Issue In Neuroscience Research Articles

Application of a Prediction Error Theory to Pavlovian Conditioning in an Insect.

Elucidation of the conditions in which associative learning occurs is a critical issue in neuroscience and comparative psychology. In Pavlovian conditioning in mammals, it is thought that the discrepancy, or error, between the actual reward and the predicted reward determines whether learning occurs. This theory stems from the finding of Kamin’s blocking effect, in which after pairing of a stimulus with an unconditioned stimulus (US), conditioning of a second stimulus is blocked when the two stimuli are presented in compound and paired with the same US. Whether this theory is applicable to any species of invertebrates, however, has remained unknown. We first showed blocking and one-trial blocking of Pavlovian conditioning in the cricket Gryllus bimaculatus, which supported the Rescorla–Wagner model but not attentional theories, the major competitive error-correction learning theories to account for blocking. To match the prediction error theory, a neural circuit model was proposed, and prediction from the model was tested: the results were consistent with the Rescorla–Wagner model but not with the retrieval theory, another competitive theory to account for blocking. The findings suggest that the Rescorla–Wagner model best accounts for Pavlovian conditioning in crickets and that the basic computation rule underlying Pavlovian conditioning in crickets is the same to those suggested in mammals. Moreover, results of pharmacological studies in crickets suggested that octopamine and dopamine mediate prediction error signals in appetitive and aversive conditioning, respectively. This was in contrast to the notion that dopamine mediates appetitive prediction error signals in mammals. The functional significance and evolutionary implications of these findings are discussed.

Amygdala stimulation promotes recovery of behavioral performance in a spatial memory task and increases GAP-43 and MAP-2 in the hippocampus and prefrontal cortex of male rats

The relationships between affective and cognitive processes are an important issue of present neuroscience. The amygdala, the hippocampus and the prefrontal cortex appear as main players in these mechanisms. We have shown that post-training electrical stimulation of the basolateral amygdala (BLA) speeds the acquisition of a motor skill, and produces a recovery in behavioral performance related to spatial memory in fimbria-fornix (FF) lesioned animals. BLA electrical stimulation rises bdnf RNA expression, BDNF protein levels, and arc RNA expression in the hippocampus. In the present paper we have measured the levels of one presynaptic protein (GAP-43) and one postsynaptic protein (MAP-2) both involved in synaptogenesis to assess whether structural neuroplastic mechanisms are involved in the memory enhancing effects of BLA stimulation. A single train of BLA stimulation produced in healthy animals an increase in the levels of GAP-43 and MAP-2 that lasted days in the hippocampus and the prefrontal cortex. In FF-lesioned rats, daily post-training stimulation of the BLA ameliorates the memory deficit of the animals and induces an increase in the level of both proteins. These results support the hypothesis that the effects of amygdala stimulation on memory recovery are sustained by an enhanced formation of new synapses.

Body ownership and agency altered by an electromyographically controlled robotic arm.

Understanding how we consciously experience our bodies is a fundamental issue in cognitive neuroscience. Two fundamental components of this are the sense of body ownership (the experience of the body as one's own) and the sense of agency (the feeling of control over one's bodily actions). These constructs have been used to investigate the incorporation of prostheses. To date, however, no evidence has been provided showing whether representations of ownership and agency in amputees are altered when operating a robotic prosthesis. Here we investigated a robotic arm using myoelectric control, for which the user varied the joint position continuously, in a rubber hand illusion task. Fifteen able-bodied participants and three trans-radial amputees were instructed to contract their wrist flexors/extensors alternately, and to watch the robotic arm move. The sense of ownership in both groups was extended to the robotic arm when the wrists of the real and robotic arm were flexed/extended synchronously, with the effect being smaller when they moved in opposite directions. Both groups also experienced a sense of agency over the robotic arm. These results suggest that these experimental settings induced successful incorporation of the prosthesis, at least for the amputees who took part in the present study.

Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data.

