Neural Activity, Neural Connectivity, and the Processing of Emotionally-Valenced Information in Older Adults: Links with Life Satisfaction

PubMed Central

Waldinger, Robert J.; Kensinger, Elizabeth A.; Schulz, Marc S.

2013-01-01

This study examines whether differences in late-life well-being are linked to how older adults encode emotionally-valenced information. Using fMRI with 39 older adults varying in life satisfaction, we examined how viewing positive and negative images affected activation and connectivity of an emotion-processing network. Participants engaged most regions within this network more robustly for positive than for negative images, but within the PFC this effect was moderated by life satisfaction, with individuals higher in satisfaction showing lower levels of activity during the processing of positive images. Participants high in satisfaction showed stronger correlations among network regions – particularly between the amygdala and other emotion processing regions – when viewing positive as compared to negative images. Participants low in satisfaction showed no valence effect. Findings suggest that late-life satisfaction is linked with how emotion-processing regions are engaged and connected during processing of valenced information. This first demonstration of a link between neural recruitment and late-life well-being suggests that differences in neural network activation and connectivity may account for the preferential encoding of positive information seen in some older adults. PMID:21590504

Basic disturbances of information processing in psychosis prediction.

PubMed

Bodatsch, Mitja; Klosterkötter, Joachim; Müller, Ralf; Ruhrmann, Stephan

2013-01-01

The basic symptoms (BS) approach provides a valid instrument in predicting psychosis onset and represents moreover a significant heuristic framework for research. The term "basic symptoms" denotes subtle changes of cognition and perception in the earliest and prodromal stages of psychosis development. BS are thought to correspond to disturbances of neural information processing. Following the heuristic implications of the BS approach, the present paper aims at exploring disturbances of information processing, revealed by functional magnetic resonance imaging (fMRI) and electro-encephalographic as characteristics of the at-risk state of psychosis. Furthermore, since high-risk studies employing ultra-high-risk criteria revealed non-conversion rates commonly exceeding 50%, thus warranting approaches that increase specificity, the potential contribution of neural information processing disturbances to psychosis prediction is reviewed. In summary, the at-risk state seems to be associated with information processing disturbances. Moreover, fMRI investigations suggested that disturbances of language processing domains might be a characteristic of the prodromal state. Neurophysiological studies revealed that disturbances of sensory processing may assist psychosis prediction in allowing for a quantification of risk in terms of magnitude and time. The latter finding represents a significant advancement since an estimation of the time to event has not yet been achieved by clinical approaches. Some evidence suggests a close relationship between self-experienced BS and neural information processing. With regard to future research, the relationship between neural information processing disturbances and different clinical risk concepts warrants further investigations. Thereby, a possible time sequence in the prodromal phase might be of particular interest.

Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations.

PubMed

Marshall, Tom R; den Boer, Sebastiaan; Cools, Roshan; Jensen, Ole; Fallon, Sean James; Zumer, Johanna M

2018-01-01

Selective attention is reflected neurally in changes in the power of posterior neural oscillations in the alpha (8-12 Hz) and gamma (40-100 Hz) bands. Although a neural mechanism that allows relevant information to be selectively processed has its advantages, it may lead to lucrative or dangerous information going unnoticed. Neural systems are also in place for processing rewarding and punishing information. Here, we examine the interaction between selective attention (left vs. right) and stimulus's learned value associations (neutral, punished, or rewarded) and how they compete for control of posterior neural oscillations. We found that both attention and stimulus-value associations influenced neural oscillations. Whereas selective attention had comparable effects on alpha and gamma oscillations, value associations had dissociable effects on these neural markers of attention. Salient targets (associated with positive and negative outcomes) hijacked changes in alpha power-increasing hemispheric alpha lateralization when salient targets were attended, decreasing it when they were being ignored. In contrast, hemispheric gamma-band lateralization was specifically abolished by negative distractors. Source analysis indicated occipital generators of both attentional and value effects. Thus, posterior cortical oscillations support both the ability to selectively attend while at the same time retaining the ability to remain sensitive to valuable features in the environment. Moreover, the versatility of our attentional system to respond separately to salient from merely positively valued stimuli appears to be carried out by separate neural processes reflected in different frequency bands.

Image object recognition based on the Zernike moment and neural networks

NASA Astrophysics Data System (ADS)

Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

1998-03-01

This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

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

PubMed Central

Berridge, Michael J.

2013-01-01

Neurons have highly developed Ca2+ signaling systems responsible for regulating a large number of neural functions such as the control of brain rhythms, information processing and the changes in synaptic plasticity that underpin learning and memory. The tonic excitatory drive, which is activated by the ascending arousal system, is particularly important for processes such as sensory perception, cognition and consciousness. The Ca2+ signaling pathway is a key component of this arousal system that regulates the neuronal excitability responsible for controlling the neural brain rhythms required for information processing and cognition. Dysregulation of the Ca2+ signaling pathway responsible for many of these neuronal processes has been implicated in the development of some of the major neural diseases in man such as Alzheimer disease, bipolar disorder and schizophrenia. Various treatments, which are known to act by reducing the activity of Ca2+ signaling, have proved successful in alleviating the symptoms of some of these neural diseases. PMID:22895098

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

PubMed Central

McDonough, Ian M.; Nashiro, Kaoru

2014-01-01

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity. PMID:24959130

Visual attention mitigates information loss in small- and large-scale neural codes

PubMed Central

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

PubMed

Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

2017-11-15

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Partial information decomposition as a unified approach to the specification of neural goal functions.

PubMed

Wibral, Michael; Priesemann, Viola; Kay, Jim W; Lizier, Joseph T; Phillips, William A

2017-03-01

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Short-term synaptic plasticity and heterogeneity in neural systems

NASA Astrophysics Data System (ADS)

Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

2013-01-01

We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

Association with emotional information alters subsequent processing of neutral faces

PubMed Central

Riggs, Lily; Fujioka, Takako; Chan, Jessica; McQuiggan, Douglas A.; Anderson, Adam K.; Ryan, Jennifer D.

2014-01-01

The processing of emotional as compared to neutral information is associated with different patterns in eye movement and neural activity. However, the ‘emotionality’ of a stimulus can be conveyed not only by its physical properties, but also by the information that is presented with it. There is very limited work examining the how emotional information may influence the immediate perceptual processing of otherwise neutral information. We examined how presenting an emotion label for a neutral face may influence subsequent processing by using eye movement monitoring (EMM) and magnetoencephalography (MEG) simultaneously. Participants viewed a series of faces with neutral expressions. Each face was followed by a unique negative or neutral sentence to describe that person, and then the same face was presented in isolation again. Viewing of faces paired with a negative sentence was associated with increased early viewing of the eye region and increased neural activity between 600 and 1200 ms in emotion processing regions such as the cingulate, medial prefrontal cortex, and amygdala, as well as posterior regions such as the precuneus and occipital cortex. Viewing of faces paired with a neutral sentence was associated with increased activity in the parahippocampal gyrus during the same time window. By monitoring behavior and neural activity within the same paradigm, these findings demonstrate that emotional information alters subsequent visual scanning and the neural systems that are presumably invoked to maintain a representation of the neutral information along with its emotional details. PMID:25566024

Impaired theta phase-resetting underlying auditory N1 suppression in chronic alcoholism.

PubMed

Fuentemilla, Lluis; Marco-Pallarés, Josep; Gual, Antoni; Escera, Carles; Polo, Maria Dolores; Grau, Carles

2009-02-18

It has been suggested that chronic alcoholism may lead to altered neural mechanisms related to inhibitory processes. Here, we studied auditory N1 suppression phenomena (i.e. amplitude reduction with repetitive stimuli) in chronic alcoholic patients as an early-stage information-processing brain function involving inhibition by the analysis of the N1 event-related potential and time-frequency computation (spectral power and phase-resetting). Our results showed enhanced neural theta oscillatory phase-resetting underlying N1 generation in suppressed N1 event-related potential. The present findings suggest that chronic alcoholism alters neural oscillatory synchrony dynamics at very early stages of information processing.

LANGUAGE EXPERIENCE SHAPES PROCESSING OF PITCH RELEVANT INFORMATION IN THE HUMAN BRAINSTEM AND AUDITORY CORTEX: ELECTROPHYSIOLOGICAL EVIDENCE.

PubMed

Krishnan, Ananthanarayan; Gandour, Jackson T

2014-12-01

Pitch is a robust perceptual attribute that plays an important role in speech, language, and music. As such, it provides an analytic window to evaluate how neural activity relevant to pitch undergo transformation from early sensory to later cognitive stages of processing in a well coordinated hierarchical network that is subject to experience-dependent plasticity. We review recent evidence of language experience-dependent effects in pitch processing based on comparisons of native vs. nonnative speakers of a tonal language from electrophysiological recordings in the auditory brainstem and auditory cortex. We present evidence that shows enhanced representation of linguistically-relevant pitch dimensions or features at both the brainstem and cortical levels with a stimulus-dependent preferential activation of the right hemisphere in native speakers of a tone language. We argue that neural representation of pitch-relevant information in the brainstem and early sensory level processing in the auditory cortex is shaped by the perceptual salience of domain-specific features. While both stages of processing are shaped by language experience, neural representations are transformed and fundamentally different at each biological level of abstraction. The representation of pitch relevant information in the brainstem is more fine-grained spectrotemporally as it reflects sustained neural phase-locking to pitch relevant periodicities contained in the stimulus. In contrast, the cortical pitch relevant neural activity reflects primarily a series of transient temporal neural events synchronized to certain temporal attributes of the pitch contour. We argue that experience-dependent enhancement of pitch representation for Chinese listeners most likely reflects an interaction between higher-level cognitive processes and early sensory-level processing to improve representations of behaviorally-relevant features that contribute optimally to perception. It is our view that long-term experience shapes this adaptive process wherein the top-down connections provide selective gating of inputs to both cortical and subcortical structures to enhance neural responses to specific behaviorally-relevant attributes of the stimulus. A theoretical framework for a neural network is proposed involving coordination between local, feedforward, and feedback components that can account for experience-dependent enhancement of pitch representations at multiple levels of the auditory pathway. The ability to record brainstem and cortical pitch relevant responses concurrently may provide a new window to evaluate the online interplay between feedback, feedforward, and local intrinsic components in the hierarchical processing of pitch relevant information.

LANGUAGE EXPERIENCE SHAPES PROCESSING OF PITCH RELEVANT INFORMATION IN THE HUMAN BRAINSTEM AND AUDITORY CORTEX: ELECTROPHYSIOLOGICAL EVIDENCE

PubMed Central

Krishnan, Ananthanarayan; Gandour, Jackson T.

2015-01-01

Pitch is a robust perceptual attribute that plays an important role in speech, language, and music. As such, it provides an analytic window to evaluate how neural activity relevant to pitch undergo transformation from early sensory to later cognitive stages of processing in a well coordinated hierarchical network that is subject to experience-dependent plasticity. We review recent evidence of language experience-dependent effects in pitch processing based on comparisons of native vs. nonnative speakers of a tonal language from electrophysiological recordings in the auditory brainstem and auditory cortex. We present evidence that shows enhanced representation of linguistically-relevant pitch dimensions or features at both the brainstem and cortical levels with a stimulus-dependent preferential activation of the right hemisphere in native speakers of a tone language. We argue that neural representation of pitch-relevant information in the brainstem and early sensory level processing in the auditory cortex is shaped by the perceptual salience of domain-specific features. While both stages of processing are shaped by language experience, neural representations are transformed and fundamentally different at each biological level of abstraction. The representation of pitch relevant information in the brainstem is more fine-grained spectrotemporally as it reflects sustained neural phase-locking to pitch relevant periodicities contained in the stimulus. In contrast, the cortical pitch relevant neural activity reflects primarily a series of transient temporal neural events synchronized to certain temporal attributes of the pitch contour. We argue that experience-dependent enhancement of pitch representation for Chinese listeners most likely reflects an interaction between higher-level cognitive processes and early sensory-level processing to improve representations of behaviorally-relevant features that contribute optimally to perception. It is our view that long-term experience shapes this adaptive process wherein the top-down connections provide selective gating of inputs to both cortical and subcortical structures to enhance neural responses to specific behaviorally-relevant attributes of the stimulus. A theoretical framework for a neural network is proposed involving coordination between local, feedforward, and feedback components that can account for experience-dependent enhancement of pitch representations at multiple levels of the auditory pathway. The ability to record brainstem and cortical pitch relevant responses concurrently may provide a new window to evaluate the online interplay between feedback, feedforward, and local intrinsic components in the hierarchical processing of pitch relevant information. PMID:25838636

Updating Procedures Can Reorganize the Neural Circuit Supporting a Fear Memory.

