Hardware implementation of an analog neural nonderivative optimizer

Analog neural systems that can automatically find the minimum value of the outputs of unknown analog systems, described by convex functions, are studied. When information about derivative or gradient are not used, these systems are called analog nonderivative optimizers. An electronic circuit for the analog neural nonderivative optimizer proposed by Teixeira and Zak, and its simulation with software PSPICE, is presented. With the simulation results and hardware implementation of the system, the validity of the proposed optimizer can be verified. These results are original, from the best of the authors knowledge.

Oxytocin shapes the neural circuitry of trust and trust adaptation in humans.

Trust and betrayal of trust are ubiquitous in human societies. Recent behavioral evidence shows that the neuropeptide oxytocin increases trust among humans, thus offering a unique chance of gaining a deeper understanding of the neural mechanisms underlying trust and the adaptation to breach of trust. We examined the neural circuitry of trusting behavior by combining the intranasal, double-blind, administration of oxytocin with fMRI. We find that subjects in the oxytocin group show no change in their trusting behavior after they learned that their trust had been breached several times while subjects receiving placebo decrease their trust. This difference in trust adaptation is associated with a specific reduction in activation in the amygdala, the midbrain regions, and the dorsal striatum in subjects receiving oxytocin, suggesting that neural systems mediating fear processing (amygdala and midbrain regions) and behavioral adaptations to feedback information (dorsal striatum) modulate oxytocin's effect on trust. These findings may help to develop deeper insights into mental disorders such as social phobia and autism, which are characterized by persistent fear or avoidance of social interactions.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Neural codes: Firing rates and beyond

Computational neuroscience has contributed significantly to our understanding of higher brain function by combining experimental neurobiology, psychophysics, modeling, and mathematical analysis. This article reviews recent advances in a key area: neural coding and information processing. It is shown that synapses are capable of supporting computations based on highly structured temporal codes. Such codes could provide a substrate for unambiguous representations of complex stimuli and be used to solve difficult cognitive tasks, such as the binding problem. Unsupervised learning rules could generate the circuitry required for precise temporal codes. Together, these results indicate that neural systems perform a rich repertoire of computations based on action potential timing.

The role of stochasticity in an information-optimal neural population code

In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise, in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and; hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

Individual Differences in the Neural Response to Personal Goal Pursuit Success and Failure: A Developmental Analysis

Regulatory focus theory (RFT) proposes two different social-cognitive motivational systems for goal pursuit: a promotion system, which is organized around strategic approach behaviors and "making good things happen," and a prevention system, which is organized around strategic avoidance and "keeping bad things from happening." The promotion and prevention systems have been extensively studied in behavioral paradigms, and RFT posits that prolonged perceived failure to make progress in pursuing promotion or prevention goals can lead to ineffective goal pursuit and chronic distress (Higgins, 1997).

Research has begun to focus on uncovering the neural correlates of the promotion and prevention systems in an attempt to differentiate them at the neurobiological level. Preliminary research suggests that the promotion and prevention systems have both distinct and overlapping neural correlates (Eddington, Dolcos, Cabeza, Krishnan, & Strauman, 2007; Strauman et al., 2013). However, little research has examined how individual differences in regulatory focus develop and manifest. The development of individual differences in regulatory focus is particularly salient during adolescence, a crucial topic to explore given the dramatic neurodevelopmental and psychosocial changes that take place during this time, especially with regard to self-regulatory abilities. A number of questions remain unexplored, including the potential for goal-related neural activation to be modulated by (a) perceived proximity to goal attainment, (b) individual differences in regulatory orientation, specifically general beliefs about one's success or failure in attaining the two kinds of goals, (c) age, with a particular focus on adolescence, and (d) homozygosity for the Met allele of the catechol-O-methyltransferase (COMT) Val158Met polymorphism, a naturally occurring genotype which has been shown to impact prefrontal cortex activation patterns associated with goal pursuit behaviors.