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

Feature binding pulse-coupled neural network model using a double colour space

The feature binding problem is one of the central issues in cognitive science and neuroscience. To implement a bundled identification of colour and shape for a colour image, a double-space vector feature binding PCNN (DVFB-PCNN) model was proposed based on the traditional pulse-coupled neural network (PCNN). In this model, the method of combining RGB colour space with HSI colour space successfully solved the problem that all colours cannot always be separated completely. Through the use of the first pulse emission time of the neurons, the different characteristics were successfully separated. Through the colour sequence produced by this process, the different characteristics belonging to the same perceived object were bound together. Experiments showed that the model can successfully achieve separation and binding of image features and will be a valuable tool for PCNN in the feature binding of colour images.

Spike-Triggered Regression for Synaptic Connectivity Reconstruction in Neuronal Networks.

How neurons are connected in the brain to perform computation is a key issue in neuroscience. Recently, the development of calcium imaging and multi-electrode array techniques have greatly enhanced our ability to measure the firing activities of neuronal populations at single cell level. Meanwhile, the intracellular recording technique is able to measure subthreshold voltage dynamics of a neuron. Our work addresses the issue of how to combine these measurements to reveal the underlying network structure. We propose the spike-triggered regression (STR) method, which employs both the voltage trace and firing activity of the neuronal population to reconstruct the underlying synaptic connectivity. Our numerical study of the conductance-based integrate-and-fire neuronal network shows that only short data of 20 ~ 100 s is required for an accurate recovery of network topology as well as the corresponding coupling strength. Our method can yield an accurate reconstruction of a large neuronal network even in the case of dense connectivity and nearly synchronous dynamics, which many other network reconstruction methods cannot successfully handle. In addition, we point out that, for sparse networks, the STR method can infer coupling strength between each pair of neurons with high accuracy in the absence of the global information of all other neurons.

Attentional modulation of early visual areas

ABSTRACTThis special issue of Cognitive Neuroscience focuses on the debate regarding whether spatial attention can rapidly modulate the event-related potential (ERP) C1 component, which reflects the initial feedforward signal in V1. A discussion paper by Baumgartner, Graulty, Hillyard, and Pitts (this issue) included an empirical experiment that failed to replicate the significant C1 attention effects of Kelly, Gomez-Ramirez, and Foxe. Commentaries were received by Ding (this issue), Klein (this issue), Pourtois, Rossi, Vuilleumier, and Rauss (this issue), Kelly and Mohr (this issue), Slagter, Alilovic, and Van Gaal (this issue), and Fu (this issue). The author response highlighted that C1 attention effects may be obtained in only limited conditions, considered problems of investigating C1 attention effects, and proposed future strategies. An empirical paper by Herde, Rossi, Pourtois, and Rauss (this issue) found that C1 was modulated by stimulus predictability, which indicates higher-level cognitive processes can modulate V1. A second discussion paper (Slotnick, this issue) compared experimental parameters across spatial attention studies and concluded certain stimulus, task, and analysis conditions were more likely to produce significant C1 attention effects. Commentaries were received by Fu (this issue), Qu and Ding (this issue), Zani and Proverbio (this issue), Pitts and Hillyard (this issue), Di Russo (this issue), and Mohr and Kelly (this issue). The author response included a more in depth analysis, and several studies that observed significant C1 attention effects survived critical analysis. The topics considered in this issue have clarified the current state of this debate and provided directions for future research.