PubMed

Kwapis, Janine L; Jarome, Timothy J; Ferrara, Nicole C; Helmstetter, Fred J

2017-07-01

Established memories undergo a period of vulnerability following retrieval, a process termed 'reconsolidation.' Recent work has shown that the hypothetical process of reconsolidation is only triggered when new information is presented during retrieval, suggesting that this process may allow existing memories to be modified. Reconsolidation has received increasing attention as a possible therapeutic target for treating disorders that stem from traumatic memories, yet little is known about how this process changes the original memory. In particular, it is unknown whether reconsolidation can reorganize the neural circuit supporting an existing memory after that memory is modified with new information. Here, we show that trace fear memory undergoes a protein synthesis-dependent reconsolidation process following exposure to a single updating trial of delay conditioning. Further, this reconsolidation-dependent updating process appears to reorganize the neural circuit supporting the trace-trained memory, so that it better reflects the circuit supporting delay fear. Specifically, after a trace-to-delay update session, the amygdala is now required for extinction of the updated memory but the retrosplenial cortex is no longer required for retrieval. These results suggest that updating procedures could be used to force a complex, poorly defined memory circuit to rely on a better-defined neural circuit that may be more amenable to behavioral or pharmacological manipulation. This is the first evidence that exposure to new information can fundamentally reorganize the neural circuit supporting an existing memory.

Imaging first impressions: distinct neural processing of verbal and nonverbal social information.

PubMed

Kuzmanovic, Bojana; Bente, Gary; von Cramon, D Yves; Schilbach, Leonhard; Tittgemeyer, Marc; Vogeley, Kai

2012-03-01

First impressions profoundly influence our attitudes and behavior toward others. However, little is known about whether and to what degree the cognitive processes that underlie impression formation depend on the domain of the available information about the target person. To investigate the neural bases of the influence of verbal as compared to nonverbal information on interpersonal judgments, we identified brain regions where the BOLD signal parametrically increased with increasing strength of evaluation based on either short text vignettes or mimic and gestural behavior. While for verbal stimuli the increasing strength of subjective evaluation was correlated with increased neural activation of precuneus and posterior cingulate cortex (PC/PCC), a similar effect was observed for nonverbal stimuli in the amygdala. These findings support the assumption that qualitatively different cognitive operations underlie person evaluation depending upon the stimulus domain: while the processing of nonverbal person information may be more strongly associated with affective processing as indexed by recruitment of the amygdala, verbal person information engaged the PC/PCC that has been related to social inferential processing. Copyright © 2011 Elsevier Inc. All rights reserved.

Abnormal neural hierarchy in processing of verbal information in patients with schizophrenia.

PubMed

Lerner, Yulia; Bleich-Cohen, Maya; Solnik-Knirsh, Shimrit; Yogev-Seligmann, Galit; Eisenstein, Tamir; Madah, Waheed; Shamir, Alon; Hendler, Talma; Kremer, Ilana

2018-01-01

Previous research indicates abnormal comprehension of verbal information in patients with schizophrenia. Yet the neural mechanism underlying the breakdown of verbal information processing in schizophrenia is poorly understood. Imaging studies in healthy populations have shown a network of brain areas involved in hierarchical processing of verbal information over time. Here, we identified critical aspects of this hierarchy, examining patients with schizophrenia. Using functional magnetic resonance imaging, we examined various levels of information comprehension elicited by naturally presented verbal stimuli; from a set of randomly shuffled words to an intact story. Specifically, patients with first episode schizophrenia ( N  = 15), their non-manifesting siblings ( N  = 14) and healthy controls ( N  = 15) listened to a narrated story and randomly scrambled versions of it. To quantify the degree of dissimilarity between the groups, we adopted an inter-subject correlation (inter-SC) approach, which estimates differences in synchronization of neural responses within and between groups. The temporal topography found in healthy and siblings groups were consistent with our previous findings - high synchronization in responses from early sensory toward high order perceptual and cognitive areas. In patients with schizophrenia, stimuli with short and intermediate temporal scales evoked a typical pattern of reliable responses, whereas story condition (long temporal scale) revealed robust and widespread disruption of the inter-SCs. In addition, the more similar the neural activity of patients with schizophrenia was to the average response in the healthy group, the less severe the positive symptoms of the patients. Our findings suggest that system-level neural indication of abnormal verbal information processing in schizophrenia reflects disease manifestations.

Closed-Loop Estimation of Retinal Network Sensitivity by Local Empirical Linearization

PubMed Central

2018-01-01

Abstract Understanding how sensory systems process information depends crucially on identifying which features of the stimulus drive the response of sensory neurons, and which ones leave their response invariant. This task is made difficult by the many nonlinearities that shape sensory processing. Here, we present a novel perturbative approach to understand information processing by sensory neurons, where we linearize their collective response locally in stimulus space. We added small perturbations to reference stimuli and tested if they triggered visible changes in the responses, adapting their amplitude according to the previous responses with closed-loop experiments. We developed a local linear model that accurately predicts the sensitivity of the neural responses to these perturbations. Applying this approach to the rat retina, we estimated the optimal performance of a neural decoder and showed that the nonlinear sensitivity of the retina is consistent with an efficient encoding of stimulus information. Our approach can be used to characterize experimentally the sensitivity of neural systems to external stimuli locally, quantify experimentally the capacity of neural networks to encode sensory information, and relate their activity to behavior. PMID:29379871

Visual attention mitigates information loss in small- and large-scale neural codes.

PubMed

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-04-01

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Motivation alters impression formation and related neural systems

PubMed Central

Zaki, Jamil; Ambady, Nalini

2017-01-01

Abstract Observers frequently form impressions of other people based on complex or conflicting information. Rather than being objective, these impressions are often biased by observers’ motives. For instance, observers often downplay negative information they learn about ingroup members. Here, we characterize the neural systems associated with biased impression formation. Participants learned positive and negative information about ingroup and outgroup social targets. Following this information, participants worsened their impressions of outgroup, but not ingroup, targets. This tendency was associated with a failure to engage neural structures including lateral prefrontal cortex, dorsal anterior cingulate cortex, temporoparietal junction, Insula and Precuneus when processing negative information about ingroup (but not outgroup) targets. To the extent that participants engaged these regions while learning negative information about ingroup members, they exhibited less ingroup bias in their impressions. These data are consistent with a model of ‘effortless bias’, under which perceivers fail to process goal-inconsistent information in order to maintain desired conclusions. PMID:27798250

System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

PubMed

Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

2017-04-01

This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains.

PubMed

Wang, Zhuo; Sornborger, Andrew T; Tao, Louis

2016-06-01

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets, i.e., in stereotypical, correlated spiking patterns of neural activity. Due to their relevance to coherent spiking, synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited the classical synfire chain's ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by streaming input, processes the input, then makes a decision based on the processed information and shuts itself down.

The neural correlates of gist-based true and false recognition

PubMed Central

Gutchess, Angela H.; Schacter, Daniel L.

2012-01-01

When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Evaluation of somatosensory cortical differences between flutter and vibration tactile stimuli.

PubMed

Han, Sang Woo; Chung, Yoon Gi; Kim, Hyung-Sik; Chung, Soon-Cheol; Park, Jang-Yeon; Kim, Sung-Phil

2013-01-01

In parallel with advances in haptic-based mobile computing systems, understanding of the neural processing of vibrotactile information becomes of great importance. In the human nervous system, two types of vibrotactile information, flutter and vibration, are delivered from mechanoreceptors to the somatosensory cortex through segregated neural afferents. To investigate how the somatosensory cortex differentiates flutter and vibration, we analyzed the cortical responses to vibrotactile stimuli with a wide range of frequencies. Specifically, we examined whether cortical activity changed most around 50 Hz, which is known as a boundary between flutter and vibration. We explored various measures to evaluate separability of cortical activity across frequency and found that the hypothesis margin method resulted in the greatest separability between flutter and vibration. This result suggests that flutter and vibration information may be processed by different neural processes in the somatosensory cortex.

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Data systems and computer science: Neural networks base R/T program overview

NASA Technical Reports Server (NTRS)

Gulati, Sandeep

1991-01-01

The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

Neural Markers of Responsiveness to the Environment in Human Sleep.

PubMed

Andrillon, Thomas; Poulsen, Andreas Trier; Hansen, Lars Kai; Léger, Damien; Kouider, Sid

2016-06-15

Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed human volunteers to classify words with lateralized hand responses while falling asleep. Using an electroencephalographic (EEG) marker of motor preparation, we show how responsiveness is modulated across sleep. These modulations are tracked using classic event-related potential analyses complemented by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which could be related to modulation in sleep depth. In REM sleep, however, this relationship was reversed. We therefore propose that, in REM sleep, endogenously generated processes compete with the processing of external input. Sleep can thus be seen as a self-regulated process in which external information can be processed in lighter stages but suppressed in deeper stages. Last, our results suggest drastically different gating mechanisms in NREM and REM sleep. Previous research has tempered the notion that sleepers are isolated from their environment. Here, we pushed this idea forward and examined, across all sleep stages, the brain's ability to flexibly process sensory information, up to the decision level. We extracted an EEG marker of motor preparation to determine the completion of the sensory processing chain and explored how it is constrained by baseline and evoked neural activity. In NREM sleep, slow waves elicited by stimuli appeared to block response preparation. We also used a novel analytic approach (Lempel-Ziv complexity) and showed that the ability to process external information correlates with neural complexity. A reversal of the correlation between complexity and motor indices in REM sleep suggests drastically different gating mechanisms across sleep stages. Copyright © 2016 the authors 0270-6474/16/366583-14$15.00/0.

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

PubMed Central

Crabtree, Gregg W.; Gogos, Joseph A.

2014-01-01

Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

PubMed

Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Evidence of Rapid Modulation by Social Information of Subjective, Physiological, and Neural Responses to Emotional Expressions

PubMed Central

Mermillod, Martial; Grynberg, Delphine; Pio-Lopez, Léo; Rychlowska, Magdalena; Beffara, Brice; Harquel, Sylvain; Vermeulen, Nicolas; Niedenthal, Paula M.; Dutheil, Frédéric; Droit-Volet, Sylvie

2018-01-01

Recent research suggests that conceptual or emotional factors could influence the perceptual processing of stimuli. In this article, we aimed to evaluate the effect of social information (positive, negative, or no information related to the character of the target) on subjective (perceived and felt valence and arousal), physiological (facial mimicry) as well as on neural (P100 and N170) responses to dynamic emotional facial expressions (EFE) that varied from neutral to one of the six basic emotions. Across three studies, the results showed reduced ratings of valence and arousal of EFE associated with incongruent social information (Study 1), increased electromyographical responses (Study 2), and significant modulation of P100 and N170 components (Study 3) when EFE were associated with social (positive and negative) information (vs. no information). These studies revealed that positive or negative social information reduces subjective responses to incongruent EFE and produces a similar neural and physiological boost of the early perceptual processing of EFE irrespective of their congruency. In conclusion, the article suggests that the presence of positive or negative social context modulates early physiological and neural activity preceding subsequent behavior. PMID:29375330

Neuromorphic photonic networks using silicon photonic weight banks.

PubMed

Tait, Alexander N; de Lima, Thomas Ferreira; Zhou, Ellen; Wu, Allie X; Nahmias, Mitchell A; Shastri, Bhavin J; Prucnal, Paul R

2017-08-07

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using "neural compiler" to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

PubMed

Norman, Kenneth A; Polyn, Sean M; Detre, Greg J; Haxby, James V

2006-09-01

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optical neural networks based on holographic correlators

NASA Astrophysics Data System (ADS)

Sokolov, V. K.; Shubnikov, E. I.

1995-10-01

The three most important models of neural networks — a bidirectional associative memory, Hopfield networks, and adaptive resonance networks — are used as examples to show that a holographic correlator has its place in the neural computing paradigm.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Artificial Neural Networks for Processing Graphs with Application to Image Understanding: A Survey

NASA Astrophysics Data System (ADS)

Bianchini, Monica; Scarselli, Franco

In graphical pattern recognition, each data is represented as an arrangement of elements, that encodes both the properties of each element and the relations among them. Hence, patterns are modelled as labelled graphs where, in general, labels can be attached to both nodes and edges. Artificial neural networks able to process graphs are a powerful tool for addressing a great variety of real-world problems, where the information is naturally organized in entities and relationships among entities and, in fact, they have been widely used in computer vision, f.i. in logo recognition, in similarity retrieval, and for object detection. In this chapter, we propose a survey of neural network models able to process structured information, with a particular focus on those architectures tailored to address image understanding applications. Starting from the original recursive model (RNNs), we subsequently present different ways to represent images - by trees, forests of trees, multiresolution trees, directed acyclic graphs with labelled edges, general graphs - and, correspondingly, neural network architectures appropriate to process such structures.

Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

NASA Astrophysics Data System (ADS)

Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

2018-05-01

The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images

NASA Astrophysics Data System (ADS)

Park, Gilsoon; Hong, Jinwoo; Lee, Jong-Min

2018-03-01

In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.

Reorganization of the Connectivity between Elementary Functions – A Model Relating Conscious States to Neural Connections

PubMed Central

Mogensen, Jesper; Overgaard, Morten

2017-01-01

In the present paper it is argued that the “neural correlate of consciousness” (NCC) does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions) model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions) and REFCON (of elementary functions and consciousness) models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions. PMID:28473797

Motivation alters impression formation and related neural systems.