This study explored the neural correlates of the promotion and prevention systems through the use of a priming paradigm involving rapid, brief, masked presentation of individually selected promotion and prevention goals to each participant while being scanned. The goals used as priming stimuli varied with regard to whether participants reported that they were close to or far away from achieving them (i.e. a "match" versus a "mismatch" representing perceived success or failure in personal goal pursuit). The study also assessed participants' overall beliefs regarding their relative success or failure in attaining promotion and prevention goals, and all participants were genotyped for the COMT Val158Met polymorphism.

A number of significant findings emerged. Both promotion and prevention priming were associated with activation in regions associated with self-referential cognition, including the left medial prefrontal cortex, cuneus, and lingual gyrus. Promotion and prevention priming were also associated with distinct patterns of neural activation; specifically, left middle temporal gyrus activation was found to be significantly greater during prevention priming. Activation in response to promotion and prevention goals was found to be modulated by self-reports of both perceived proximity to goal achievement and goal orientation. Age also had a significant effect on activation, such that activation in response to goal priming became more robust in the prefrontal cortex and in default mode network regions as a function of increasing age. Finally, COMT genotype also modulated the neural response to goal priming both alone and through interactions with regulatory focus and age. Overall, these findings provide further clarification of the neural underpinnings of the promotion and prevention systems as well as provide information about the role of development and individual differences at the personality and genetic level on activity in these neural systems.

Exotic dynamics in a firing rate model of neural tissue with threshold accommodation

Many of the equations describing the dynamics of neural systems are written in terms of firing rate functions, which themselves are often taken to be threshold functions of synaptic activity. Dating back to work by Hill in 1936 it has been recognized that more realistic models of neural tissue can be obtained with the introduction of state-dependent dynamic thresholds. In this paper we treat a specific phenomenological model of threshold accommodation that mimics many of the properties originally described by Hill. Importantly we explore the consequences of this dynamic threshold at the tissue level, by modifying a standard neural field model of Wilson-Cowan type. As in the case without threshold accommodation classical Mexican-Hat connectivity is shown to allow for the existence of spatially localized states (bumps) in both one and two dimensions. Importantly an analysis of bump stability in one dimension, using recent Evans function techniques, shows that bumps may undergo instabilities leading to the emergence of both breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. In the regime where a bump solution does not exist direct numerical simulations show the possibility of self-replicating bumps via a form of bump splitting. Simulations in two space dimensions show analogous localized and traveling solutions to those seen in one dimension. Indeed dynamical behavior in this neural model appears reminiscent of that seen in other dissipative systems that support localized structures, and in particular those of coupled cubic complex Ginzburg-Landau equations. Further numerical explorations illustrate that the traveling pulses in this model exhibit particle like properties, similar to those of dispersive solitons observed in some three component reaction-diffusion systems. A preliminary account of this work first appeared in S Coombes and M R Owen, Bumps, breathers, and waves in a neural network with spike frequency adaptation, Physical Review Letters 94 (2005), 148102(1-4).

Action and perception: Neural indices of learning in infants

Early human development offers a unique perspective in investigating the potential cognitive and social implications of action and perception. Specifically, during infancy, action production and action perception undergo foundational developments. One essential component to examine developments in action processing is the analysis of others’ actions as meaningful and goal-directed. Little research, however, has examined the underlying neural systems that may be associated with emerging action and perception abilities, and infants’ learning of goal-directed actions. The current study examines the mu rhythm—a brain oscillation found in the electroencephalogram (EEG)—that has been associated with action and perception. Specifically, the present work investigates whether the mu signal is related to 9-month-olds’ learning of a novel goal-directed means-end task. The findings of this study demonstrate a relation between variations in mu rhythm activity and infants’ ability to learn a novel goal-directed means-end action task (compared to a visual pattern learning task used as a comparison task). Additionally, we examined the relations between standardized assessments of early motor competence, infants’ ability to learn a novel goal-directed task, and mu rhythm activity. We found that: 1a) mu rhythm activity during observation of a grasp uniquely predicted infants’ learning on the cane training task, 1b) mu rhythm activity during observation and execution of a grasp did not uniquely predict infants’ learning on the visual pattern learning task (comparison learning task), 2) infants’ motor competence did not predict infants’ learning on the cane training task, 3) mu rhythm activity during observation and execution was not related to infants’ measure of motor competence, and 4) mu rhythm activity did not predict infants’ learning on the cane task above and beyond infants’ motor competence. The results from this study demonstrate that mu rhythm activity is a sensitive measure to detect individual differences in infants’ action and perception abilities, specifically their learning of a novel goal-directed action.

