Banknote recognition: investigating processing and cognition framework using competitive neural network

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria’s Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.     

Neurodynamics, tonality, and the auditory brainstem response

Tonal relationships are foundational in music, providing the basis upon which musical structures, such as melodies, are constructed and perceived. A recent dynamic theory of musical tonality predicts that networks of auditory neurons resonate nonlinearly to musical stimuli. Nonlinear resonance leads to stability and attraction relationships among neural frequencies, and these neural dynamics give rise to the perception of relationships among tones that we collectively refer to as tonal cognition. Because this model describes the dynamics of neural populations, it makes specific predictions about human auditory neurophysiology. Here, we show how predictions about the auditory brainstem response (ABR) are derived from the model. To illustrate, we derive a prediction about population responses to musical intervals that has been observed in the human brainstem. Our modeled ABR shows qualitative agreement with important features of the human ABR. This provides a source of evidence that fundamental principles of auditory neurodynamics might underlie the perception of tonal relationships, and forces reevaluation of the role of learning and enculturation in tonal cognition.     

Opinion: the neural basis of human moral cognition

Moral cognitive neuroscience is an emerging field of research that focuses on the neural basis of uniquely human forms of social cognition and behaviour. Recent functional imaging and clinical evidence indicates that a remarkably consistent network of brain regions is involved in moral cognition. These findings are fostering new interpretations of social behavioural impairments in patients with brain dysfunction, and require new approaches to enable us to understand the complex links between individuals and society. Here, we propose a cognitive neuroscience view of how cultural and context-dependent knowledge, semantic social knowledge and motivational states can be integrated to explain complex aspects of human moral cognition.     

Dysregulation of neural calcium signaling in Alzheimer disease,bipolar disorder and schizophrenia

Neurons have highly developed Ca²⁺ signaling systems responsible for regulating a large number of neural functions such as the control of brain rhythms, information processing and the changes in synaptic plasticity that underpin learning and memory. The tonic excitatory drive, which is activated by the ascending arousal system, is particularly important for processes such as sensory perception, cognition and consciousness. The Ca²⁺ signaling pathway is a key component of this arousal system that regulates the neuronal excitability responsible for controlling the neural brain rhythms required for information processing and cognition. Dysregulation of the Ca²⁺ signaling pathway responsible for many of these neuronal processes has been implicated in the development of some of the major neural diseases in man such as Alzheimer disease, bipolar disorder and schizophrenia. Various treatments, which are known to act by reducing the activity of Ca²⁺ signaling, have proved successful in alleviating the symptoms of some of these neural diseases.     

Introspective minds: using ALE meta-analyses to study commonalities in the neural correlates of emotional processing, social & unconstrained cognition

Previous research suggests overlap between brain regions that show task-induced deactivations and those activated during the performance of social-cognitive tasks. Here, we present results of quantitative meta-analyses of neuroimaging studies, which confirm a statistical convergence in the neural correlates of social and resting state cognition. Based on the idea that both social and unconstrained cognition might be characterized by introspective processes, which are also thought to be highly relevant for emotional experiences, a third meta-analysis was performed investigating studies on emotional processing. By using conjunction analyses across all three sets of studies, we can demonstrate significant overlap of task-related signal change in dorso-medial prefrontal and medial parietal cortex, brain regions that have, indeed, recently been linked to introspective abilities. Our findings, therefore, provide evidence for the existence of a core neural network, which shows task-related signal change during socio-emotional tasks and during resting states.     

Human neuroimaging of oxytocin and vasopressin in social cognition

The neuropeptides oxytocin and vasopressin have increasingly been identified as modulators of human social behaviors and associated with neuropsychiatric disorders characterized by social dysfunction, such as autism. Identifying the human brain regions that are impacted by oxytocin and vasopressin in a social context is essential to fully characterize the role of oxytocin and vasopressin in complex human social cognition. Advances in human non-invasive neuroimaging techniques and genetics have enabled scientists to begin to elucidate the neurobiological basis of the influence of oxytocin and vasopressin on human social behaviors. Here we review the findings to-date from investigations of the acute and chronic effects of oxytocin and vasopressin on neural activity underlying social cognitive processes using "pharmacological fMRI" and "imaging genetics", respectively. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior.     