PubMed

Hughes, Brent L; Zaki, Jamil; Ambady, Nalini

2017-01-01

Observers frequently form impressions of other people based on complex or conflicting information. Rather than being objective, these impressions are often biased by observers' motives. For instance, observers often downplay negative information they learn about ingroup members. Here, we characterize the neural systems associated with biased impression formation. Participants learned positive and negative information about ingroup and outgroup social targets. Following this information, participants worsened their impressions of outgroup, but not ingroup, targets. This tendency was associated with a failure to engage neural structures including lateral prefrontal cortex, dorsal anterior cingulate cortex, temporoparietal junction, Insula and Precuneus when processing negative information about ingroup (but not outgroup) targets. To the extent that participants engaged these regions while learning negative information about ingroup members, they exhibited less ingroup bias in their impressions. These data are consistent with a model of 'effortless bias', under which perceivers fail to process goal-inconsistent information in order to maintain desired conclusions. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Deficits in context-dependent adaptive coding of reward in schizophrenia

PubMed Central

Kirschner, Matthias; Hager, Oliver M; Bischof, Martin; Hartmann-Riemer, Matthias N; Kluge, Agne; Seifritz, Erich; Tobler, Philippe N; Kaiser, Stefan

2016-01-01

Theoretical principles of information processing and empirical findings suggest that to efficiently represent all possible rewards in the natural environment, reward-sensitive neurons have to adapt their coding range dynamically to the current reward context. Adaptation ensures that the reward system is most sensitive for the most likely rewards, enabling the system to efficiently represent a potentially infinite range of reward information. A deficit in neural adaptation would prevent precise representation of rewards and could have detrimental effects for an organism’s ability to optimally engage with its environment. In schizophrenia, reward processing is known to be impaired and has been linked to different symptom dimensions. However, despite the fundamental significance of coding reward adaptively, no study has elucidated whether adaptive reward processing is impaired in schizophrenia. We therefore studied patients with schizophrenia (n=27) and healthy controls (n=25), using functional magnetic resonance imaging in combination with a variant of the monetary incentive delay task. Compared with healthy controls, patients with schizophrenia showed less efficient neural adaptation to the current reward context, which leads to imprecise neural representation of reward. Importantly, the deficit correlated with total symptom severity. Our results suggest that some of the deficits in reward processing in schizophrenia might be due to inefficient neural adaptation to the current reward context. Furthermore, because adaptive coding is a ubiquitous feature of the brain, we believe that our findings provide an avenue in defining a general impairment in neural information processing underlying this debilitating disorder. PMID:27430009

Neural suppression of irrelevant information underlies optimal working memory performance.

PubMed

Zanto, Theodore P; Gazzaley, Adam

2009-03-11

Our ability to focus attention on task-relevant information and ignore distractions is reflected by differential enhancement and suppression of neural activity in sensory cortex (i.e., top-down modulation). Such selective, goal-directed modulation of activity may be intimately related to memory, such that the focus of attention biases the likelihood of successfully maintaining relevant information by limiting interference from irrelevant stimuli. Despite recent studies elucidating the mechanistic overlap between attention and memory, the relationship between top-down modulation of visual processing during working memory (WM) encoding, and subsequent recognition performance has not yet been established. Here, we provide neurophysiological evidence in healthy, young adults that top-down modulation of early visual processing (< 200 ms from stimulus onset) is intimately related to subsequent WM performance, such that the likelihood of successfully remembering relevant information is associated with limiting interference from irrelevant stimuli. The consequences of a failure to ignore distractors on recognition performance was replicated for two types of feature-based memory, motion direction and color. Moreover, attention to irrelevant stimuli was reflected neurally during the WM maintenance period as an increased memory load. These results suggest that neural enhancement of relevant information is not the primary determinant of high-level performance, but rather optimal WM performance is dependent on effectively filtering irrelevant information through neural suppression to prevent overloading a limited memory capacity.

Functionally integrated neural processing of linguistic and talker information: An event-related fMRI and ERP study.

PubMed

Zhang, Caicai; Pugh, Kenneth R; Mencl, W Einar; Molfese, Peter J; Frost, Stephen J; Magnuson, James S; Peng, Gang; Wang, William S-Y

2016-01-01

Speech signals contain information of both linguistic content and a talker's voice. Conventionally, linguistic and talker processing are thought to be mediated by distinct neural systems in the left and right hemispheres respectively, but there is growing evidence that linguistic and talker processing interact in many ways. Previous studies suggest that talker-related vocal tract changes are processed integrally with phonetic changes in the bilateral posterior superior temporal gyrus/superior temporal sulcus (STG/STS), because the vocal tract parameter influences the perception of phonetic information. It is yet unclear whether the bilateral STG is also activated by the integral processing of another parameter - pitch, which influences the perception of lexical tone information and is related to talker differences in tone languages. In this study, we conducted separate functional magnetic resonance imaging (fMRI) and event-related potential (ERP) experiments to examine the spatial and temporal loci of interactions of lexical tone and talker-related pitch processing in Cantonese. We found that the STG was activated bilaterally during the processing of talker changes when listeners attended to lexical tone changes in the stimuli and during the processing of lexical tone changes when listeners attended to talker changes, suggesting that lexical tone and talker processing are functionally integrated in the bilateral STG. It extends the previous study, providing evidence for a general neural mechanism of integral phonetic and talker processing in the bilateral STG. The ERP results show interactions of lexical tone and talker processing 500-800ms after auditory word onset (a simultaneous posterior P3b and a frontal negativity). Moreover, there is some asymmetry in the interaction, such that unattended talker changes affect linguistic processing more than vice versa, which may be related to the ambiguity that talker changes cause in speech perception and/or attention bias to talker changes. Our findings have implications for understanding the neural encoding of linguistic and talker information. Copyright © 2015 Elsevier Inc. All rights reserved.

Adaptive Neurons For Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1990-01-01

Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

Dynamic interactions in neural networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arbib, M.A.; Amari, S.

The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

Spintronic characteristics of self-assembled neurotransmitter acetylcholine molecular complexes enable quantum information processing in neural networks and brain

NASA Astrophysics Data System (ADS)

Tamulis, Arvydas; Majauskaite, Kristina; Kairys, Visvaldas; Zborowski, Krzysztof; Adhikari, Kapil; Krisciukaitis, Sarunas

2016-09-01

Implementation of liquid state quantum information processing based on spatially localized electronic spin in the neurotransmitter stable acetylcholine (ACh) neutral molecular radical is discussed. Using DFT quantum calculations we proved that this molecule possesses stable localized electron spin, which may represent a qubit in quantum information processing. The necessary operating conditions for ACh molecule are formulated in self-assembled dimer and more complex systems. The main quantum mechanical research result of this paper is that the neurotransmitter ACh systems, which were proposed, include the use of quantum molecular spintronics arrays to control the neurotransmission in neural networks.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

PubMed Central

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A.; Navas, Adrian; Villacorta-Atienza, Jose A.

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons. PMID:26648863

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

PubMed

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Relationship between brainstem, cortical and behavioral measures relevant to pitch salience in humans.

PubMed

Krishnan, Ananthanarayan; Bidelman, Gavin M; Smalt, Christopher J; Ananthakrishnan, Saradha; Gandour, Jackson T

2012-10-01

Neural representation of pitch-relevant information at both the brainstem and cortical levels of processing is influenced by language or music experience. However, the functional roles of brainstem and cortical neural mechanisms in the hierarchical network for language processing, and how they drive and maintain experience-dependent reorganization are not known. In an effort to evaluate the possible interplay between these two levels of pitch processing, we introduce a novel electrophysiological approach to evaluate pitch-relevant neural activity at the brainstem and auditory cortex concurrently. Brainstem frequency-following responses and cortical pitch responses were recorded from participants in response to iterated rippled noise stimuli that varied in stimulus periodicity (pitch salience). A control condition using iterated rippled noise devoid of pitch was employed to ensure pitch specificity of the cortical pitch response. Neural data were compared with behavioral pitch discrimination thresholds. Results showed that magnitudes of neural responses increase systematically and that behavioral pitch discrimination improves with increasing stimulus periodicity, indicating more robust encoding for salient pitch. Absence of cortical pitch response in the control condition confirms that the cortical pitch response is specific to pitch. Behavioral pitch discrimination was better predicted by brainstem and cortical responses together as compared to each separately. The close correspondence between neural and behavioral data suggest that neural correlates of pitch salience that emerge in early, preattentive stages of processing in the brainstem may drive and maintain with high fidelity the early cortical representations of pitch. These neural representations together contain adequate information for the development of perceptual pitch salience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Emotional Effects in Visual Information Processing

DTIC Science & Technology

2009-10-24

1 Emotional Effects on Visual Information Processing FA4869-08-0004 AOARD 074018 Report October 24, 2009...TITLE AND SUBTITLE Emotional Effects in Visual Information Processing 5a. CONTRACT NUMBER FA48690810004 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...objective of this research project was to investigate how emotion influences visual information processing and the neural correlates of the effects

Multitask neurovision processor with extensive feedback and feedforward connections

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1991-11-01

A multi-task neuro-vision parameter which performs a variety of information processing operations associated with the early stages of biological vision is presented. The network architecture of this neuro-vision processor, called the positive-negative (PN) neural processor, is loosely based on the neural activity fields exhibited by thalamic and cortical nervous tissue layers. The computational operation performed by the processor arises from the strength of the recurrent feedback among the numerous positive and negative neural computing units. By adjusting the feedback connections it is possible to generate diverse dynamic behavior that may be used for short-term visual memory (STVM), spatio-temporal filtering (STF), and pulse frequency modulation (PFM). The information attributes that are to be processes may be regulated by modifying the feedforward connections from the signal space to the neural processor.

Dissociable neural modulation underlying lasting first impressions, changing your mind for the better, and changing it for the worse.

PubMed

Bhanji, Jamil P; Beer, Jennifer S

2013-05-29

Unattractive job candidates face a disadvantage when interviewing for a job. Employers' evaluations are colored by the candidate's physical attractiveness even when they take job interview performance into account. This example illustrates unexplored questions about the neural basis of social evaluation in humans. What neural regions support the lasting effects of initial impressions (even after getting to know someone)? How does the brain process information that changes our minds about someone? Job candidates' competence was evaluated from photographs and again after seeing snippets of job interviews. Left lateral orbitofrontal cortex modulation serves as a warning signal for initial reactions that ultimately undermine evaluations even when additional information is taken into account. The neural basis of changing one's mind about a candidate is not a simple matter of computing the amount of competence-affirming information in their job interview. Instead, seeing a candidate for the better is somewhat distinguishable at the neural level from seeing a candidate for the worse. Whereas amygdala modulation marks the extremity of evaluation change, favorable impression change additionally draws on parametric modulation of lateral prefrontal cortex and unfavorable impression change additionally draws on parametric modulation of medial prefrontal cortex, temporal cortex, and striatum. Investigating social evaluation as a dynamic process (rather than a one-time impression) paints a new picture of its neural basis and highlights the partially dissociable processes that contribute to changing your mind about someone for the better or the worse.

Neural processing of high and low spatial frequency information in faces changes across development: qualitative changes in face processing during adolescence.

PubMed

Peters, Judith C; Vlamings, Petra; Kemner, Chantal

2013-05-01

Face perception in adults depends on skilled processing of interattribute distances ('configural' processing), which is disrupted for faces presented in inverted orientation (face inversion effect or FIE). Children are not proficient in configural processing, and this might relate to an underlying immaturity to use facial information in low spatial frequency (SF) ranges, which capture the coarse information needed for configural processing. We hypothesized that during adolescence a shift from use of high to low SF information takes place. Therefore, we studied the influence of SF content on neural face processing in groups of children (9-10 years), adolescents (14-15 years) and young adults (21-29 years) by measuring event-related potentials (ERPs) to upright and inverted faces which varied in SF content. Results revealed that children show a neural FIE in early processing stages (i.e. P1; generated in early visual areas), suggesting a superficial, global facial analysis. In contrast, ERPs of adults revealed an FIE at later processing stages (i.e. N170; generated in face-selective, higher visual areas). Interestingly, adolescents showed FIEs in both processing stages, suggesting a hybrid developmental stage. Furthermore, adolescents and adults showed FIEs for stimuli containing low SF information, whereas such effects were driven by both low and high SF information in children. These results indicate that face processing has a protracted maturational course into adolescence, and is dependent on changes in SF processing. During adolescence, sensitivity to configural cues is developed, which aids the fast and holistic processing that is so special for faces. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

PubMed

Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

2016-04-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

PubMed Central

Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

Intelligent processing of acoustic emission signals

NASA Astrophysics Data System (ADS)

Sachse, Wolfgang; Grabec, Igor

1992-07-01

Recent developments in applying neural-like signal-processing procedures for analyzing acoustic emission signals are summarized. These procedures employ a set of learning signals to develop a memory that can subsequently be utilized to process other signals to recover information about an unknown source. A majority of the current applications to process ultrasonic waveforms are based on multilayered, feed-forward neural networks, trained with some type of back-error propagation rule.