Frequency Domain Connectivity Identification: An Application of Partial Directed Coherence in fMRI

Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 30:452-461, 2009. (C) 2007 Wiley-Liss, Inc.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Abnormal Visual Activation in Parkinson`s Disease Patients

Among nonmotor symptoms observed in Parkinson`s disease (PD) dysfunction in the visual system, including hallucinations, has a significant impact in their quality of life. To further explore the visual system in PD patients we designed two fMRI experiments comparing 18 healthy volunteers with 16 PD patients without visual complaints in two visual fMRI paradigms: the flickering checkerboard task and a facial perception paradigm. PD patients displayed a decreased activity in the primary visual cortex (Broadmann area 17) bilaterally as compared to healthy volunteers during flickering checkerboard task and increased activity in fusiform gyms (Broadmann area 37) during facial perception paradigm. Our findings confirm the notion that PD patients show significant changes in the visual cortex system even before the visual symptoms are clinically evident. Further studies are necessary to evaluate the contribution of these abnormalities to the development visual symptoms in PD. (C) 2010 Movement Disorder Society

Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder

Emotional liability and mood dysregulation characterize bipolar disorder (BID), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Participants comprised 46 individuals (age range: 18-56 years): 21 with a DSM-IV diagnosis of BID, type I currently remitted; and 25 age- and gender-matched healthy controls (HC). Participants performed an event-related functional magnetic resonance imaging paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (I)CM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Prefrontal cortical and striatal activity to happy and fear faces in bipolar disorder is associated with comorbid substance abuse and eating disorder

Background: The spectrum approach was used to examine contributions of comorbid symptom dimensions of substance abuse and eating disorder to abnormal prefrontal-cortical and subcortical-striatal activity to happy and fear faces previously demonstrated in bipolar disorder (BD). Method: Fourteen remitted BD-type I and sixteen healthy individuals viewed neutral, mild and intense happy and fear faces in two event-related fMRI experiments. All individuals completed Substance-Use and Eating-Disorder Spectrum measures. Region-of-Interest analyses for bilateral prefrontal and subcortical-striatal regions were performed. Results: BD individuals scored significantly higher on these spectrum measures than healthy individuals (p<0.05), and were distinguished by activity in prefrontal and subcortical-striatal regions. BD relative to healthy individuals showed reduced dorsal prefrontal-cortical activity to all faces. Only BD individuals showed greater subcortical-striatal activity to happy and neutral faces. In BD individuals, negative correlations were shown between substance use severity and right PFC activity to intense happy faces (p<0.04), and between substance use severity and right caudate nucleus activity to neutral faces (p<0.03). Positive correlations were shown between eating disorder and right ventral putamen activity to intense happy (p<0.02) and neutral faces (p<0.03). Exploratory analyses revealed few significant relationships between illness variables and medication upon neural activity in BID individuals. Limitations: Small sample size of predominantly medicated BD individuals. Conclusion: This study is the first to report relationships between comorbid symptom dimensions of substance abuse and eating disorder and prefrontal-cortical and subcortical-striatal activity to facial expressions in BD. Our findings suggest that these comorbid features may contribute to observed patterns of functional abnormalities in neural systems underlying mood regulation in BD. (C) 2009 Elsevier B.V. All rights reserved.

Abnormal Amygdala-Prefrontal Effective Connectivity to Happy Faces Differentiates Bipolar from Major Depression

Background: Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. Methods: Thirty-one depressed individuals-15 bipolar depressed (BD) and 16 major depressed (MDD), DSM-IV diagnostic criteria, ages 18-55 years, matched for age, age of illness onset, illness duration, and depression severity-and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). Results: During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. Conclusions: Abnormal, left-sided, top-down OMPFC-amygdala and right-sided, bottom-up, amygdala-OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.