-anchored; Cartesian theatre; consciousness; emergence; extrinsic constraints; filling-in; Gestalt; homunculus; indirect realism; intrinsic constraints; invariance; isomorphism; multistability; objective phenomenology; perceptual modeling; perspective; phenomenology; psychophysical parallelism; psychophysical postulate; qualia; reification; representationalism; structural coherence. (shrink)

During the latter part of the nineteenth century our description of nerve cell structure underwent a relatively unrecognized, though fundamental, transformation-a quiet revolution of sorts. The central problem facing scientists in neurology (the study of the nervous system) was a related pair: are nerve cells continuous with each other or not, and how is information conducted? Microscope resolution and staining techniques were inadequate at the time to yield definitive proof either way. I contend that explanatory coherence provides a means to (...) elucidate this instance of theory change in the history of science. The means of elucidation will be Thagard's computational model of explanatory coherence, ECHO (). (shrink)

A standard way of representing causation is with neuron diagrams. This has become popular since the influential work of David Lewis. But it should not be assumed that such representations are metaphysically neutral and amenable to any theory of causation. On the contrary, this way of representing causation already makes several Humean assumptions about what causation is, and which suit Lewis’s programme of Humean Supervenience. An alternative of a vector diagram is better suited for a powers ontology. Causation should (...) be understood as connecting property types and tokens where there are dispositions towards some properties rather than others. Such a model illustrates how an effect is typically polygenous: caused by many powers acting with each other, and sometimes against each other. It models causation as a tendency towards an effect which can be counteracted. The model can represent cases of causal complexity, interference, over-determination and causation of absence (equilibrium). (shrink)

The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.

Studies on the snailMelampusreveal that connectivity is crucial to the survival of transplanted ganglia. Transplanted CNS ganglia can innervate targets or induce supernumerary structures. Neuron survival is optimized by the neural incorporation that occurs when a transplanted ganglion is substituted for an excised ganglion. Better provision for the trophic requirements of neurons will improve the success of mammalian fetal transplants.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, primarily leading to the degeneration of motor neurons. The traditional focus on motor neuron‐centric mechanisms has recently shifted towards understanding the contribution of non‐neuronal cells, such as microglia, in ALS pathophysiology. Advances in induced pluripotent stem cell (iPSC) technology have enabled the generation of iPSC‐derived microglia monocultures and co‐cultures to investigate their role in ALS pathogenesis. Here, we briefly review the insights gained from these studies into the role of microglia in (...) ALS. While iPSC‐derived microglia monocultures have revealed intrinsic cellular dysfunction due to ALS‐associated mutations, microglia‐motor neuron co‐culture studies have demonstrated neurotoxic effects of mutant microglia on motor neurons. Based on these findings, we briefly discuss currently unresolved questions and how they could be addressed in future studies. iPSC models hold promise for uncovering disease‐relevant pathways in ALS and identifying potential therapeutic targets. (shrink)

The discovery of so-called ‘mirror neurons’ - found to respond both to own actions and the observation of similar actions performed by others - has been enormously influential in the cognitive sciences and beyond. Given the self-other symmetry these neurons have been hypothesized as underlying a ‘mirror mechanism’ that lets us share representations and thereby ground core social cognitive functions from intention understanding to linguistic abilities and empathy. I argue that mirror neurons are important for very different reasons. Rather than (...) a symmetric ubiquitous or context- independent mechanism, I propose that these neurons are part of broader sensorimotor circuits, which help us navigate and predict the social affordance space that we meet others in. To develop both the critical and positive project I analyze the interpretive choices and the debate surrounding the mirror neuron research and show how the field is marred by highly questionable assumptions about respectively motor and social cognition. The discovery of mirror neurons - and the sensorimotor circuits of which these neurons are a part – actually empirically challenge many of these tacit assumptions. Findings of sensorimotor goal representations at levels of abstraction well beyond actual sensory information and kinetic movements challenge the idea of motor cognition as primarily output production. Additionally, the focus on 3rd person mindreading of hidden mental states is misguiding the field of social cognition. Much ‘mind-reading’ seems rooted in sensorimotor representations and a developmentally primary 2nd person understanding of actions and the mental lives of others, which precisely breaks the assumed dichotomy between mind and behavior. I propose a Social Affordance model where parallel fronto-parietal sensorimotor circuits support representations not just of other people’s actions but of the overall social affordance space. It is a process that monitors concrete goals and teleological possibilities that the environment affords respectively oneself and other present agents. With this model I hypothesize that the complex spectrum of sensorimotor integrations are indeed essential not only to normal action choice calibration but also to social cognitive abilities, as the sensorimotor teleological representations let us relate to others and understand their action choices in a shared pragmatic and intentional context. (shrink)