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

1999-01-01

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

Neural plasticity and its initiating conditions in tinnitus.

PubMed

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Four Mechanistic Models of Peer Influence on Adolescent Cannabis Use.

PubMed

Caouette, Justin D; Feldstein Ewing, Sarah W

2017-06-01

Most adolescents begin exploring cannabis in peer contexts, but the neural mechanisms that underlie peer influence on adolescent cannabis use are still unknown. This theoretical overview elucidates the intersecting roles of neural function and peer factors in cannabis use in adolescents. Novel paradigms using functional magnetic resonance imaging (fMRI) in adolescents have identified distinct neural mechanisms of risk decision-making and incentive processing in peer contexts, centered on reward-motivation and affect regulatory neural networks; these findings inform a theoretical model of peer-driven cannabis use decisions in adolescents. We propose four "mechanistic profiles" of social facilitation of cannabis use in adolescents: (1) peer influence as the primary driver of use; (2) cannabis exploration as the primary driver, which may be enhanced in peer contexts; (3) social anxiety; and (4) negative peer experiences. Identification of "neural targets" involved in motivating cannabis use may inform clinicians about which treatment strategies work best in adolescents with cannabis use problems, and via which social and neurocognitive processes.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1997-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1998-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.

PubMed

Shaeri, Mohammad Ali; Sodagar, Amir M

2015-05-01

This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.

The Sustained Effect of Emotional Signals on Neural Processing in 12-Month-Olds

ERIC Educational Resources Information Center

Leventon, Jacqueline S.; Bauer, Patricia J.

2013-01-01

Around the end of the first year of life, infants develop a social referencing ability -- using emotional information from others to guide their own behavior. Much research on social referencing has focused on changes in behavior in response to emotional information. The present study was an investigation of the changes in neural responses that…

Neural Mechanisms of Encoding Social and Non-Social Context Information in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Greimel, Ellen; Nehrkorn, Barbara; Fink, Gereon R.; Kukolja, Juraj; Kohls, Gregor; Muller, Kristin; Piefke, Martina; Kamp-Becker, Inge; Remschmidt, Helmut; Herpertz-Dahlmann, Beate; Konrad, Kerstin; Schulte-Ruther, Martin

2012-01-01

Individuals with autism spectrum disorder (ASD) often fail to attach context to their memories and are specifically impaired in processing social aspects of contextual information. The aim of the present study was to investigate the modulatory influence of social vs. non-social context on neural mechanisms during encoding in ASD. Using…

Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks.

PubMed

Fuchs, Erich; Gruber, Christian; Reitmaier, Tobias; Sick, Bernhard

2009-09-01

Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.

Towards neural correlates of auditory stimulus processing: A simultaneous auditory evoked potentials and functional magnetic resonance study using an odd-ball paradigm

PubMed Central

Milner, Rafał; Rusiniak, Mateusz; Lewandowska, Monika; Wolak, Tomasz; Ganc, Małgorzata; Piątkowska-Janko, Ewa; Bogorodzki, Piotr; Skarżyński, Henryk

2014-01-01

Background The neural underpinnings of auditory information processing have often been investigated using the odd-ball paradigm, in which infrequent sounds (deviants) are presented within a regular train of frequent stimuli (standards). Traditionally, this paradigm has been applied using either high temporal resolution (EEG) or high spatial resolution (fMRI, PET). However, used separately, these techniques cannot provide information on both the location and time course of particular neural processes. The goal of this study was to investigate the neural correlates of auditory processes with a fine spatio-temporal resolution. A simultaneous auditory evoked potentials (AEP) and functional magnetic resonance imaging (fMRI) technique (AEP-fMRI), together with an odd-ball paradigm, were used. Material/Methods Six healthy volunteers, aged 20–35 years, participated in an odd-ball simultaneous AEP-fMRI experiment. AEP in response to acoustic stimuli were used to model bioelectric intracerebral generators, and electrophysiological results were integrated with fMRI data. Results fMRI activation evoked by standard stimuli was found to occur mainly in the primary auditory cortex. Activity in these regions overlapped with intracerebral bioelectric sources (dipoles) of the N1 component. Dipoles of the N1/P2 complex in response to standard stimuli were also found in the auditory pathway between the thalamus and the auditory cortex. Deviant stimuli induced fMRI activity in the anterior cingulate gyrus, insula, and parietal lobes. Conclusions The present study showed that neural processes evoked by standard stimuli occur predominantly in subcortical and cortical structures of the auditory pathway. Deviants activate areas non-specific for auditory information processing. PMID:24413019

Tracking neural coding of perceptual and semantic features of concrete nouns

PubMed Central

Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark; Wehbe, Leila; Fyshe, Alona; Salmelin, Riitta; Mitchell, Tom

2015-01-01

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes. PMID:22565201

Neural Signatures of Stimulus Features in Visual Working Memory—A Spatiotemporal Approach

PubMed Central

Jackson, Margaret C.; Klein, Christoph; Mohr, Harald; Shapiro, Kimron L.; Linden, David E. J.

2010-01-01

We examined the neural signatures of stimulus features in visual working memory (WM) by integrating functional magnetic resonance imaging (fMRI) and event-related potential data recorded during mental manipulation of colors, rotation angles, and color–angle conjunctions. The N200, negative slow wave, and P3b were modulated by the information content of WM, and an fMRI-constrained source model revealed a progression in neural activity from posterior visual areas to higher order areas in the ventral and dorsal processing streams. Color processing was associated with activity in inferior frontal gyrus during encoding and retrieval, whereas angle processing involved right parietal regions during the delay interval. WM for color–angle conjunctions did not involve any additional neural processes. The finding that different patterns of brain activity underlie WM for color and spatial information is consistent with ideas that the ventral/dorsal “what/where” segregation of perceptual processing influences WM organization. The absence of characteristic signatures of conjunction-related brain activity, which was generally intermediate between the 2 single conditions, suggests that conjunction judgments are based on the coordinated activity of these 2 streams. PMID:19429863

A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes.

PubMed

León-Domínguez, Umberto; Vela-Bueno, Antonio; Froufé-Torres, Manuel; León-Carrión, Jose

2013-06-01

The thalamo-cortical system has been defined as a neural network associated with consciousness. While there seems to be wide agreement that the thalamo-cortical system directly intervenes in vigilance and arousal, a divergence of opinion persists regarding its intervention in the control of other cognitive processes necessary for consciousness. In the present manuscript, we provide a review of recent scientific findings on the thalamo-cortical system and its role in the control and regulation of the flow of neural information necessary for conscious cognitive processes. We suggest that the axis formed by the medial prefrontal cortex and different thalamic nuclei (reticular nucleus, intralaminar nucleus, and midline nucleus), represents a core component for consciousness. This axis regulates different cerebral structures which allow basic cognitive processes like attention, arousal and memory to emerge. In order to produce a synchronized coherent response, neural communication between cerebral structures must have exact timing (chronometry). Thus, a chronometric functional sub-network within the thalamo-cortical system keeps us in an optimal and continuous functional state, allowing high-order cognitive processes, essential to awareness and qualia, to take place. Copyright © 2013 Elsevier Ltd. All rights reserved.

Consciousness, information integration, and the brain.

PubMed

Tononi, Giulio

2005-01-01

Clinical observations have established that certain parts of the brain are essential for consciousness whereas other parts are not. For example, different areas of the cerebral cortex contribute different modalities and submodalities of consciousness, whereas the cerebellum does not, despite having even more neurons. It is also well established that consciousness depends on the way the brain functions. For example, consciousness is much reduced during slow wave sleep and generalized seizures, even though the levels of neural activity are comparable or higher than in wakefulness. To understand why this is so, empirical observations on the neural correlates of consciousness need to be complemented by a principled theoretical approach. Otherwise, it is unlikely that we could ever establish to what extent consciousness is present in neurological conditions such as akinetic mutism, psychomotor seizures, or sleepwalking, and to what extent it is present in newborn babies and animals. A principled approach is provided by the information integration theory of consciousness. This theory claims that consciousness corresponds to a system's capacity to integrate information, and proposes a way to measure such capacity. The information integration theory can account for several neurobiological observations concerning consciousness, including: (i) the association of consciousness with certain neural systems rather than with others; (ii) the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; (iii) the reduction of consciousness during dreamless sleep and generalized seizures; and (iv) the time requirements on neural interactions that support consciousness.

Study on algorithm of process neural network for soft sensing in sewage disposal system

NASA Astrophysics Data System (ADS)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

Lesion Mapping the Four-Factor Structure of Emotional Intelligence

PubMed Central

Operskalski, Joachim T.; Paul, Erick J.; Colom, Roberto; Barbey, Aron K.; Grafman, Jordan

2015-01-01

Emotional intelligence (EI) refers to an individual’s ability to process and respond to emotions, including recognizing the expression of emotions in others, using emotions to enhance thought and decision making, and regulating emotions to drive effective behaviors. Despite their importance for goal-directed social behavior, little is known about the neural mechanisms underlying specific facets of EI. Here, we report findings from a study investigating the neural bases of these specific components for EI in a sample of 130 combat veterans with penetrating traumatic brain injury. We examined the neural mechanisms underlying experiential (perceiving and using emotional information) and strategic (understanding and managing emotions) facets of EI. Factor scores were submitted to voxel-based lesion symptom mapping to elucidate their neural substrates. The results indicate that two facets of EI (perceiving and managing emotions) engage common and distinctive neural systems, with shared dependence on the social knowledge network, and selective engagement of the orbitofrontal and parietal cortex for strategic aspects of emotional information processing. The observed pattern of findings suggests that sub-facets of experiential and strategic EI can be characterized as separable but related processes that depend upon a core network of brain structures within frontal, temporal and parietal cortex. PMID:26858627

Dissociable neural correlates of contour completion and contour representation in illusory contour perception.

PubMed

Wu, Xiang; He, Sheng; Bushara, Khalaf; Zeng, Feiyan; Liu, Ying; Zhang, Daren

2012-10-01

Object recognition occurs even when environmental information is incomplete. Illusory contours (ICs), in which a contour is perceived though the contour edges are incomplete, have been extensively studied as an example of such a visual completion phenomenon. Despite the neural activity in response to ICs in visual cortical areas from low (V1 and V2) to high (LOC: the lateral occipital cortex) levels, the details of the neural processing underlying IC perception are largely not clarified. For example, how do the visual areas function in IC perception and how do they interact to archive the coherent contour perception? IC perception involves the process of completing the local discrete contour edges (contour completion) and the process of representing the global completed contour information (contour representation). Here, functional magnetic resonance imaging was used to dissociate contour completion and contour representation by varying each in opposite directions. The results show that the neural activity was stronger to stimuli with more contour completion than to stimuli with more contour representation in V1 and V2, which was the reverse of that in the LOC. When inspecting the neural activity change across the visual pathway, the activation remained high for the stimuli with more contour completion and increased for the stimuli with more contour representation. These results suggest distinct neural correlates of contour completion and contour representation, and the possible collaboration between the two processes during IC perception, indicating a neural connection between the discrete retinal input and the coherent visual percept. Copyright © 2011 Wiley Periodicals, Inc.

Temporal pattern processing in songbirds.

PubMed

Comins, Jordan A; Gentner, Timothy Q

2014-10-01

Understanding how the brain perceives, organizes and uses patterned information is directly related to the neurobiology of language. Given the present limitations, such knowledge at the scale of neurons, neural circuits and neural populations can only come from non-human models, focusing on shared capacities that are relevant to language processing. Here we review recent advances in the behavioral and neural basis of temporal pattern processing of natural auditory communication signals in songbirds, focusing on European starlings. We suggest a general inhibitory circuit for contextual modulation that can act to control sensory representations based on patterning rules. Copyright © 2014. Published by Elsevier Ltd.

Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity.

PubMed

Bertone, Armando; Mottron, Laurent; Jelenic, Patricia; Faubert, Jocelyn

2005-10-01

Visuo-perceptual processing in autism is characterized by intact or enhanced performance on static spatial tasks and inferior performance on dynamic tasks, suggesting a deficit of dorsal visual stream processing in autism. However, previous findings by Bertone et al. indicate that neuro-integrative mechanisms used to detect complex motion, rather than motion perception per se, may be impaired in autism. We present here the first demonstration of concurrent enhanced and decreased performance in autism on the same visuo-spatial static task, wherein the only factor dichotomizing performance was the neural complexity required to discriminate grating orientation. The ability of persons with autism was found to be superior for identifying the orientation of simple, luminance-defined (or first-order) gratings but inferior for complex, texture-defined (or second-order) gratings. Using a flicker contrast sensitivity task, we demonstrated that this finding is probably not due to abnormal information processing at a sub-cortical level (magnocellular and parvocellular functioning). Together, these findings are interpreted as a clear indication of altered low-level perceptual information processing in autism, and confirm that the deficits and assets observed in autistic visual perception are contingent on the complexity of the neural network required to process a given type of visual stimulus. We suggest that atypical neural connectivity, resulting in enhanced lateral inhibition, may account for both enhanced and decreased low-level information processing in autism.