A Linear Approximate Model of Creativity in Quantum and Chaos Theory

The appearance of creativity is one of the most attractive issues in neuroscience and psychology. Creativity has been investigated qualitatively by using concepts of quantum and chaos theory since the end of the 20th century. Here, we show an explicit mathematical model which tries to explain the dynamics on the development of creativity. The present model is constructed by a linear approximation of chaos theory in nonlinear dynamical system. Also, it is the classical approximation of quantum mechanics, in which Newtonian mechanics is derived. Significant feature of the creativity is an existence of discontinuity which may relate to sudden appearance of idea. Such discontinuous nature relates not only to nonlinear dynamics but also to quantum theory. In this study, we first investigate the characteristics of creative attitudes by means of factor analysis and abstract two chief factors of the creative attitudes; that are “efforts and durability” and “independence and originality”. Moreover, we find a significant positive relationship between the emotional experiences and the two creative attitudes. Scholastic ability judged by paper tests, on the other hand, also has a significant positive relationship with the factor of “effort and durability” but not with that of “independence and originality”. Secondly, we build a mathematical model on the change of the two chief factors of creative attitudes using the linear approximation in nonlinear dynamical systems. We use the Dirac delta function to express the discontinuity. The calculated results are expressed in a relation between the novelty and the workload. The present linear approximation model should be the first one to build mathematically a predictive model of creativity.

Unique Configurations of Compression and Truncation of Neuronal Activity Underlie l-DOPA-Induced Selection of Motor Patterns in Aplysia.

A key issue in neuroscience is understanding the ways in which neuromodulators such as dopamine modify neuronal activity to mediate selection of distinct motor patterns. We addressed this issue by applying either low or high concentrations of l-DOPA (40 or 250 μM) and then monitoring activity of up to 130 neurons simultaneously in the feeding circuitry of Aplysia using a voltage-sensitive dye (RH-155). l-DOPA selected one of two distinct buccal motor patterns (BMPs): intermediate (low l-DOPA) or bite (high l-DOPA) patterns. The selection of intermediate BMPs was associated with shortening of the second phase of the BMP (retraction), whereas the selection of bite BMPs was associated with shortening of both phases of the BMP (protraction and retraction). Selection of intermediate BMPs was also associated with truncation of individual neuron spike activity (decreased burst duration but no change in spike frequency or burst latency) in neurons active during retraction. In contrast, selection of bite BMPs was associated with compression of spike activity (decreased burst latency and duration and increased spike frequency) in neurons projecting through specific nerves, as well as increased spike frequency of protraction neurons. Finally, large-scale voltage-sensitive dye recordings delineated the spatial distribution of neurons active during BMPs and the modification of that distribution by the two concentrations of l-DOPA.

Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity

While averaged dynamics of brain function are known to estimate the underlying structure, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. These complex functional dynamics, measured by EEG and fMRI, are thought to arise from a shared underlying structural architecture, which can be measured by diffusion MRI (dMRI). While simulation and data transformation (e.g. graph theory measures) have been proposed to refine the understanding of the underlying function-structure relationship, the potential complementary and/or independent contribution of EEG and fMRI to this relationship is still poorly understood.As such, we explored this relationship by analyzing the function-structure correlation in fourteen healthy subjects with simultaneous resting-state EEG-fMRI and dMRI acquisitions. We show that the combination of EEG and fMRI connectivity better explains dMRI connectivity and that this represents a genuine model improvement over fMRI-only models for both group-averaged connectivity matrices and at the individual level. Furthermore, this model improves the prediction within each resting-state network. The best model fit to underlying structure is mediated by fMRI and EEG-δ connectivity in combination with Euclidean distance and interhemispheric connectivity with more local contributions of EEG-γ at the scale of resting-state networks.This highlights that the factors mediating the relationship between functional and structural metrics of connectivity are context and scale dependent, influenced by topological, geometric and architectural features. It also suggests that fMRI studies employing simultaneous EEG measures may characterize additional and essential parts of the underlying neuronal activity of the resting-state, which might be of special interest for both clinical studies and the investigation of resting-state dynamics.