In this paper, my aim is to understand the origin of experimental and scientific models of pathogeny of the diseases of the nervous system in the Salpêtrière (Paris). I will analyse the role of the contexts of cell theory, microscopy and the advances in histological techniques in the creation of various pathogenic models, based on the concept of the cell, the Wallerian degeneration and the neurone concept. I argue that, as medicine and pathology remain autonomous in their methods and goals, (...) because of the evident degree of complexity of diseases, close and reciprocal interactions with sciences, their practices and theories, make it possible to establish convergences between clinical observations, pathological data and those from the experimental models of pathologies. The search for pathogenic models behaves like an engine, which is efficient in assembling facts, in testing pathogeneses and reforming nosologies, combined with the breakthroughs in biology. This paper is a case study showing the emergence of such interactions in the last decades of the nineteenth-century in Paris. (shrink)

In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be sharply distinguished (...) from one another, and that correspond to conceptually different interpretations of the notion of “biological accuracy”. They include the ambition to reach extremely deep levels in the mechanistic decomposition hierarchy, to simulate networks composed of extremely large numbers of neural units, to build systems able to generate rich behavioural repertoires, to simulate “complex” neuron models, to implement the “best” theories available on brain structure and function. Some questions will be raised concerning the significance of each of these modelling ambitions with respect to the various roles played by simulations in the study of the brain. (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)

Mirror neuron research has come a long way since the early 1990s, and many theorists are now stressing the heterogeneity and complexity of the sensorimotor properties of fronto-parietal circuits. However, core aspects of the initial ‘ mirror mechanism ’ theory, i.e. the idea of a symmetric encapsulated mirroring function translating sensory action perceptions into motor formats, still appears to be shaping much of the debate. This article challenges the empirical plausibility of the sensorimotor segregation implicit in the original mirror (...) metaphor. It is proposed instead that the teleological organization found in the broader fronto-parietal circuits might be inherently sensorimotor. Thus the idea of an independent ‘purely perceptual’ goal understanding process is questioned. Further, it is hypothesized that the often asymmetric, heterogeneous and contextually modulated mirror and canonical neurons support a function of multisensory mapping and tracking of the perceiving agents affordance space. Such a shift in the interpretative framework offers a different theoretical handle on how sensorimotor processes might ground various aspects of intentional action choice and social cognition. Mirror neurons would under the proposed “social affordance model” be seen as dynamic parts of larger circuits, which support tracking of currently shared and competing action possibilities. These circuits support action selection processes—but also our understanding of the options and action potentials that we and perhaps others have in the affordance space. In terms of social cognition ‘ mirror ’ circuits might thus help us understand not only the intentional actions others are actually performing—but also what they could have done, did not do and might do shortly. (shrink)

In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions. The matrix signal commonly used in engineering applications also puts forward higher requirements for processing speed. The eigenvalues of the matrix represent many characteristics of the matrix. Its mathematical meaning represents the expansion of the inherent vector, and its physical meaning represents the spectrum of vibration. The eigenvalue of a matrix is the focus of matrix theory. The (...) problem of matrix eigenvalues is widely used in many research fields such as physics, chemistry, and biology. A neural network is a neuron model constructed by imitating biological neural networks. Since it was proposed, the application research of its typical models, such as recurrent neural networks and cellular neural networks, has become a new hot spot. With the emergence of deep neural network theory, scholars continue to combine deep neural networks to calculate matrix eigenvalues. This article aims to study the estimation and application of matrix eigenvalues based on deep neural networks. This article introduces the related methods of matrix eigenvalue estimation based on deep neural networks, and also designs experiments to compare the time of matrix eigenvalue estimation methods based on deep neural networks and traditional algorithms. It was found that under the serial algorithm, the algorithm based on the deep neural network reduced the calculation time by about 7% compared with the traditional algorithm, and under the parallel algorithm, the calculation time was reduced by about 17%. Experiments are also designed to calculate matrix eigenvalues with Obj and recurrent neural networks (RNNS) models, which proves that the Oja algorithm is only suitable for calculating the maximum eigenvalues of non-negative matrices, while RNNS is commonly used in general models. (shrink)

