Recent work combining cognitive neuroscience with computational modelling suggests that distributed patterns of neural firing may represent probability distributions. This paper asks: what makes it the case that distributed patterns of firing, as well as carrying information about (correlating with) probability distributions over worldly parameters, represent such distributions? In examples of probabilistic population coding, it is the way information is used in downstream processing so as to lead to successful behaviour. In these cases content depends on factors beyond bare (...) information, contra Brian Skyrms’ (2010) view that representational content can be fully characterised in information-theoretic terms. (shrink)

The minimally conscious sta te (MCS) is usually ascribed when a patientwith brain damage exhibits obser vable volitional behaviors that predict recovery ofcognitive funct ions. Nevertheless, a patient with brain damage who lacks motorcapacit y might nonetheless be in MCS. For this reason, some clinicians use neuralsignals as a communicative means for MCS ascription. For instance, a vegetativestate patient is diagnosed with MCS if activity in the motor area is observed whenthe instruction to imagine wiggling toes is given. The validi (...) ty of using neuralsignals in ascribing MCS requires a special sort of inference. That is, no-reportparadigmatic assessments must have inductively strong ways of inferring a pur-ported informationa l content from the observed neural signal that grounds the factthat the patient has top-down cognitive control (or residual volition). Shannon’smathemat ical theory of communication and Bayes’ theorem reveals the formalstructure of neural communication. On the basis of relevant data from the neuro-science literature, I conclude that the formal structure combined with the data showsthat neural signals can be used as a communicative means for operational diagnosticcriteria for MCS ascription. (shrink)

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That (...) means that the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (shrink)

Post-comatose patients are classified as being in a minimally conscious state when they have executive functions. Because traditional behavioral assessments may not capture signs of executive functions in post-comatose patients, clinicians look to localized brain activities in response to task instructions, such as imagining wiggling toes, to diagnose minimal consciousness. This paper critically assesses the assumption underlying such alternative methods: that brain activities are neural signals conveying information about minimal consciousness. Based on a Shannon-inspired idea of information, I distinguish (...) between informational and engineering aspects of clinical tasks. The informational aspect concerns the conditional probability that, for example, given activity in the motor areas of the brain in response to task instructions, a patient is imagining wiggling toes. The engineering aspect concerns efficient activation of the relevant brain areas in a patient under the task conditions. This distinction shows that the current alternative methods are not informationally problematic, but are structurally “ill-formed.” For instance, the toe-imagery task requires the capacity to comprehend syntactically complex sentences, which can be dissociated from minimal consciousness. I propose a misrepresentation task, which tests the capacity to misconceptualize lukewarm water as melting wax, as a supplement to the current alternative methods. This task is as informationally reliable as these methods, but is structurally “well-formed,” as it does not rely methodologically on prerequisites such as language comprehension. (shrink)

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

The unification of microscopic and macroscopic models of brain behaviour is of paramount importance and Wright & Liley's target article provides some important groundwork. In this commentary, I propose that a useful approach for the future is to incorporate a developmental perspective into such models. This may be an important constraint, providing a key to understanding the nature of macroscopic measures of brain function such as functional measures like ERP.

Chemosensation and mechanosensation cover an enormous spectrum of processes by which animals use information from the environment to adapt their behavior. For pragmatic reasons, these sensory modalities are commonly investigated independently. Recent advances, however, have revealed numerous situations in which they function together to control animals’ actions. Highlighting examples from diverse vertebrates and invertebrates, we first discuss sensory receptors and neurons that have dual roles in the detection of chemical and mechanical stimuli. Next we present cases where peripheral chemosensory and (...) mechanosensory pathways are discrete but intimately packaged to permit coordinated reception of external cues. Finally, we consider how chemical and mechanical signals converge in central neural circuitry to enable multisensory integration. These insights demonstrate how investigation of the interplay between different sensory modalities is key to a more holistic and realistic understanding of sensory‐guided behaviors. (shrink)