Morphology of Dbx1 respiratory neurons in the preB\xf6tzinger complex and reticular formation of neonatal mice

The relationship between neuron morphology and function is a perennial issue in neuroscience. Information about synaptic integration, network connectivity, and the specific roles of neuronal subpopulations can be obtained through morphological analysis of key neurons within a microcircuit. Here we present morphologies of two classes of brainstem respiratory neurons. First, interneurons derived from Dbx1-expressing precursors (Dbx1 neurons) in the preBötzinger complex (preBötC) of the ventral medulla that generate the rhythm for inspiratory breathing movements. Second, Dbx1 neurons of the intermediate reticular formation that influence the motor pattern of pharyngeal and lingual movements during the inspiratory phase of the breathing cycle. We describe the image acquisition and subsequent digitization of morphologies of respiratory Dbx1 neurons from the preBötC and the intermediate reticular formation that were first recorded in vitro. These data can be analyzed comparatively to examine how morphology influences the roles of Dbx1 preBötC and Dbx1 reticular interneurons in respiration and can also be utilized to create morphologically accurate compartmental models for simulation and modeling of respiratory circuits.

Neuroethics: the institutionalization of ethics in neuroscience

Abstract Recent advances in neuroscience have led to numerous ethical questions. Neuroethics is the study of ethical, legal and social advancements in neuroscience which, despite being a recently developed discipline, has a long historical tradition. The concern with ethical issues in neuroscience is extremely old and dates back to the philosophical and scientific traditions that originally sought to understand the relationship between the brain and behavior. More recently, the field of neuroethics has emerged in the context of public and academic debate over the consequences of the advances arising from neuroscience. In the current context, technological innovations, which have arisen from the expansion of neuroscience research, have raised new ethical dilemmas. This article aims to analyze the historical course of ethics within the field of neuroscience, specifically the appearance and the recent institutionalization of neuroethics.

What is "the unconscious," and where is it located in the brain? A neuropsychoanalytic perspective.

This is a brief overview of my "neuropsychoanalytic" perspective on the unconscious. It should make clear how much psychoanalysis has to gain from incorporating the findings of neuroscientific disciplines studying the same part of nature-the workings of the human mind. I hope it makes equally clear what useful new perspectives can be cast on current issues in cognitive neuroscience, if they, in turn, incorporate the findings of psychoanalysis.

Electric stimulation of the right temporo-parietal junction induces a task-specific effect in deceptive behaviors

How the brain generates a lie is an important and unsolved issue in neuroscience. Previous studies indicated that mentalizing, the ability to understand and manipulate the mental states of others, plays a critical role in successful deception. Accordingly, recent neuroimaging studies reported deception-related activity in the right temporo-parietal junction (rTPJ), a brain region closely related to the mentalizing ability. Detailed functions of rTPJ in deception, however, remain unclear. In the present study, we investigated a causal relationship between rTPJ and deception using transcranial direct-current stimulation (tDCS). Subjects received anodal tDCS to their rTPJ or V1 (control) and then performed three tasks in which they aimed to deceive another participant to get monetary rewards. In one of the three tasks, we found a significant decrease in a rate of successful deception when rTPJ was stimulated, indicating that neural enhancement of rTPJ caused poorer (not better) deceptive performances. Our results suggest that, in some tasks involving selfish (money-motivated) lying, neural processing in rTPJ does not contribute to successful deception through the metalizing ability. Rather, it would be related to the self-monitoring of morally-unacceptable behaviors (lying). The neural enhancement of rTPJ therefore increased the psychological resistance to lying, resulting in poorer deceptive performances.

Different roles for inhibition in the rhythm-generating respiratory network.

Unraveling the interplay of excitation and inhibition within rhythm-generating networks remains a fundamental issue in neuroscience. We use a biophysical model to investigate the different roles of local and long-range inhibition in the respiratory network, a key component of which is the pre-Bötzinger complex inspiratory microcircuit. Increasing inhibition within the microcircuit results in a limited number of out-of-phase neurons before rhythmicity and synchrony degenerate. Thus unstructured local inhibition is destabilizing and cannot support the generation of more than one rhythm. A two-phase rhythm requires restructuring the network into two microcircuits coupled by long-range inhibition in the manner of a half-center. In this context, inhibition leads to greater stability of the two out-of-phase rhythms. We support our computational results with in vitro recordings from mouse pre-Bötzinger complex. Partial excitation block leads to increased rhythmic variability, but this recovers after blockade of inhibition. Our results support the idea that local inhibition in the pre-Bötzinger complex is present to allow for descending control of synchrony or robustness to adverse conditions like hypoxia. We conclude that the balance of inhibition and excitation determines the stability of rhythmogenesis, but with opposite roles within and between areas. These different inhibitory roles may apply to a variety of rhythmic behaviors that emerge in widespread pattern-generating circuits of the nervous system.NEW & NOTEWORTHY The roles of inhibition within the pre-Bötzinger complex (preBötC) are a matter of debate. Using a combination of modeling and experiment, we demonstrate that inhibition affects synchrony, period variability, and overall frequency of the preBötC and coupled rhythmogenic networks. This work expands our understanding of ubiquitous motor and cognitive oscillatory networks.