Temporal Context in Speech Processing and Attentional Stream Selection: A Behavioral and Neural Perspective

ERIC Educational Resources Information Center

Golumbic, Elana M. Zion; Poeppel, David; Schroeder, Charles E.

2012-01-01

The human capacity for processing speech is remarkable, especially given that information in speech unfolds over multiple time scales concurrently. Similarly notable is our ability to filter out of extraneous sounds and focus our attention on one conversation, epitomized by the "Cocktail Party" effect. Yet, the neural mechanisms underlying on-line…

Specific and Nonspecific Neural Activity during Selective Processing of Visual Representations in Working Memory

ERIC Educational Resources Information Center

Oh, Hwamee; Leung, Hoi-Chung

2010-01-01

In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…

Dynamical synapses enhance neural information processing: gracefulness, accuracy, and mobility.

PubMed

Fung, C C Alan; Wong, K Y Michael; Wang, He; Wu, Si

2012-05-01

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity: short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning and may serve as substrates for neural systems manipulating temporal information on relevant timescales. This study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors: the network that is initially being stimulated to an active state decays to a silent state very slowly on the timescale of STD rather than on that of neuralsignaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.

A novel neural-wavelet approach for process diagnostics and complex system modeling

NASA Astrophysics Data System (ADS)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

Beyond Reason: Emotion

NASA Astrophysics Data System (ADS)

Suárez Araujo, Carmen Paz; Barahona da Fonseca, Isabel; Barahona da Fonseca, José; Simões da Fonseca, J.

2004-08-01

A theoretical approach that aims to the identification of information processing that may be responsible for emotional dimensions of subjective experience is studied as an initial step in the construction of a neural net model of affective dimensions of psychological experiences. In this paper it is suggested that a way of orientated recombination of attributes can be present not only in the perceptive processing but also in cognitive ones. We will present an analysis of the most important emotion theories, we show their neural organization and we propose the neural computation approach as an appropriate framework for generating knowledge about the neural base of emotional experience. Finally, in this study we present a scheme corresponding to framework to design a computational neural multi-system for Emotion (CONEMSE).

The integration of audio-tactile information is modulated by multimodal social interaction with physical contact in infancy.

PubMed

Tanaka, Yukari; Kanakogi, Yasuhiro; Kawasaki, Masahiro; Myowa, Masako

2018-04-01

Interaction between caregivers and infants is multimodal in nature. To react interactively and smoothly to such multimodal signals, infants must integrate all these signals. However, few empirical infant studies have investigated how multimodal social interaction with physical contact facilitates multimodal integration, especially regarding audio - tactile (A-T) information. By using electroencephalogram (EEG) and event-related potentials (ERPs), the present study investigated how neural processing involved in A-T integration is modulated by tactile interaction. Seven- to 8-months-old infants heard one pseudoword both whilst being tickled (multimodal 'A-T' condition), and not being tickled (unimodal 'A' condition). Thereafter, their EEG was measured during the perception of the same words. Compared to the A condition, the A-T condition resulted in enhanced ERPs and higher beta-band activity within the left temporal regions, indicating neural processing of A-T integration. Additionally, theta-band activity within the middle frontal region was enhanced, which may reflect enhanced attention to social information. Furthermore, differential ERPs correlated with the degree of engagement in the tickling interaction. We provide neural evidence that the integration of A-T information in infants' brains is facilitated through tactile interaction with others. Such plastic changes in neural processing may promote harmonious social interaction and effective learning in infancy. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Linking Neural and Symbolic Representation and Processing of Conceptual Structures

PubMed Central

van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

2017-01-01

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures. PMID:28848460

Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation.

PubMed

Werkle-Bergner, Markus; Grandy, Thomas H; Chicherio, Christian; Schmiedek, Florian; Lövdén, Martin; Lindenberger, Ulman

2014-06-18

Higher cognitive functions, such as human perceptual decision making, require information processing and transmission across wide-spread cortical networks. Temporally synchronized neural firing patterns are advantageous for efficiently representing and transmitting information within and between assemblies. Computational, empirical, and conceptual considerations all lead to the expectation that the informational redundancy of neural firing rates is positively related to their synchronization. Recent theorizing and initial evidence also suggest that the coding of stimulus characteristics and their integration with behavioral goal states require neural interactions across a hierarchy of timescales. However, most studies thus have focused on neural activity in a single frequency range or on a restricted set of brain regions. Here we provide evidence for cooperative spatiotemporal dynamics of slow and fast EEG signals during perceptual decision making at the single-trial level. Participants performed three masked two-choice decision tasks, one each with numerical, verbal, or figural content. Decrements in posterior α power (8-14 Hz) were paralleled by increments in high-frequency (>30 Hz) signal entropy in trials demanding active sensory processing. Simultaneously, frontocentral θ power (4-7 Hz) increased, indicating evidence integration. The coordinated α/θ dynamics were tightly linked to decision speed and remarkably similar across tasks, suggesting a domain-general mechanism. In sum, we demonstrate an inverse association between decision-related changes in widespread low-frequency power and local high-frequency entropy. The cooperation among mechanisms captured by these changes enhances the informational density of neural response patterns and qualifies as a neural coding system in the service of perceptual decision making. Copyright © 2014 the authors 0270-6474/14/348519-10$15.00/0.

Shared neural coding for social hierarchy and reward value in primate amygdala.

PubMed

Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

2018-03-01

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Energy coding in biological neural networks

PubMed Central

Zhang, Zhikang

2007-01-01

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model’s ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function. PMID:19003513

Disruption of visual awareness during the attentional blink is reflected by selective disruption of late-stage neural processing

PubMed Central

Harris, Joseph A.; McMahon, Alex R.; Woldorff, Marty G.

2015-01-01

Any information represented in the brain holds the potential to influence behavior. It is therefore of broad interest to determine the extent and quality of neural processing of stimulus input that occurs with and without awareness. The attentional blink is a useful tool for dissociating neural and behavioral measures of perceptual visual processing across conditions of awareness. The extent of higher-order visual information beyond basic sensory signaling that is processed during the attentional blink remains controversial. To determine what neural processing at the level of visual-object identification occurs in the absence of awareness, electrophysiological responses to images of faces and houses were recorded both within and outside of the attentional blink period during a rapid serial visual presentation (RSVP) stream. Electrophysiological results were sorted according to behavioral performance (correctly identified targets versus missed targets) within these blink and non-blink periods. An early index of face-specific processing (the N170, 140–220 ms post-stimulus) was observed regardless of whether the subject demonstrated awareness of the stimulus, whereas a later face-specific effect with the same topographic distribution (500–700 ms post-stimulus) was only seen for accurate behavioral discrimination of the stimulus content. The present findings suggest a multi-stage process of object-category processing, with only the later phase being associated with explicit visual awareness. PMID:23859644

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

NASA Astrophysics Data System (ADS)

QingJie, Wei; WenBin, Wang

2017-06-01

In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval

Unpacking the neural associations of emotion and judgment in emotion-congruent judgment

PubMed Central

Beer, Jennifer S.

2012-01-01

The current study takes a new approach to understand the neural systems that support emotion-congruent judgment. The bulk of previous neural research has inferred emotional influences on judgment from disadvantageous judgments or non-random individual differences. The current study manipulated the influence of emotional information on judgments of stimuli that were equivocally composed of positive and negative attributes. Emotion-congruent processing was operationalized in two ways: neural activation significantly associated with primes that lead to emotionally congruent judgments and neural activation significantly associated with judgments that were preceded by emotionally congruent primes. Distinct regions of medial orbitofrontal cortex were associated with these patterns of emotion-congruent processing. Judgments that were incongruent with preceding primes were associated with dorsomedial prefrontal cortex, ventrolateral prefrontal cortex and lateral orbitofrontal cortex activity. The current study demonstrates a new approach to investigate the neural systems associated with emotion-congruent judgment. The findings suggest that medial OFC may support attentional processes that underlie emotion-congruent judgment. PMID:21511825

Unconscious neural processing differs with method used to render stimuli invisible

PubMed Central

Fogelson, Sergey V.; Kohler, Peter J.; Miller, Kevin J.; Granger, Richard; Tse, Peter U.

2014-01-01

Visual stimuli can be kept from awareness using various methods. The extent of processing that a given stimulus receives in the absence of awareness is typically used to make claims about the role of consciousness more generally. The neural processing elicited by a stimulus, however, may also depend on the method used to keep it from awareness, and not only on whether the stimulus reaches awareness. Here we report that the method used to render an image invisible has a dramatic effect on how category information about the unseen stimulus is encoded across the human brain. We collected fMRI data while subjects viewed images of faces and tools, that were rendered invisible using either continuous flash suppression (CFS) or chromatic flicker fusion (CFF). In a third condition, we presented the same images under normal fully visible viewing conditions. We found that category information about visible images could be extracted from patterns of fMRI responses throughout areas of neocortex known to be involved in face or tool processing. However, category information about stimuli kept from awareness using CFS could be recovered exclusively within occipital cortex, whereas information about stimuli kept from awareness using CFF was also decodable within temporal and frontal regions. We conclude that unconsciously presented objects are processed differently depending on how they are rendered subjectively invisible. Caution should therefore be used in making generalizations on the basis of any one method about the neural basis of consciousness or the extent of information processing without consciousness. PMID:24982647

Unconscious neural processing differs with method used to render stimuli invisible.

PubMed

Fogelson, Sergey V; Kohler, Peter J; Miller, Kevin J; Granger, Richard; Tse, Peter U

2014-01-01

Visual stimuli can be kept from awareness using various methods. The extent of processing that a given stimulus receives in the absence of awareness is typically used to make claims about the role of consciousness more generally. The neural processing elicited by a stimulus, however, may also depend on the method used to keep it from awareness, and not only on whether the stimulus reaches awareness. Here we report that the method used to render an image invisible has a dramatic effect on how category information about the unseen stimulus is encoded across the human brain. We collected fMRI data while subjects viewed images of faces and tools, that were rendered invisible using either continuous flash suppression (CFS) or chromatic flicker fusion (CFF). In a third condition, we presented the same images under normal fully visible viewing conditions. We found that category information about visible images could be extracted from patterns of fMRI responses throughout areas of neocortex known to be involved in face or tool processing. However, category information about stimuli kept from awareness using CFS could be recovered exclusively within occipital cortex, whereas information about stimuli kept from awareness using CFF was also decodable within temporal and frontal regions. We conclude that unconsciously presented objects are processed differently depending on how they are rendered subjectively invisible. Caution should therefore be used in making generalizations on the basis of any one method about the neural basis of consciousness or the extent of information processing without consciousness.

Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2016-04-01

Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

Theory of Mind: A Neural Prediction Problem

PubMed Central

Koster-Hale, Jorie; Saxe, Rebecca

2014-01-01

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others’ goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind. PMID:24012000

Associations between maternal negative affect and adolescent's neural response to peer evaluation

PubMed Central

Tan, Patricia Z.; Lee, Kyung Hwa; Dahl, Ronald E.; Nelson, Eric E.; Stroud, Laura J.; Siegle, Greg J.; Morgan, Judith K.; Silk, Jennifer S.

2016-01-01

Parenting is often implicated as a potential source of individual differences in youths’ emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent–adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents’ neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence. PMID:24613174

Internal state of monkey primary visual cortex (V1) predicts figure-ground perception.

PubMed

Supèr, Hans; van der Togt, Chris; Spekreijse, Henk; Lamme, Victor A F

2003-04-15

When stimulus information enters the visual cortex, it is rapidly processed for identification. However, sometimes the processing of the stimulus is inadequate and the subject fails to notice the stimulus. Human psychophysical studies show that this occurs during states of inattention or absent-mindedness. At a neurophysiological level, it remains unclear what these states are. To study the role of cortical state in perception, we analyzed neural activity in the monkey primary visual cortex before the appearance of a stimulus. We show that, before the appearance of a reported stimulus, neural activity was stronger and more correlated than for a not-reported stimulus. This indicates that the strength of neural activity and the functional connectivity between neurons in the primary visual cortex participate in the perceptual processing of stimulus information. Thus, to detect a stimulus, the visual cortex needs to be in an appropriate state.

Emotional facial expressions reduce neural adaptation to face identity.

PubMed

Gerlicher, Anna M V; van Loon, Anouk M; Scholte, H Steven; Lamme, Victor A F; van der Leij, Andries R

2014-05-01

In human social interactions, facial emotional expressions are a crucial source of information. Repeatedly presented information typically leads to an adaptation of neural responses. However, processing seems sustained with emotional facial expressions. Therefore, we tested whether sustained processing of emotional expressions, especially threat-related expressions, would attenuate neural adaptation. Neutral and emotional expressions (happy, mixed and fearful) of same and different identity were presented at 3 Hz. We used electroencephalography to record the evoked steady-state visual potentials (ssVEP) and tested to what extent the ssVEP amplitude adapts to the same when compared with different face identities. We found adaptation to the identity of a neutral face. However, for emotional faces, adaptation was reduced, decreasing linearly with negative valence, with the least adaptation to fearful expressions. This short and straightforward method may prove to be a valuable new tool in the study of emotional processing.