Neural crest cells are multipotent progenitors, capable of producing diverse cell types upon differentiation. Recent studies have identified significant heterogeneity in both the fates produced and genes expressed by different premigratory crest cells. While these cells may be specified toward particular fates prior to migration, transplant studies show that some may still be capable of respecification at this time. Here we summarize evidence that extracellular signals in the local environment may act to specify premigratory crest and thus generate diversity (...) in the population. Three main classes of signals–Wnts, BMP2/BMP4 and TGFβ1,2,3–have been shown to directly influence the production of particular neural crest cell fates, and all are expressed near the premigratory crest. This system may therefore provide a good model for integration of multiple signaling pathways during embryonic cell fate specification. BioEssays 22:708–716, 2000. © 2000 John Wiley & Sons, Inc. (shrink)

Neural crest cells are multipotent progenitors, capable of producing diverse cell types upon differentiation. Recent studies have identified significant heterogeneity in both the fates produced and genes expressed by different premigratory crest cells. While these cells may be specified toward particular fates prior to migration, transplant studies show that some may still be capable of respecification at this time. Here we summarize evidence that extracellular signals in the local environment may act to specify premigratory crest and thus generate diversity (...) in the population. Three main classes of signals–Wnts, BMP2/BMP4 and TGFβ1,2,3–have been shown to directly influence the production of particular neural crest cell fates, and all are expressed near the premigratory crest. This system may therefore provide a good model for integration of multiple signaling pathways during embryonic cell fate specification. BioEssays 22:708–716, 2000. © 2000 John Wiley & Sons, Inc. (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)

Humans in diverse cultures develop a capacity to recognize and share others’ emotional states. In this article, studies in adult and developmental populations are reviewed and synthesized to build a framework for understanding the neural bases and development of emotion recognition. It is proposed that foundations for the development of emotion recognition are provided by an experience-expectant neural circuitry that emerges early in life, biases infants to attend to biologically salient information, and is refined and specialized through experience (...) for processing species-typical signals of emotions. It is also discussed how genetic variations and experiences during sensitive periods of development can affect the developmental process and give rise to subtle individual differences in emotion recognition. (shrink)

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions. (...) The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis network, a recurrent independent component correlation algorithm [independent component analysis ] network, a t-SNE support vector machine network, a t-SNE back propagation neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency. (shrink)

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform, which is often used in the field of signal processing to (...) extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT. (shrink)

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog (...) computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

Psychedelics have been recognized as model interventions for studying altered states of consciousness. However, few empirical studies of the shamanic state of consciousness, which is anecdotally similar to the psychedelic state, exist. We investigated the neural correlates of shamanic trance using high-density electroencephalography (EEG) in 24 shamanic practitioners and 24 healthy controls during rest, shamanic drumming, and classical music listening, followed by an assessment of altered states of consciousness. EEG data were used to assess changes in absolute power, connectivity, (...) signal diversity, and criticality, which were correlated with assessment measures. We also compared assessment scores to those of individuals in a previous study under the influence of psychedelics. Shamanic practitioners were significantly different from controls in several domains of altered states of consciousness, with scores comparable to or exceeding that of healthy volunteers under the influence of psychedelics. Practitioners also displayed increased gamma power during drumming that positively correlated with elementary visual alterations. Furthermore, shamanic practitioners had decreased low alpha and increased low beta connectivity during drumming and classical music and decreased neural signal diversity in the gamma band during drumming that inversely correlated with insightfulness. Finally, criticality in practitioners was increased during drumming in the low and high beta and gamma bands, with increases in the low beta band correlating with complex imagery and elementary visual alterations. These findings suggest that psychedelic drug-induced and non-pharmacologic alterations in consciousness have overlapping phenomenal traits but are distinct states of consciousness, as reflected by the unique brain-related changes during shamanic trance compared to previous literature investigating the psychedelic state. (shrink)