Automatic imitation or “imitative compatibility” is thought to be mediated by the mirror neuron system and to be a laboratory model of the motor mimicry that occurs spontaneously in naturalistic social interaction. Imitative compatibility and spatial compatibility effects are known to depend on different stimulus dimensions—body movement topography and relative spatial position. However, it is not yet clear whether these two types of stimulus–response compatibility effect are mediated by the same or different cognitive processes. We present an interactive (...) activation model of imitative and spatial compatibility, based on a dual-route architecture, which substantiates the view they are mediated by processes of the same kind. The model, which is in many ways a standard application of the interactive activation approach, simulates all key results of a recent study by Catmur and Heyes (2011). Specifically, it captures the difference in the relative size of imitative and spatial compatibility effects; the lack of interaction when the imperative and irrelevant stimuli are presented simultaneously; the relative speed of responses in a quintile analysis when the imperative and irrelevant stimuli are presented simultaneously; and the different time courses of the compatibility effects when the imperative and irrelevant stimuli are presented asynchronously. (shrink)

Mirror neuron activation (MNA) has been applied to number of business ethics contexts including marketing, charitable giving, organizational connectedness, and leadership. Unfortunately, the business literature has often ignored research in philosophy and psychology which can provide insight into the application of mirror neurons to business contexts and other disciplines. I will argue that the use of mirror neurons to support business decisions cannot be established solely on the existence of neural activation as it requires higher level cognitive functions that (...) go beyond biological reductionism. But research into MNA does support their use in basic motor action understanding which is below the level of mental and psychological attributions. The corollary supports a dual-process model. On the one hand, mirror neurons are an automatic and unconscious process by which we understand human movement and, on the other hand, there is a cognitive system by which we understand action in terms of situational specifics and mental states. (shrink)

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and (...) self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel “iterant deformable sensorimotor medium” (IDSM), designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated body and environment and show that under certain conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor “meso-scale” between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks. (shrink)

We describe a way of modeling high‐dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron‐like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit depends on the activities in earlier layers are learned by minimizing the energy assigned to data vectors that are actually observed and maximizing the (...) energy assigned to “confabulations” that are generated by perturbing an observed data vector in a direction that decreases its energy under the current model. (shrink)

The cell assembly model of language posits that words are laid down in the cortex by discrete sets of neurons distributed over specific parts of the brain. The strong internal links of these “word webs” may not only bind articulatory and acoustic knowledge of a lexical item, they may also link word and meaning; for example, by connecting neuron populations related to word forms to those of actions and perceptions to which the words refer. Therefore, the cortical activation (...) elicited by words should reflect aspects of word meaning, a postulate that has received strong support from recent work using neurophysiological and metabolic imaging. Segalowitz & Lane make the point that this neurobiological model can also be used to predict reaction times in behavioral experiments, using the behavioral distinction between content and function words as an example. We acclaim their view, but warn that response times might be related to different mechanisms at the neuronal level, including the cortical distribution and internal connectivity of cell assemblies along with their mutual connections in the grammatical (syntactic and semantic) network. (shrink)

We present a model for generating a kind of neural synchrony in which the individual spike trains of one neuron or group of neurons closely match the spike trains of another. This kind of neural synchrony has been observed in animals performing auditory, visual and attentional information processing tasks. Our model is realized in a system of functionally identical, refractory spiking neurons. Larger systems with more sophisticated information processing capabilities can be constructed from aggregated instances of the (...) basic network. (shrink)

The HIT model comes close to a view suggested by Donald Hebb, that cognitive representations are organized as distributed neuron webs, cell assemblies, whose components are mutually connected and whose internal connections provide continuous information exchange among sub-components of the representation. Two questions are asked related to (1) the organization of internal connections of a concept representation and (2) the conditions under which information exchange between components are assumed in the HIT model.