Surfing a spike wave down the ventral stream.

PubMed

VanRullen, Rufin; Thorpe, Simon J

2002-10-01

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Receptive amusia: evidence for cross-hemispheric neural networks underlying music processing strategies.

PubMed

Schuppert, M; Münte, T F; Wieringa, B M; Altenmüller, E

2000-03-01

Perceptual musical functions were investigated in patients suffering from unilateral cerebrovascular cortical lesions. Using MIDI (Musical Instrument Digital Interface) technique, a standardized short test battery was established that covers local (analytical) as well as global perceptual mechanisms. These represent the principal cognitive strategies in melodic and temporal musical information processing (local, interval and rhythm; global, contour and metre). Of the participating brain-damaged patients, a total of 69% presented with post-lesional impairments in music perception. Left-hemisphere-damaged patients showed significant deficits in the discrimination of local as well as global structures in both melodic and temporal information processing. Right-hemisphere-damaged patients also revealed an overall impairment of music perception, reaching significance in the temporal conditions. Detailed analysis outlined a hierarchical organization, with an initial right-hemisphere recognition of contour and metre followed by identification of interval and rhythm via left-hemisphere subsystems. Patterns of dissociated and associated melodic and temporal deficits indicate autonomous, yet partially integrated neural subsystems underlying the processing of melodic and temporal stimuli. In conclusion, these data contradict a strong hemispheric specificity for music perception, but indicate cross-hemisphere, fragmented neural substrates underlying local and global musical information processing in the melodic and temporal dimensions. Due to the diverse profiles of neuropsychological deficits revealed in earlier investigations as well as in this study, individual aspects of musicality and musical behaviour very likely contribute to the definite formation of these widely distributed neural networks.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

PubMed

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Four Mechanistic Models of Peer Influence on Adolescent Cannabis Use

PubMed Central

Caouette, Justin D.; Feldstein Ewing, Sarah W.

2017-01-01

Purpose of review Most adolescents begin exploring cannabis in peer contexts, but the neural mechanisms that underlie peer influence on adolescent cannabis use are still unknown. This theoretical overview elucidates the intersecting roles of neural function and peer factors in cannabis use in adolescents. Recent findings Novel paradigms using functional magnetic resonance imaging (fMRI) in adolescents have identified distinct neural mechanisms of risk decision-making and incentive processing in peer contexts, centered on reward-motivation and affect regulatory neural networks; these findings inform a theoretical model of peer-driven cannabis use decisions in adolescents. Summary We propose four “mechanistic profiles” of social facilitation of cannabis use in adolescents: (1) peer influence as the primary driver of use; (2) cannabis exploration as the primary driver, which may be enhanced in peer contexts; (3) social anxiety; and (4) negative peer experiences. Identification of “neural targets” involved in motivating cannabis use may inform clinicians about which treatment strategies work best in adolescents with cannabis use problems, and via which social and neurocognitive processes. PMID:29104847

Processing Biological Gender and Number Information during Chinese Pronoun Resolution: ERP Evidence for Functional Differentiation

ERIC Educational Resources Information Center

Xu, Xiaodong; Jiang, Xiaoming; Zhou, Xiaolin

2013-01-01

There have been a number of behavioral and neural studies on the processing of syntactic gender and number agreement information, marked by different morpho-syntactic features during sentence comprehension. By using the event-related potential (ERP) technique, the present study investigated whether the processing of semantic gender information and…

Neural processing of visual information under interocular suppression: a critical review

PubMed Central

Sterzer, Philipp; Stein, Timo; Ludwig, Karin; Rothkirch, Marcus; Hesselmann, Guido

2014-01-01

When dissimilar stimuli are presented to the two eyes, only one stimulus dominates at a time while the other stimulus is invisible due to interocular suppression. When both stimuli are equally potent in competing for awareness, perception alternates spontaneously between the two stimuli, a phenomenon called binocular rivalry. However, when one stimulus is much stronger, e.g., due to higher contrast, the weaker stimulus can be suppressed for prolonged periods of time. A technique that has recently become very popular for the investigation of unconscious visual processing is continuous flash suppression (CFS): High-contrast dynamic patterns shown to one eye can render a low-contrast stimulus shown to the other eye invisible for up to minutes. Studies using CFS have produced new insights but also controversies regarding the types of visual information that can be processed unconsciously as well as the neural sites and the relevance of such unconscious processing. Here, we review the current state of knowledge in regard to neural processing of interocularly suppressed information. Focusing on recent neuroimaging findings, we discuss whether and to what degree such suppressed visual information is processed at early and more advanced levels of the visual processing hierarchy. We review controversial findings related to the influence of attention on early visual processing under interocular suppression, the putative differential roles of dorsal and ventral areas in unconscious object processing, and evidence suggesting privileged unconscious processing of emotional and other socially relevant information. On a more general note, we discuss methodological and conceptual issues, from practical issues of how unawareness of a stimulus is assessed to the overarching question of what constitutes an adequate operational definition of unawareness. Finally, we propose approaches for future research to resolve current controversies in this exciting research area. PMID:24904469

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats

PubMed Central

Deolindo, Camila S.; Kunicki, Ana C. B.; da Silva, Maria I.; Lima Brasil, Fabrício; Moioli, Renan C.

2018-01-01

Accumulating evidence suggests that neural interactions are distributed and relate to animal behavior, but many open questions remain. The neural assembly hypothesis, formulated by Hebb, states that synchronously active single neurons may transiently organize into functional neural circuits—neuronal assemblies (NAs)—and that would constitute the fundamental unit of information processing in the brain. However, the formation, vanishing, and temporal evolution of NAs are not fully understood. In particular, characterizing NAs in multiple brain regions over the course of behavioral tasks is relevant to assess the highly distributed nature of brain processing. In the context of NA characterization, active tactile discrimination tasks with rats are elucidative because they engage several cortical areas in the processing of information that are otherwise masked in passive or anesthetized scenarios. In this work, we investigate the dynamic formation of NAs within and among four different cortical regions in long-range fronto-parieto-occipital networks (primary somatosensory, primary visual, prefrontal, and posterior parietal cortices), simultaneously recorded from seven rats engaged in an active tactile discrimination task. Our results first confirm that task-related neuronal firing rate dynamics in all four regions is significantly modulated. Notably, a support vector machine decoder reveals that neural populations contain more information about the tactile stimulus than the majority of single neurons alone. Then, over the course of the task, we identify the emergence and vanishing of NAs whose participating neurons are shown to contain more information about animal behavior than randomly chosen neurons. Taken together, our results further support the role of multiple and distributed neurons as the functional unit of information processing in the brain (NA hypothesis) and their link to active animal behavior. PMID:29375324

An fMRI-study on semantic priming of panic-related information in depression without comorbid anxiety.

PubMed

Sass, Katharina; Kircher, Tilo; Gauggel, Siegfried; Habel, Ute

2014-04-30

Depression often involves anxiety symptoms and shows a strong comorbidity with panic disorder. However, the neural basis is unclear. The aim of the current study was to use semantic priming to investigate the neural correlates of panic and anxiety-related information processing in depression. In a lexical decision task, panic/agoraphobia-disorder-related and neutral word-pairs were presented during functional magnetic resonance imaging. Participants comprised 19 patients with major depression but without comorbid anxiety and 19 demographically matched controls. On a behavioral level, comparable significant priming effects were found for the neutral condition, while only patients showed a significant inhibition effect (slower reaction time for panic-related stimuli) for the panic condition. On a neural level, significant group differences emerged in left fronto-parietal (enhanced activation for patients) and left temporo-occipital regions (reduced activation for patients). The results showed that depressed patients recruit not only areas related to the interaction of emotion and semantic processing but also regions that are related to fear circuitry to process panic-related information. Hence, in the context of depression, there seems to be a pathological processing of panic-related information that could play an important role during the disorder and should be considered. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity.

PubMed

Wang, Danny J J; Jann, Kay; Fan, Chang; Qiao, Yang; Zang, Yu-Feng; Lu, Hanbing; Yang, Yihong

2018-01-01

Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing-increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.

Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain.

PubMed

Lopopolo, Alessandro; Frank, Stefan L; van den Bosch, Antal; Willems, Roel M

2017-01-01

Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using stochastic measures derived from computational language models to detect neural correlates of phoneme, part-of-speech, and word processing in an fMRI experiment. Probabilistic language models have proven to be useful tools for studying how language is processed as a sequence of symbols unfolding in time. Conditional probabilities between sequences of words are at the basis of probabilistic measures such as surprisal and perplexity which have been successfully used as predictors of several behavioural and neural correlates of sentence processing. Here we computed perplexity from sequences of words and their parts of speech, and their phonemic transcriptions. Brain activity time-locked to each word is regressed on the three model-derived measures. We observe that the brain keeps track of the statistical structure of lexical, syntactic and phonological information in distinct areas.

Interactions between neural networks: a mechanism for tuning chaos and oscillations.

PubMed

Wang, Lipo

2007-06-01

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability.

Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

PubMed

Lee, Sang Eun; Han, Yeji; Park, HyunWook

2016-01-01

The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing.

PubMed

Delis, Ioannis; Dmochowski, Jacek P; Sajda, Paul; Wang, Qi

2018-07-15

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making. Copyright © 2018 Elsevier Inc. All rights reserved.

Rodent wearable ultrasound system for wireless neural recording.

PubMed

Piech, David K; Kay, Joshua E; Boser, Bernhard E; Maharbiz, Michel M

2017-07-01

Advances in minimally-invasive, distributed biological interface nodes enable possibilities for networks of sensors and actuators to connect the brain with external devices. The recent development of the neural dust sensor mote has shown that utilizing ultrasound backscatter communication enables untethered sub-mm neural recording devices. These implanted sensor motes require a wearable external ultrasound interrogation device to enable in-vivo, freely-behaving neural interface experiments. However, minimizing the complexity and size of the implanted sensors shifts the power and processing burden to the external interrogator. In this paper, we present an ultrasound backscatter interrogator that supports real-time backscatter processing in a rodent-wearable, completely wireless device. We demonstrate a generic digital encoding scheme which is intended for transmitting neural information. The system integrates a front-end ultrasonic interface ASIC with off-the-shelf components to enable a highly compact ultrasound interrogation device intended for rodent neural interface experiments but applicable to other model systems.

Neural processing of gendered information is more robustly associated with mothers' gendered communication with children than mothers' implicit and explicit gender stereotypes.

PubMed

Endendijk, Joyce J; Spencer, Hannah; Bos, Peter A; Derks, Belle

2018-04-26

Processes like gender socialization (the ways in which parents convey information to their children about how girls and boys should behave) often happen unconsciously and might therefore be studied best with neuroscientific measures. We examined whether neural processing of gender-stereotype-congruent and incongruent information is more robustly related to mothers' gendered socialization of their child than mothers' implicit and explicit gender stereotypes. To this end, we examined event-related potentials (ERPs) of mothers (N = 35) completing an implicit gender-stereotype task and mothers' gender stereotypes in relation to observed gendered communication with their child (2-6 years old) in a naturalistic picture-book-reading setting. Increased N2 activity (previously related to attentional processes) to gender stimuli in the implicit gender-stereotype task was associated with mothers' positive evaluation of similar gendered behaviors and activities in the picture book they read with their child. Increased P300 activity (previously related to attention to unexpected events) to incongruent trials in the gender-stereotype task was associated with a more positive evaluation of congruent versus incongruent pictures. Compared to mothers' gender stereotypes, neural processing of gendered information was more robustly related to how mothers talk to their children about boys' and girls' stereotype-congruent and incongruent behavior, and masculine and feminine activities.

Neural Network Processing of Natural Language: II. Towards a Unified Model of Corticostriatal Function in Learning Sentence Comprehension and Non-Linguistic Sequencing

ERIC Educational Resources Information Center

Dominey, Peter Ford; Inui, Toshio; Hoen, Michel

2009-01-01

A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in language learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent…

Updated energy budgets for neural computation in the neocortex and cerebellum

PubMed Central

Howarth, Clare; Gleeson, Padraig; Attwell, David

2012-01-01

The brain's energy supply determines its information processing power, and generates functional imaging signals. The energy use on the different subcellular processes underlying neural information processing has been estimated previously for the grey matter of the cerebral and cerebellar cortex. However, these estimates need reevaluating following recent work demonstrating that action potentials in mammalian neurons are much more energy efficient than was previously thought. Using this new knowledge, this paper provides revised estimates for the energy expenditure on neural computation in a simple model for the cerebral cortex and a detailed model of the cerebellar cortex. In cerebral cortex, most signaling energy (50%) is used on postsynaptic glutamate receptors, 21% is used on action potentials, 20% on resting potentials, 5% on presynaptic transmitter release, and 4% on transmitter recycling. In the cerebellar cortex, excitatory neurons use 75% and inhibitory neurons 25% of the signaling energy, and most energy is used on information processing by non-principal neurons: Purkinje cells use only 15% of the signaling energy. The majority of cerebellar signaling energy use is on the maintenance of resting potentials (54%) and postsynaptic receptors (22%), while action potentials account for only 17% of the signaling energy use. PMID:22434069

Stimulus information contaminates summation tests of independent neural representations of features

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2002-01-01

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression

PubMed Central

Garrett, Neil; Sharot, Tali; Faulkner, Paul; Korn, Christoph W.; Roiser, Jonathan P.; Dolan, Raymond J.