To find the neural substrates of consciousness, researchers compare subjects’ neural activity when they are aware of stimuli against neural activity when they are not aware. Ideally, to guarantee that the neural substrates of consciousness—and nothing but the neural substrates of consciousness—are isolated, the only difference between these two contrast conditions should be conscious awareness. Nevertheless, in practice, it is quite challenging to eliminate confounds and irrelevant differences between conscious and unconscious conditions. In particular, there (...) is an often-neglected confound that is crucial to eliminate from neuroimaging studies: task performance. Unless subjects’ task performance is matched (and hence perceptual signal processing is matched), researchers risk finding the neural correlates of perception, rather than conscious perception. Here, we discuss the theoretical motivations for the performance matching framework and review empirical demonstrations of, and theoretical inferences derived from, obtaining differences in consciousness while controlling for task performance. We summarize signal detection theoretic modeling frameworks that explain how it is that we can derive performance-matched differences in consciousness without the effect being trivially driven by differences in criterion setting, and also provide principles for designing experimental paradigms that yield performance-matched differences in awareness. Finally, we address potential technical and theoretical issues that stem from matching performance across conditions of awareness, and we introduce the notion of “triangulation” for designing comprehensive experimental sets that can better reveal the neural substrates of consciousness. (shrink)

Can information theory be used to understand neural signaling? Yes, but assumptions have to be made about the nature of that signaling. The traditional view is that the individual neural spike is an all-or-none phenomenon, which allows neural spikes to be viewed as discrete, binary pulses, similar in kind to the signals in digital computers. Under this assumption, the tools of information theory can be used to derive results about the properties of neural signals. However, new (...) results from neuroscience demonstrate that the precise shape of the individual spike can be functionally significant, thus violating the assumption that spikes can always be treated as a binary pulse. Instead, spikes must sometimes be viewed as a continuous signal. Fortunately, information-theoretic tools exist for the study of continuous signals; unfortunately, their use in the continuous domain is very different from their use in the discrete domain, and not always well understood. Researchers interested in making precise claims about the nature of the information used, stored, and processed in neural systems must pay careful attention to these differences. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in (...) predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance (...) on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty, Behaviorally Adaptive Training, and Combined Adaptive Training using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

Possible systemic effects of general anesthetic agents on neural information processing are discussed in the context of the thalamocortical suppression hypothesis presented by Drs. Alkire, Haier, and Fallon (this issue) in their PET study of the anesthetized state. Accounts of the neural requisites of consciousness fall into two broad categories. Neuronal-specificity theories postulate that activity in particular neural populations is sufficient for conscious awareness, while process-coherence theories postulate that particular organizations of neural activity are sufficient. Accounts (...) of anesthetic narcosis, on the other hand, explain losses of consciousness in terms of neural signal-suppressions, transmission blocks, and the disruptions of signal interpretation. While signal-suppression may account for the actions of some anesthetic agents, the existence of anesthetics, such as choralose, that cause both loss of consciousness and elevated discharge rates, is problematic for a general theory of narcosis that is based purely on signal suppression and transmission-block. However, anesthetic agents also alter relative firing rates and temporal discharge patterns that may disrupt the coherence of neural signals and the functioning of the neural networks that interpret them. It is difficult at present, solely on the basis of regional brain metabolic rates, to test process-coherence hypotheses regarding organizational requisites for conscious awareness. While these pioneering PET studies have great merit as panoramic windows of mind-brain correlates, wider ranges of theory and empirical evidence need to be brought into the formulation of truly comprehensive theories of consciousness and anesthesia. (shrink)

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

Lifelong premature ejaculation (LPE) is a common male sexual dysfunction. Lack of active control for rapid ejaculation brought great distress to sexual harmony, and even fertility. Previous neurophysiology studies revealed an ejaculation-related control mechanism in the brain. However, it remains unclear whether this inhibitory network is altered in LPE patients. The present study investigated the central inhibitory network function of LPE patients by using stop signal task-related functional magnetic resonance imaging and resting-state functional connectivity analysis. The results showed no (...) difference in task-related behavioral performance or neural activation during response inhibition between LPE patients and controls. However, LPE patients showed a significantly different correlation pattern between the stop signal reaction time (SSRT) and left inferior frontal gyrus activation during successful inhibition, in which a typical negative correlation between SSRT and the activation was completely disappeared in patients. In addition, using the left inferior frontal gyrus as a seed, patients showed weaker functional connectivity between the seed and two areas (left dental nucleus and right frontal pole) compared with controls. These data suggest that LPE patients have an abnormal brain control network, which may contribute to the reduced central control of rapid ejaculation. This study provides new insights into the neural mechanism of LPE involving the central inhibitory network, which may offer an underlying intervention target for future treatment. (shrink)