One's model of skill determines what one expects from neural network modelling and how one proposes to go about enhancing expertise. We view skill acquisition as a progression from acting on the basis of a rough theory of a domain in terms of facts and rules to being able to respond appropriately to the current situation on the basis of neuron connections changed by the results of responses to the relevant aspects of many past situations. Viewing skill acquisition (...) in this ways suggests how one can avoid the problem currently facing AI of how to train a network to make human-like generalizations. In training a network one must progress, as the human learner does, from rules and facts to wholistic responses. As to future work, from our perspective one should not try to enhance expertise as in traditional AI by attempting to construct improved theories of a domain, but rather by improving the learner's access to the relevant aspects of a domain so as to facilitate learning from experience. (shrink)

This article is a response to the 1994 Star Island conference on the “Decade of the Brain” from a Jewish perspective. After a brief introduction about the logical function of models and maps, I compare and contrast three models of the human: Ezekiel's vision of the chariot in the Hebrew Scriptures, Franz Rosenzweig's geometry of the human face in Der Stern der Erlosung (the Star of Redemption), and a standard anatomical picture of the human brain. Whereas Rosenzweigs face is seen (...) to be compatible with Ezekiel's chariot, both are seen to be radically distinct from the implicit conception of what a human being is in modern medical science. I conclude with a suggestion that the differences are to be understood in terms of their different intended functions and express my hope for some new kind of model that will incorporate the functional advantages of both. (shrink)

Something remarkable is happening in the cognitive sciences. After a quarter of a century of cognitive models that were inspired by the metaphor of the digital computer, the newest cognitive models are inspired by the properties of the brain itself. Variously referred to as connectionist, parallel distributed processing, or neutral network models, they explore the idea that complex intellectual operations can be carried out by large networks of simple, neuron-like units. The units themselves are identical, very low-level and 'stupid'. (...) Intelligent performance is derived from the pattern of connection strengths between units, and the fundamental cognitive activity is pattern recognition and completion. Connectionism and the Mind provides an introduction to this newly emerging approach to understanding the mind. The first few chapters focus on network architecture, offering accessible treatment of the equations that describe learning and the propagation of activation. Furthermore, the reader is walked step-by-step through the activities of networks engaged in pattern recognition, learning, and cognitive tasks such as memory retrieval and prototype formation. The remainder of the book addresses the implications of connectionism for theories of the mind, both philosophical and psychological. Foe example: What Role is played by pattern recognition and completion as basic as cognitive functions? Connectionist models have particular strength in learning and pattern recognition; should they be limited to those functions, or can they provide an overall account of cognitive functioning? In particular, can connectionist models provide an adequate account of the ability to employ linguistic and other symbol systems, or must an adequate system incorporate symbol processing as a basic cognitive capacity? Finally, Connectionism and the Mind examines the relation of connectionist models to philosophical accounts of propositional attitudes, and to a variety of other inquiries in cognitive psychology, linguistics, developmental psychology, artificial intelligence and neuroscience. (shrink)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms of predictive coding.

During sensory stimulation, visual cortical neurons undergo massive synaptic bombardment. This increases their input conductance, and action potentials mainly result from membrane potential fluctuations. To understand the response properties of neurons operating in this regime, we studied a model neuron with synaptic inputs represented by transient membrane conductance changes. We show that with a simultaneous increase of excitation and inhibition, the firing rate first increases, reaches a maximum, and then decreases at higher input rates. Comodulation of excitation and (...) inhibition, therefore, does not provide a straightforward way of controlling the neuronal firing rate, in contrast to coding mechanisms postulated previously. The synaptically induced conductance increase plays a key role in this effect: it decreases firing rate by shunting membrane potential fluctuations, and increases it by reducing the membrane time constant, allowing for faster membrane potential transients. These findings do not depend on details of the model and, hence, are relevant to cells of other cortical areas as well. (shrink)

We explore the understanding of conscious states in terms of spatio-temporal dynamics through modelling a mobile agent. Conscious states are associated with an agent's spontaneous and deterministic fluctuation between attachment to and detachment from the surroundings. It is because of this fluctuating nature, we argue, that an agent can perceive structure in the world. Perception requires a conscious state in physical devices. This is a central concern of this paper, and we examine it by simulating a mobile agent equipped with (...) an interconnected Fitz-Hugh-Nagumo (FHN) neuron network with delayed signal transmissions. The agent can move around a space by sensing the environment pattern through the input neurons and computing the motor outputs via the FHN network. The agent shows a variety of motion styles and a spontaneous selection of motion styles responding to the surroundings. Such a phenomenon is named embodied chaotic itinerancy (ECI), as an extension of chaotic itinerant dynamics, which is known to be a typical dynamic with a high degree of freedom. We take this selective mode of response to be significant, particularly those interacting with spatial pattern, as an inevitable property of conscious states. (shrink)