2014-01-01

Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health. PMID:25221492

Interoception and stress

PubMed Central

Schulz, André; Vögele, Claus

2015-01-01

Afferent neural signals are continuously transmitted from visceral organs to the brain. Interoception refers to the processing of visceral-afferent neural signals by the central nervous system, which can finally result in the conscious perception of bodily processes. Interoception can, therefore, be described as a prominent example of information processing on the ascending branch of the brain–body axis. Stress responses involve a complex neuro-behavioral cascade, which is elicited when the organism is confronted with a potentially harmful stimulus. As this stress cascade comprises a range of neural and endocrine pathways, stress can be conceptualized as a communication process on the descending branch of the brain–body axis. Interoception and stress are, therefore, associated via the bi-directional transmission of information on the brain–body axis. It could be argued that excessive and/or enduring activation (e.g., by acute or chronic stress) of neural circuits, which are responsible for successful communication on the brain–body axis, induces malfunction and dysregulation of these information processes. As a consequence, interoceptive signal processing may be altered, resulting in physical symptoms contributing to the development and/or maintenance of body-related mental disorders, which are associated with stress. In the current paper, we summarize findings on psychobiological processes underlying acute and chronic stress and their interaction with interoception. While focusing on the role of the physiological stress axes (hypothalamic-pituitary-adrenocortical axis and autonomic nervous system), psychological factors in acute and chronic stress are also discussed. We propose a positive feedback model involving stress (in particular early life or chronic stress, as well as major adverse events), the dysregulation of physiological stress axes, altered perception of bodily sensations, and the generation of physical symptoms, which may in turn facilitate stress. PMID:26257668

Cortical Neural Computation by Discrete Results Hypothesis

PubMed Central

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called “Discrete Results” (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of “Discrete Results” is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel “Discrete Results” concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation. PMID:27807408

Cortical Neural Computation by Discrete Results Hypothesis.

PubMed

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation.

Regional Neural Response Differences in the Determination of Faces or Houses Positioned in a Wide Visual Field

PubMed Central

Wu, Jinglong; Chen, Kewei; Imajyo, Satoshi; Ohno, Seiichiro; Kanazawa, Susumu

2013-01-01

In human visual cortex, the primary visual cortex (V1) is considered to be essential for visual information processing; the fusiform face area (FFA) and parahippocampal place area (PPA) are considered as face-selective region and places-selective region, respectively. Recently, a functional magnetic resonance imaging (fMRI) study showed that the neural activity ratios between V1 and FFA were constant as eccentricities increasing in central visual field. However, in wide visual field, the neural activity relationships between V1 and FFA or V1 and PPA are still unclear. In this work, using fMRI and wide-view present system, we tried to address this issue by measuring neural activities in V1, FFA and PPA for the images of faces and houses aligning in 4 eccentricities and 4 meridians. Then, we further calculated ratio relative to V1 (RRV1) as comparing the neural responses amplitudes in FFA or PPA with those in V1. We found V1, FFA, and PPA showed significant different neural activities to faces and houses in 3 dimensions of eccentricity, meridian, and region. Most importantly, the RRV1s in FFA and PPA also exhibited significant differences in 3 dimensions. In the dimension of eccentricity, both FFA and PPA showed smaller RRV1s at central position than those at peripheral positions. In meridian dimension, both FFA and PPA showed larger RRV1s at upper vertical positions than those at lower vertical positions. In the dimension of region, FFA had larger RRV1s than PPA. We proposed that these differential RRV1s indicated FFA and PPA might have different processing strategies for encoding the wide field visual information from V1. These different processing strategies might depend on the retinal position at which faces or houses are typically observed in daily life. We posited a role of experience in shaping the information processing strategies in the ventral visual cortex. PMID:23991147

AOD furnace splash soft-sensor in the smelting process based on improved BP neural network

NASA Astrophysics Data System (ADS)

Ma, Haitao; Wang, Shanshan; Wu, Libin; Yu, Ying

2017-11-01

In view of argon oxygen refining low carbon ferrochrome production process, in the splash of smelting process as the research object, based on splash mechanism analysis in the smelting process , using multi-sensor information fusion and BP neural network modeling techniques is proposed in this paper, using the vibration signal, the audio signal and the flame image signal in the furnace as the characteristic signal of splash, the vibration signal, the audio signal and the flame image signal in the furnace integration and modeling, and reconstruct splash signal, realize the splash soft measurement in the smelting process, the simulation results show that the method can accurately forecast splash type in the smelting process, provide a new method of measurement for forecast splash in the smelting process, provide more accurate information to control splash.

Neural networks for data mining electronic text collections

NASA Astrophysics Data System (ADS)

Walker, Nicholas; Truman, Gregory

1997-04-01

The use of neural networks in information retrieval and text analysis has primarily suffered from the issues of adequate document representation, the ability to scale to very large collections, dynamism in the face of new information and the practical difficulties of basing the design on the use of supervised training sets. Perhaps the most important approach to begin solving these problems is the use of `intermediate entities' which reduce the dimensionality of document representations and the size of documents collections to manageable levels coupled with the use of unsupervised neural network paradigms. This paper describes the issues, a fully configured neural network-based text analysis system--dataHARVEST--aimed at data mining text collections which begins this process, along with the remaining difficulties and potential ways forward.

The neural substrates of social influence on decision making.

PubMed

Tomlin, Damon; Nedic, Andrea; Prentice, Deborah A; Holmes, Philip; Cohen, Jonathan D

2013-01-01

The mechanisms that govern human learning and decision making under uncertainty have been the focus of intense behavioral and, more recently, neuroscientific investigation. Substantial progress has been made in building models of the processes involved, and identifying underlying neural mechanisms using simple, two-alternative forced choice decision tasks. However, less attention has been given to how social information influences these processes, and the neural systems that mediate this influence. Here we sought to address these questions by using tasks similar to ones that have been used to study individual decision making behavior, and adding conditions in which participants were given trial-by-trial information about the performance of other individuals (their choices and/or their rewards) simultaneously playing the same tasks. We asked two questions: How does such information about the behavior of others influence performance in otherwise simple decision tasks, and what neural systems mediate this influence? We found that bilateral insula exhibited a parametric relationship to the degree of misalignment of the individual's performance with those of others in the group. Furthermore, activity in the bilateral insula significantly predicted participants' subsequent choices to align their behavior with others in the group when they were misaligned either in their choices (independent of success) or their degree of success (independent of specific choices). These findings add to the growing body of empirical data suggesting that the insula participates in an important way in social information processing and decision making.

Neural microgenesis of personally familiar face recognition

PubMed Central

Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

2015-01-01

Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network. PMID:26283361

Neural microgenesis of personally familiar face recognition.

PubMed

Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

2015-09-01

Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network.

3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

NASA Technical Reports Server (NTRS)

Ross, Muriel D.; Cutler, Lynn; Meyer, Glenn; Lam, Tony; Vaziri, Parshaw

1990-01-01

Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

Classification of cardiac arrhythmias using competitive networks.

PubMed

Leite, Cicilia R M; Martin, Daniel L; Sizilio, Glaucia R A; Dos Santos, Keylly E A; de Araujo, Bruno G; Valentim, Ricardo A M; Neto, Adriao D D; de Melo, Jorge D; Guerreiro, Ana M G

2010-01-01

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

NASA Astrophysics Data System (ADS)

Sang, Nong; Zhang, Tianxu

1997-12-01

Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

Mechanism on brain information processing: Energy coding

NASA Astrophysics Data System (ADS)

Wang, Rubin; Zhang, Zhikang; Jiao, Xianfa

2006-09-01

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, the authors present a brand new scientific theory that offers a unique mechanism for brain information processing. They demonstrate that the neural coding produced by the activity of the brain is well described by the theory of energy coding. Due to the energy coding model's ability to reveal mechanisms of brain information processing based upon known biophysical properties, they cannot only reproduce various experimental results of neuroelectrophysiology but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, they estimate that the theory has very important consequences for quantitative research of cognitive function.

Separable Processes Before, During, and After the N400 Elicited by Previously Inferred and New Information: Evidence from Time-Frequency Decompositions

PubMed Central

Steele, Vaughn R.; Bernat, Edward M.; van den Broek, Paul; Collins, Paul F.; Patrick, Christopher J.; Marsolek, Chad J.

2012-01-01

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1–6 Hz (theta) and 0–2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. PMID:23165117

Interactions between neural networks: a mechanism for tuning chaos and oscillations

PubMed Central

2007-01-01

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability. PMID:19003511

Multi-Frame Convolutional Neural Networks for Object Detection in Temporal Data

DTIC Science & Technology

2017-03-01

maximum 200 words) Given the problem of detecting objects in video , existing neural-network solutions rely on a post-processing step to combine...information across frames and strengthen conclusions. This technique has been successful for videos with simple, dominant objects but it cannot detect objects...Computer Science iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT Given the problem of detecting objects in video , existing neural-network solutions rely

Processing of chromatic information in a deep convolutional neural network.

PubMed

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

Neural Mechanisms of Selective Visual Attention.

PubMed

Moore, Tirin; Zirnsak, Marc

2017-01-03

Selective visual attention describes the tendency of visual processing to be confined largely to stimuli that are relevant to behavior. It is among the most fundamental of cognitive functions, particularly in humans and other primates for whom vision is the dominant sense. We review recent progress in identifying the neural mechanisms of selective visual attention. We discuss evidence from studies of different varieties of selective attention and examine how these varieties alter the processing of stimuli by neurons within the visual system, current knowledge of their causal basis, and methods for assessing attentional dysfunctions. In addition, we identify some key questions that remain in identifying the neural mechanisms that give rise to the selective processing of visual information.

Neural markers of errors as endophenotypes in neuropsychiatric disorders

PubMed Central

Manoach, Dara S.; Agam, Yigal

2013-01-01

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach. PMID:23882201

Neural markers of errors as endophenotypes in neuropsychiatric disorders.

PubMed

Manoach, Dara S; Agam, Yigal

2013-01-01

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

Neurocomputation by Reaction Diffusion

NASA Astrophysics Data System (ADS)

Liang, Ping

1995-08-01

This Letter demonstrates the possible role nonsynaptic diffusion neurotransmission may play in neurocomputation using an artificial neural network model. A reaction-diffusion neural network model with field-based information-processing mechanisms is proposed. The advantages of nonsynaptic field neurotransmission from a computational viewpoint demonstrated in this Letter include long-range inhibition using only local interaction, nonhardwired and changeable (target specific) long-range communication pathways, and multiple simultaneous spatiotemporal organization processes in the same medium.

Neural Correlates of Individual Differences in Strategic Retrieval Processing

ERIC Educational Resources Information Center

Bridger, Emma K.; Herron, Jane E.; Elward, Rachael L.; Wilding, Edward L.

2009-01-01

Processes engaged when information is encoded into memory are an important determinant of whether that information will be recovered subsequently. Also influential, however, are processes engaged at the time of retrieval, and these were investigated here by using event-related potentials (ERPs) to measure a specific class of retrieval operations.…

Associations between maternal negative affect and adolescent's neural response to peer evaluation.

PubMed

Tan, Patricia Z; Lee, Kyung Hwa; Dahl, Ronald E; Nelson, Eric E; Stroud, Laura J; Siegle, Greg J; Morgan, Judith K; Silk, Jennifer S

2014-04-01

Parenting is often implicated as a potential source of individual differences in youths' emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent-adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents' neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Neuronal integration in visual cortex elevates face category tuning to conscious face perception

PubMed Central

Fahrenfort, Johannes J.; Snijders, Tineke M.; Heinen, Klaartje; van Gaal, Simon; Scholte, H. Steven; Lamme, Victor A. F.

2012-01-01

The human brain has the extraordinary capability to transform cluttered sensory input into distinct object representations. For example, it is able to rapidly and seemingly without effort detect object categories in complex natural scenes. Surprisingly, category tuning is not sufficient to achieve conscious recognition of objects. What neural process beyond category extraction might elevate neural representations to the level where objects are consciously perceived? Here we show that visible and invisible faces produce similar category-selective responses in the ventral visual cortex. The pattern of neural activity evoked by visible faces could be used to decode the presence of invisible faces and vice versa. However, only visible faces caused extensive response enhancements and changes in neural oscillatory synchronization, as well as increased functional connectivity between higher and lower visual areas. We conclude that conscious face perception is more tightly linked to neural processes of sustained information integration and binding than to processes accommodating face category tuning. PMID:23236162

The neural oscillations of conflict adaptation in the human frontal region.