Neuroscientists continue to search for 'the' neural correlate of consciousness (NCC). In this article, I argue that a framework in which there are at least two distinct NCCs is increasingly making more sense of empirical results than one in which there is a single NCC. I outline the distinction between phenomenal NCC and access NCC, and show how they can be distinguished by experimental approaches, in particular signal- detection theory approaches. Recent findings in cognitive neuroscience provide an empirical (...) case for two different NCCs. (shrink)

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla–Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction (...) error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor. Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold. (shrink)

Block [Block, N. . Two neural correlates of consciousness. Trends in Cognitive Science, 9, 46–52] and Snodgrass claim that a signal detection theory analysis of qualitative difference paradigms, in particular the exclusion failure paradigm, reveals cases of phenomenal consciousness without access consciousness. This claim is unwarranted on several grounds. First, partial cognitive access rather than a total lack of cognitive access can account for exclusion failure results. Second, Snodgrass’s Objective Threshold/Strategic model of perception relies on a problematic ‘enable’ (...) approach to perception that denies the possibility of intentional control of unconscious perception and any effect of following different task instructions on the presence/absence of phenomenal consciousness. Many of Block’s purported examples of phenomenal consciousness without cognitive access also rely on this problematic approach. Third, qualitative difference paradigms may index only a subset of access consciousness. Thus, qualitative difference paradigms like exclusion failure cannot be used to isolate phenomenal consciousness, any attempt to do so still faces serious methodological problems. (shrink)

Background: The regulation of muscle force is a vital aspect of sensorimotor control, requiring intricate neural processes. While neural activity associated with upper extremity force control has been documented, extrapolation to lower extremity force control is limited. Knowledge of how the brain regulates force control for knee extension and flexion may provide insights as to how pathology or intervention impacts central control of movement.Objectives: To develop and implement a neuroimaging-compatible force control paradigm for knee extension and flexion.Methods: A (...) magnetic resonance imaging safe load cell was used in a customized apparatus to quantify force during neuroimaging. Visual biofeedback and a target sinusoidal wave that fluctuated between 0 and 5 N was provided via an MRI-safe virtual reality display. Fifteen right leg dominant female participants completed a knee extension and flexion force matching paradigm during neuroimaging. The force-matching error was calculated based on the difference between the visual target and actual performance. Brain activation patterns were calculated and associated with force-matching error and the difference between quadriceps and hamstring force-matching tasks were evaluated with a mixed-effects model.Results: Knee extension and flexion force-matching tasks increased BOLD signal among cerebellar, sensorimotor, and visual-processing regions. Increased knee extension force-matching error was associated with greater right frontal cortex and left parietal cortex activity and reduced left lingual gyrus activity. Increased knee flexion force-matching error was associated with reduced left frontal and right parietal region activity. Knee flexion force control increased bilateral premotor, secondary somatosensory, and right anterior temporal activity relative to knee extension. The force-matching error was not statistically different between tasks.Conclusion: Lower extremity force control results in unique activation strategies depending on if engaging knee extension or flexion, with knee flexion requiring increased neural activity for the same level of force and no difference in relative error. These fMRI compatible force control paradigms allow precise behavioral quantification of motor performance concurrent with brain activity for lower extremity sensorimotor function and may serve as a method for future research to investigate how pathologies affect lower extremity neuromuscular function. (shrink)

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when (...) the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the (...) other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

A central source of socially meaningful signals is the face, which can be visually analyzed to understand a person's emotions, intentions, beliefs, and desires, along with information about that person's social status, approachability, age, and gender. This article reviews the neural basis of the perception of such signals in humans, focusing on facial expression and gaze, and touching on lesser-studied signals such as pupil dilation and blushing. It discusses the involvement of structures such as the insula, orbitofrontal cortex, amygdala, (...) and superior temporal gyrus, which are studied in some detail with respect to how they process social stimuli. It reveals that social perception also involves information from other modalities and depends on the context in which signals occur. (shrink)

This paper focuses on neural learning from adaptive neural control for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number (...) of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation condition for neural networks. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme. (shrink)