Research progress on Drosophila visual cognition in China

Visual cognition,as one of the fundamental aspects of cognitive neuroscience,is generally associated with high-order brain functions in animals and human.Drosophila,as a model organism,shares certain features of visual cognition in common with mammals at the genetic,molecular,cellular,and even higher behavioral levels.From learning and memory to decision making,Drosophila covers a broad spectrum of higher cognitive behaviors beyond what we had expected.Armed with powerful tools of genetic manipulation in Drosophila,an increasing number of studies have been conducted in order to elucidate the neural circuit mechanisms underlying these cognitive behaviors from a genes-brain-behavior perspective.The goal of this review is to integrate the most important studies on visual cognition in Drosophila carried out in mainland China during the last decade into a body of knowledge encompassing both the basic neural operations and circuitry of higher brain function in Drosophila.Here,we consider a series of the higher cognitive behaviors beyond learning and memory,such as visual pattern recognition,feature and context generalization,different feature memory traces,salience-based decision,attention-like behavior,and cross-modal leaning and memory.We discuss the possible general gain-gating mechanism implementing by dopamine-mushroom body circuit in fly's visual cognition.We hope that our brief review on this aspect will inspire further study on visual cognition in flies,or even beyond.     

New thinking: the evolution of human cognition

Humans are animals that specialize in thinking and knowing, and our extraordinary cognitive abilities have transformed every aspect of our lives. In contrast to our chimpanzee cousins and Stone Age ancestors, we are complex political, economic, scientific and artistic creatures, living in a vast range of habitats, many of which are our own creation. Research on the evolution of human cognition asks what types of thinking make us such peculiar animals, and how they have been generated by evolutionary processes. New research in this field looks deeper into the evolutionary history of human cognition, and adopts a more multi-disciplinary approach than earlier 'Evolutionary Psychology'. It is informed by comparisons between humans and a range of primate and non-primate species, and integrates findings from anthropology, archaeology, economics, evolutionary biology, neuroscience, philosophy and psychology. Using these methods, recent research reveals profound commonalities, as well striking differences, between human and non-human minds, and suggests that the evolution of human cognition has been much more gradual and incremental than previously assumed. It accords crucial roles to cultural evolution, techno-social co-evolution and gene-culture co-evolution. These have produced domain-general developmental processes with extraordinary power-power that makes human cognition, and human lives, unique.     

Does Computation Reveal Machine Cognition?

This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine computations, these claims do not hold out in unraveling machine cognition, let alone machine consciousness (if there is any such thing). The nature and form of machine cognition remains further confused also because of attempts to explain human cognition in terms of computation and to model/simulate (aspects of) human cognitive processing in machines. Given that these problems in characterizing machine cognition persist, a view of machine cognition that aims to avoid these problems is outlined. The argument that is advanced is that something becomes a computation in machines only when a human interprets it, which is a kind of semiotic causation. From this it follows that a computing machine is not engaged in a computation unless a human interprets what it is doing; instead, it is engaged in machine cognition, which is defined as a member or subset of the set of all possible mappings of inputs to outputs. The human interpretation, which is a semiotic process, gives meaning to what a machine does, and then what it does becomes a computation.     

Learning with half a brain

Since the 1970s, human subjects that have undergone corpus callosotomy have provided important insights into neural mechanisms of perception, memory, and cognition. The ability to test the function of each hemisphere independently of the other offers unique advantages for investigating systems that are thought to underlie cognition. However, such approaches have been limited to mammals. Here we describe comparable experiments on an insect brain to demonstrate learning‐associated changes within one brain hemisphere. After training one half of their bisected brains, cockroaches learn to extend the antenna supplying that brain hemisphere towards an illuminated diode after this has been paired with an odor stimulus. The antenna supplying the naïve hemisphere shows no response. Cockroaches retain this ability for up to 24 h, during which, shortly after training, the mushroom body of the trained hemisphere alone undergoes specific post‐translational alterations of microglomerular synaptic complexes in its calyces. © 2007 Wiley Periodicals, Inc. Develop Neurobiol, 2007.     