Action Organization in Lateral Occipitotemporal Cortex.

Being able to recognize what other people are doing is crucial in complex social interactions. How this is achieved quasi-automatically by our brains has been a longstanding issue in cognitive neuroscience. Experiments using fMRI show that perceiving another person's action activates, to a large

Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity.

Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.

Neural mechanisms of cognitive control / Mecanismos neurales de control cognitivo

Understanding the neural basis of cognitive control is a central issue in cognitive neuroscience, given its core importance for the flexibility that characterizes human behaviour. This review integrates the main findings in the field, underscoring the role of fronto-parietal regions in both proactive (representing tasks in anticipation to prepare the system for action) and reactive (detecting and resolving conflicts in processing) control. In addition, we review the dynamics of interaction between these areas and other brain regions in the range of slow frequencies. Finally, we highlight central questions in the field that have yet to be answered.

Sensitivity to carbon dioxide and translational studies of anxiety disorders

Heightened concentrations of CO2 in inhaled air provoke temporary acidification of the brain, followed by compensatory hyperventilation and increased arousal/anxiety. These responses are likely to map a basic, latent general alarm/avoidance system that is largely shared across mammals, and are sources of individual differences. By showing paroxysmal respiratory and emotional responses to CO2 challenges, humans with panic and separation anxiety disorders lie at one extreme of the distribution for CO2 sensitivity. This is also a developmental trait, sensitive to interference with parental cares.By sharing CO2 sensitivity with humans, rodents constitute a valuable resource to model panic and separation anxiety in the laboratory. Advantages of modeling CO2 sensitivity in rodents include non-inferential measurements (e.g. respiratory readouts) as proxies for human conditions, unbiased investigation of gene–environment interplays, and flexible availability of tissues for mechanistic studies.Data in humans and animals such as those reported in this issue of Neuroscience begin to reveal that CO2-driven behavioral responses stem from anatomo-physiological systems that are relatively separated from those subserving general dispositions to anxiety. This supports the notion that sensitivity to suffocative stimuli and ensuing human panic are significantly independent from trait/cognitive anxiety, and corroborates newer conceptualizations that distinguish between fear and anxiety circuitries.

Introduction to special issue: Attention & Plasticity

ABSTRACT Mechanisms of attention are a prime target for investigating the plasticity of the adult brain, as these core mechanisms act at the intersection of top-down and bottom-up processing, and the wide variety of methods used in attention research can be utilized to elucidate the mechanisms of plasticity. This special issue of Cognitive Neuroscience presents three new empirical papers and a discussion paper with peer commentaries. In the first article, Voelker, Sheese, and colleagues investigate the influence of genetic variation on the effectiveness of attention training. Della Libera and colleagues then present a study investigating how individual differences in personality traits affect the acquisition of reward-based attention biases. In the final empirical paper, Hopfinger and colleagues present a transcranial stimulation study investigating the influence of different oscillatory stimulations on the efficiency of attentional reorienting. Finally, Voelker, Piscopo, and colleagues present a discussion paper in which they suggest that successful training of attention is linked to changes in the underlying white matter. The papers in this special issue present a sampling of the range of issues and methodologies being brought to bear to further our understanding of the malleability of the mechanisms of attention and of the plasticity of the brain.