The global signal, which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS (...) correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography. (shrink)

In this article I evaluate recent attempts to illuminate the human infant cry from an evolutionary perspective. Infants are born into an uncertain parenting environment, which can range from indulgent care of offspring to infanticide. Infant cries are in large part adaptations that maintain proximity to and elicit care from caregivers. Although there is not strong evidence for acoustically distinct cry types, infant cries may function as a graded signal. During pain-induced autonomic nervous system arousal, for example, neural (...) input to the vocal cords increases cry pitch. Caregivers may use this acoustic information, together with other cues, to guide caregiving behavior. Serious pathology, on the other hand, results in chronically and severely abnormal cry acoustics. Such abnormal crying may be a proximate cause of adaptive infant maltreatment, in circumstances in which parents cut their losses and reduce or withdraw investment from infants with low survival chances. An increase in the amount of crying during the first few months of life is a human universal, and excessive crying, or colic, represents the upper end of this normal increase. Potential signal functions of excessive crying include manipulation of parents to acquire additional resources, honest signaling of need, and honest signaling of vigor. Current evidence does not strongly support any one of these hypotheses, but the evidence is most consistent with the hypothesis that excessive early infant crying is a signal of vigor that evolved to reduce the risk of a reduction or withdrawal of parental care. Key Words: colic; crying; early infant crying; honest signaling; infanticide; parental care; parent-offspring conflict; separation call; vocalization. (shrink)

Tactile distance perception is believed to require that immediate afferent signals be referenced to a stored representation of body size and shape (the body model). For this ability, recent studies have reported that the stored body representations involved are highly distorted, at least in the case of the hand, with the hand dorsum represented as wider and squatter than it actually is. Here, we aim to define the neural basis of this phenomenon. In a behavioural experiment participants estimated the (...) distance between touches on two points by adjusting the length of a visually-presented line on the screen. The technique of multidensional scaling (MDS) was used to reconstruct a perceptual map of tactile space. Analysis of spatial distortion using Procrustes alignment showed that maps were stretched in the mediolateral hand axis. In order to determine the neural correlates of these body distortions, we performed an fMRI study. For each participant, we used a searchlight pattern classifier with Euclidean distance on pre-defined regions of interests (ROIs). In order to relate the representations between the different points and to computational models, we compare response-pattern dissimilarity matrices in these ROIs. Similar to the behavioural experiment, we used MDS to reconstruct maps of the neural representation of tactile space using the values from the dissimilarities matrices. We were able to reconstruct the perceptual map of tactile space in the contralateral primary somatosensory and motor cortices. This suggests that these areas are critical to generate the tactile representations of the dorsum of the hand. (shrink)

Recent neural models clarify many properties of mental imagery as part of the process whereby bottom-up visual information is influenced by top-down expectations, and how these expectations control visual attention. Volitional signals can transform modulatory top-down signals into supra-threshold imagery. Visual hallucinations can occur when the normal control of these volitional signals is lost.

Neural cell adhesion molecules of the immunoglobulin superfamily are important components of the network of guidance cues and receptors that govern axon growth and guidance during development. For neural cell adhesion molecule L1, the combined application of human genetics, knockout mouse technology, and cell biology is providing fundamental insight into the role of L1 in mediating neuronal differentiation. Disease-causing mutations as well as mouse models of L1 disruption can now be used to examine the relevance of L1 binding (...) specificities and signal transduction pathways that have been observed in vitro. BioEssays 20:668–675, 1998.© 1998 John Wiley & Sons Inc. (shrink)

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We (...) find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses. (shrink)

Neural cell adhesion molecules of the immunoglobulin superfamily are important components of the network of guidance cues and receptors that govern axon growth and guidance during development. For neural cell adhesion molecule L1, the combined application of human genetics, knockout mouse technology, and cell biology is providing fundamental insight into the role of L1 in mediating neuronal differentiation. Disease-causing mutations as well as mouse models of L1 disruption can now be used to examine the relevance of L1 binding (...) specificities and signal transduction pathways that have been observed in vitro. BioEssays 20:668–675, 1998.© 1998 John Wiley & Sons Inc. (shrink)