Human Perceptual and Phobic Biases for Snakes: A Review of the Experimental Evidence

In this paper I review the literature on the evolutionary origins of phobias and describe the current state of research on the neurobiology and developmental origins of ophidiophobia—fear of snakes. In doing so I compare experimental evidence related to evolutionary explanations for snake fears and phobias which are outlined in Seligman’s Preparedness Theory and Isbell’s Snake Detection Theory. These theories have been tested extensively using a variety of experimental paradigms aimed at determining the “innateness” of snake fears, the neural pathways involved in fear responses to snakes, and the perceptual biases associated with snake stimuli. However, in the vast majority of these experiments, the stimuli presented are photographs of snakes rather than the real thing. I argue that this point of methodology, while ironically supportive of the findings, is based on some assumptions about cognition and consciousness which run counter to neuroscience. In understanding human responses to snakes, we need to understand better the interplay between cognition and consciousness and how these represent a pluralism of mind in which perception is much more than we think.     

Deconstructing memory in Drosophila

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.     

Resolving Discontinuity: A Minimalist Distinction between Human and Non-human Minds

Our genotype is so similar to those of the African apes, andour last common ancestor with them so recent, that it seemsimpossible that human and non-human cognition should differqualitatively. But the outputs of human cognition are uniquein their limitless creativity and adaptability. Exaption resolvesthe apparent paradox. Assume that the power to create symbolsemerges from stimulus-stimulus linkages and is latent in manyanimals, and that the structural side of language emerges fromthe argument structures inherent in the social calculus associatedwith reciprocal altruism. These adaptations confer the potentialfor language. However, creating complex messages requires uniquelylong-lasting coherence of neural signals, which depends in turnon the large quantities of neurons unique to Homo. The onlydifference between human and non-human minds is that we cansustain longer and more complex trains of thought. All else(emotions, rational processes, even consciousness) could beexactly the same.     

Environmental effects on fish neural plasticity and cognition

Most fishes experiencing challenging environments are able to adjust and adapt their physiology and behaviour to help them cope more effectively. Much of this flexibility is supported and influenced by cognition and neural plasticity. The understanding of fish cognition and the role played by different regions of the brain has improved significantly in recent years. Techniques such as lesioning, tract tracing and quantifying changes in gene expression help in mapping specialized brain areas. It is now recognized that the fish brain remains plastic throughout a fish's life and that it continues to be sensitive to environmental challenges. The early development of fish brains is shaped by experiences with the environment and this can promote positive and negative effects on both neural plasticity and cognitive ability. This review focuses on what is known about the interactions between the environment, the telencephalon and cognition. Examples are used from a diverse array of fish species, but there could be a lot to be gained by focusing research on neural plasticity and cognition in fishes for which there is already a wealth of knowledge relating to their physiology, behaviour and natural history, e.g. the Salmonidae.     

Cognitive ornithology: the evolution of avian intelligence

Comparative psychologists interested in the evolution of intelligence have focused their attention on social primates, whereas birds tend to be used as models of associative learning. However, corvids and parrots, which have forebrains relatively the same size as apes, live in complex social groups and have a long developmental period before becoming independent, have demonstrated ape-like intelligence. Although, ornithologists have documented thousands of hours observing birds in their natural habitat, they have focused their attention on avian behaviour and ecology, rather than intelligence. This review discusses recent studies of avian cognition contrasting two different approaches; the anthropocentric approach and the adaptive specialization approach. It is argued that the most productive method is to combine the two approaches. This is discussed with respects to recent investigations of two supposedly unique aspects of human cognition; episodic memory and theory of mind. In reviewing the evidence for avian intelligence, corvids and parrots appear to be cognitively superior to other birds and in many cases even apes. This suggests that complex cognition has evolved in species with very different brains through a process of convergent evolution rather than shared ancestry, although the notion that birds and mammals may share common neural connectivity patterns is discussed.     

Maps of space in human frontoparietal cortex

Prefrontal cortex (PFC) and posterior parietal cortex (PPC) are neural substrates for spatial cognition. We here review studies in which we tested the hypothesis that human frontoparietal cortex may function as a priority map. According to priority map theory, objects or locations in the visual world are represented by neural activity that is proportional to their attentional priority. Using functional magnetic resonance imaging (fMRI), we first identified topographic maps in PFC and PPC as candidate priority maps of space. We then measured fMRI activity in candidate priority maps during the delay periods of a covert attention task, a spatial working memory task, and a motor planning task to test whether the activity depended on the particular spatial cognition. Our hypothesis was that some, but not all, candidate priority maps in PFC and PPC would be agnostic with regard to what was being prioritized, in that their activity would reflect the location in space across tasks rather than a particular kind of spatial cognition (e.g., covert attention). To test whether patterns of delay period activity were interchangeable during the spatial cognitive tasks, we used multivariate classifiers. We found that decoders trained to predict the locations on one task (e.g., working memory) cross-predicted the locations on the other tasks (e.g., covert attention and motor planning) in superior precentral sulcus (sPCS) and in a region of intraparietal sulcus (IPS2), suggesting that these patterns of maintenance activity may be interchangeable across the tasks. Such properties make sPCS in frontal cortex and IPS2 in parietal cortex viable priority map candidates, and suggest that these areas may be the human homologs of the monkey frontal eye field (FEF) and lateral intraparietal area (LIP).     

A link between neuroscience and informatics: large-scale modeling of memory processes

Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.     

Stem Cell Transplantation Strategies for the Restoration of Cognitive Dysfunction Caused by Cranial Radiotherapy

Radiotherapy often provides the only clinical recourse for those afflicted with primary or metastatic brain tumors. While beneficial, cranial irradiation can induce a progressive and debilitating decline in cognition that may, in part, be caused by the depletion of neural stem cells. Given the increased survival of patients diagnosed with brain cancer, quality of life in terms of cognitive health has become an increasing concern, especially in the absence of any satisfactory long-term treatments.To address this serious health concern we have used stem cell replacement as a strategy to combat radiation-induced cognitive decline. Our model utilizes athymic nude rats subjected to cranial irradiation. The ionizing radiation is delivered as either whole brain or as a highly focused beam to the hippocampus via linear accelerator (LINAC) based stereotaxic radiosurgery. Two days following irradiation, human neural stem cells (hNSCs) were stereotaxically transplanted into the hippocampus. Rats were then assessed for changes in cognition, grafted cell survival and for the expression of differentiation-specific markers 1 and 4-months after irradiation. Our cognitive testing paradigms have demonstrated that animals engrafted with hNSCs exhibit significant improvements in cognitive function. Unbiased stereology reveals significant survival (10-40%) of the engrafted cells at 1 and 4-months after transplantation, dependent on the amount and type of cells grafted. Engrafted cells migrate extensively, differentiate along glial and neuronal lineages, and express a range of immature and mature phenotypic markers.Our data demonstrate direct cognitive benefits derived from engrafted human stem cells, suggesting that this procedure may one day afford a promising strategy for the long-term functional restoration of cognition in individuals subjected to cranial radiotherapy. To promote the dissemination of the critical procedures necessary to replicate and extend our studies, we have provided written and visual documentation of several key steps in our experimental plan, with an emphasis on stereotaxic radiosurgey and transplantation.     

Nonlinear Dynamics of Emotion-Cognition Interaction: When Emotion Does not Destroy Cognition?

Emotion (i.e., spontaneous motivation and subsequent implementation of a behavior) and cognition (i.e., problem solving by information processing) are essential to how we, as humans, respond to changes in our environment. Recent studies in cognitive science suggest that emotion and cognition are subserved by different, although heavily integrated, neural systems. Understanding the time-varying relationship of emotion and cognition is a challenging goal with important implications for neuroscience. We formulate here the dynamical model of emotion-cognition interaction that is based on the following principles: (1) the temporal evolution of cognitive and emotion modes are captured by the incoming stimuli and competition within and among themselves (competition principle); (2) metastable states exist in the unified emotion-cognition phase space; and (3) the brain processes information with robust and reproducible transients through the sequence of metastable states. Such a model can take advantage of the often ignored temporal structure of the emotion-cognition interaction to provide a robust and generalizable method for understanding the relationship between brain activation and complex human behavior. The mathematical image of the robust and reproducible transient dynamics is a Stable Heteroclinic Sequence (SHS), and the Stable Heteroclinic Channels (SHCs). These have been hypothesized to be possible mechanisms that lead to the sequential transient behavior observed in networks. We investigate the modularity of SHCs, i.e., given a SHS and a SHC that is supported in one part of a network, we study conditions under which the SHC pertaining to the cognition will continue to function in the presence of interfering activity with other parts of the network, i.e., emotion.