PubMed

Tang, Dandan; Hu, Li; Chen, Antao

2013-07-01

Incongruency between print color and the semantic meaning of a word in a classical Stroop task activates the human conflict monitoring system and triggers a behavioral conflict. Conflict adaptation has been suggested to mediate the cortical processing of neural oscillations in such a conflict situation. However, the basic mechanisms that underlie the influence of conflict adaptation on the changes of neural oscillations are not clear. In the present study, electroencephalography (EEG) data were recorded from sixteen healthy human participants while they were performing a color-word Stroop task within a novel look-to-do transition design that included two response modalities. In the 'look' condition, participants were informed to look at the color of presented words but no responses were required; in the 'do' condition, they were informed to make arranged responses to the color of presented words. Behaviorally, a reliable conflict adaptation was observed. Time-frequency analysis revealed that (1) in the 'look' condition, theta-band activity in the left- and right-frontal regions reflected a conflict-related process at a response inhibition level; and (2) in the 'do' condition, both theta-band activity in the left-frontal region and alpha-band activity in the left-, right-, and centro-frontal regions reflected a process of conflict control, which triggered neural and behavioral adaptation. Taken together, these results suggest that there are frontal mechanisms involving neural oscillations that can mediate response inhibition processes and control behavioral conflict. Copyright © 2013 Elsevier B.V. All rights reserved.

When language meets action: the neural integration of gesture and speech.

PubMed

Willems, Roel M; Ozyürek, Asli; Hagoort, Peter

2007-10-01

Although generally studied in isolation, language and action often co-occur in everyday life. Here we investigated one particular form of simultaneous language and action, namely speech and gestures that speakers use in everyday communication. In a functional magnetic resonance imaging study, we identified the neural networks involved in the integration of semantic information from speech and gestures. Verbal and/or gestural content could be integrated easily or less easily with the content of the preceding part of speech. Premotor areas involved in action observation (Brodmann area [BA] 6) were found to be specifically modulated by action information "mismatching" to a language context. Importantly, an increase in integration load of both verbal and gestural information into prior speech context activated Broca's area and adjacent cortex (BA 45/47). A classical language area, Broca's area, is not only recruited for language-internal processing but also when action observation is integrated with speech. These findings provide direct evidence that action and language processing share a high-level neural integration system.

Stimulus Sensitivity of a Spiking Neural Network Model

NASA Astrophysics Data System (ADS)

Chevallier, Julien

2018-02-01

Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

Action recognition is sensitive to the identity of the actor.

PubMed

Ferstl, Ylva; Bülthoff, Heinrich; de la Rosa, Stephan

2017-09-01

Recognizing who is carrying out an action is essential for successful human interaction. The cognitive mechanisms underlying this ability are little understood and have been subject of discussions in embodied approaches to action recognition. Here we examine one solution, that visual action recognition processes are at least partly sensitive to the actor's identity. We investigated the dependency between identity information and action related processes by testing the sensitivity of neural action recognition processes to clothing and facial identity information with a behavioral adaptation paradigm. Our results show that action adaptation effects are in fact modulated by both clothing information and the actor's facial identity. The finding demonstrates that neural processes underlying action recognition are sensitive to identity information (including facial identity) and thereby not exclusively tuned to actions. We suggest that such response properties are useful to help humans in knowing who carried out an action. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

The influence of positive and negative emotional associations on semantic processing in depression: an fMRI study.

PubMed

Sass, Katharina; Habel, Ute; Kellermann, Thilo; Mathiak, Klaus; Gauggel, Siegfried; Kircher, Tilo

2014-02-01

In depression, patients suffer from emotional and cognitive deficits, among others in semantic processing. If these semantic deficits are cognitive or interact with emotional dysfunctions, is still an open question. The aim of the current study was to investigate the influence of emotional valence on the neural correlates of semantic priming in major depression. In a lexical decision task, positive, negative, and neutral word pairs were presented during fMRI measurement. Nineteen inpatients and 19 demographically matched controls were recruited. Behaviorally, positive and neutral valence induced a priming effect whereas negative valence induced no effect (controls) or even inhibition (slower RT for related stimuli) in patients. At the neural level, the semantic relation effect revealed similar neural activation in right middle frontal regions for patients and controls. Group differences emerged in the right fusiform gyrus and the ACC. Activity associated with positive valence differed at the DLPFC and amygdala and for negative valence at putamen and cerebellum. The activation of amygdala and DLPFC correlated negatively with the severity of depression. To conclude, semantic processing deficits in depression are modulated by emotional valence of the stimulus on the behavioral as well as on neural level in right-lateralized prefrontal areas and the amygdala. The results highlighted an influence of depression severity on emotion information processing as the severity of symptoms correlated negatively with neural responses to positively and negatively valenced information. Hence, the dysfunctional emotion processing may further enhance the cognitive deficits in depression. Copyright © 2012 Wiley Periodicals, Inc.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Detection, location, and quantification of structural damage by neural-net-processed moiré profilometry

NASA Astrophysics Data System (ADS)

Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Hooker, Jeffery A.

1992-03-01

The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

Neural dynamics underlying emotional transmissions between individuals

PubMed Central

Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

2017-01-01

Abstract Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social–emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional feedback to participants who viewed a movie in the scanner. Participants in the social group (but not in the control group) believed that the feedback was coming from another person who was co-viewing the same movie. We found that social–emotional feedback significantly affected the neural dynamics both in the core affect and in the medial pre-frontal regions. Specifically, the response time-courses in those regions exhibited increased similarity across recipients and increased neural alignment with the timeline of the feedback in the social compared with control group. Taken in conjunction with previous research, this study suggests that emotional cues from others shape the neural dynamics across the whole neural continuum of emotional processing in the brain. Moreover, it demonstrates that interpersonal neural alignment can serve as a neural mechanism through which affective information is conveyed between individuals. PMID:28575520

Stress leads to aberrant hippocampal involvement when processing schema-related information.

PubMed

Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

2018-01-01

Prior knowledge, represented as a mental schema, has critical impact on how we organize, interpret, and process incoming information. Recent findings indicate that the use of an existing schema is coordinated by the medial prefrontal cortex (mPFC), communicating with parietal areas. The hippocampus, however, is crucial for encoding schema-unrelated information but not for schema-related information. A recent study indicated that stress mediators may affect schema-related memory, but the underlying neural mechanisms are currently unknown. Here, we thus tested the impact of acute stress on neural processing of schema-related information. We exposed healthy participants to a stress or control manipulation before they processed, in the MRI scanner, words related or unrelated to a preexisting schema activated by a specific cue. Participants' memory for the presented material was tested 3-5 d after encoding. Overall, the processing of schema-related information activated the mPFC, the precuneus, and the angular gyrus. Stress resulted in aberrant hippocampal activity and connectivity while participants processed schema-related information. This aberrant engagement of the hippocampus was linked to altered subsequent memory. These findings suggest that stress may interfere with the efficient use of prior knowledge during encoding and may have important practical implications, in particular for educational settings. © 2018 Vogel et al.; Published by Cold Spring Harbor Laboratory Press.

The SENNAPE Project: An University-Industry Joint Program in Information Technology.

ERIC Educational Resources Information Center

Seixas, J. M.; Maidantchik, C.; Caloba, L. P.

The SENNAPE (Software Engineering and Neural Networks Applied to Physics and Electricity) project has been putting together the European and the Brazilian industries towards neural processing developments in the fields of high-energy physics and electricity. It is a multi-disciplinary international collaboration with the participation of different…

Results of research on development of an intellectual information system of bankruptcy risk assessment of the enterprise

NASA Astrophysics Data System (ADS)

Telipenko, E.; Chernysheva, T.; Zakharova, A.; Dumchev, A.

2015-10-01

The article represents research results about the knowledge base development for the intellectual information system for the bankruptcy risk assessment of the enterprise. It is described the process analysis of the knowledge base development; the main process stages, some problems and their solutions are given. The article introduces the connectionist model for the bankruptcy risk assessment based on the analysis of industrial enterprise financial accounting. The basis for this connectionist model is a three-layer perceptron with the back propagation of error algorithm. The knowledge base for the intellectual information system consists of processed information and the processing operation method represented as the connectionist model. The article represents the structure of the intellectual information system, the knowledge base, and the information processing algorithm for neural network training. The paper shows mean values of 10 indexes for industrial enterprises; with the help of them it is possible to carry out a financial analysis of industrial enterprises and identify correctly the current situation for well-timed managerial decisions. Results are given about neural network testing on the data of both bankrupt and financially strong enterprises, which were not included into training and test sets.

Frequency tagging to track the neural processing of contrast in fast, continuous sound sequences.

PubMed

Nozaradan, Sylvie; Mouraux, André; Cousineau, Marion

2017-07-01

The human auditory system presents a remarkable ability to detect rapid changes in fast, continuous acoustic sequences, as best illustrated in speech and music. However, the neural processing of rapid auditory contrast remains largely unclear, probably due to the lack of methods to objectively dissociate the response components specifically related to the contrast from the other components in response to the sequence of fast continuous sounds. To overcome this issue, we tested a novel use of the frequency-tagging approach allowing contrast-specific neural responses to be tracked based on their expected frequencies. The EEG was recorded while participants listened to 40-s sequences of sounds presented at 8Hz. A tone or interaural time contrast was embedded every fifth sound (AAAAB), such that a response observed in the EEG at exactly 8 Hz/5 (1.6 Hz) or harmonics should be the signature of contrast processing by neural populations. Contrast-related responses were successfully identified, even in the case of very fine contrasts. Moreover, analysis of the time course of the responses revealed a stable amplitude over repetitions of the AAAAB patterns in the sequence, except for the response to perceptually salient contrasts that showed a buildup and decay across repetitions of the sounds. Overall, this new combination of frequency-tagging with an oddball design provides a valuable complement to the classic, transient, evoked potentials approach, especially in the context of rapid auditory information. Specifically, we provide objective evidence on the neural processing of contrast embedded in fast, continuous sound sequences. NEW & NOTEWORTHY Recent theories suggest that the basis of neurodevelopmental auditory disorders such as dyslexia might be an impaired processing of fast auditory changes, highlighting how the encoding of rapid acoustic information is critical for auditory communication. Here, we present a novel electrophysiological approach to capture in humans neural markers of contrasts in fast continuous tone sequences. Contrast-specific responses were successfully identified, even for very fine contrasts, providing direct insight on the encoding of rapid auditory information. Copyright © 2017 the American Physiological Society.

Synaptic E-I Balance Underlies Efficient Neural Coding.

PubMed

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Synaptic E-I Balance Underlies Efficient Neural Coding

PubMed Central

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding. PMID:29456491

Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition

NASA Astrophysics Data System (ADS)

Boniecki, P.; Nowakowski, K.; Slosarz, P.; Dach, J.; Pilarski, K.

2012-04-01

The purpose of the project was to identify the degree of organic matter decomposition by means of a neural model based on graphical information derived from image analysis. Empirical data (photographs of compost content at various stages of maturation) were used to generate an optimal neural classifier (Boniecki et al. 2009, Nowakowski et al. 2009). The best classification properties were found in an RBF (Radial Basis Function) artificial neural network, which demonstrates that the process is non-linear.

Analysis of haptic information in the cerebral cortex

PubMed Central

2016-01-01

Haptic sensing of objects acquires information about a number of properties. This review summarizes current understanding about how these properties are processed in the cerebral cortex of macaques and humans. Nonnoxious somatosensory inputs, after initial processing in primary somatosensory cortex, are partially segregated into different pathways. A ventrally directed pathway carries information about surface texture into parietal opercular cortex and thence to medial occipital cortex. A dorsally directed pathway transmits information regarding the location of features on objects to the intraparietal sulcus and frontal eye fields. Shape processing occurs mainly in the intraparietal sulcus and lateral occipital complex, while orientation processing is distributed across primary somatosensory cortex, the parietal operculum, the anterior intraparietal sulcus, and a parieto-occipital region. For each of these properties, the respective areas outside primary somatosensory cortex also process corresponding visual information and are thus multisensory. Consistent with the distributed neural processing of haptic object properties, tactile spatial acuity depends on interaction between bottom-up tactile inputs and top-down attentional signals in a distributed neural network. Future work should clarify the roles of the various brain regions and how they interact at the network level. PMID:27440247

Top-down modulation: the crossroads of perception, attention and memory

NASA Astrophysics Data System (ADS)

Gazzaley, Adam

2010-02-01

Research in our laboratory focuses on understanding the neural mechanisms that serve at the crossroads of perception, memory and attention, specifically exploring how brain region interactions underlie these abilities. To accomplish this, we study top-down modulation, the process by which we enhance neural activity associated with relevant information and suppress activity for irrelevant information, thus establishing a neural basis for all higher-order cognitive operations. We also study alterations in top-down modulation that occur with normal aging. Our experiments are performed on human participants, using a multimodal approach that integrates functional MRI (fMRI), transcranial magnetic stimulation (TMS) and electroencephalography (EEG).

Interfacing to the brain’s motor decisions

PubMed Central

2017-01-01

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components. PMID:28003406

The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition

PubMed Central

Wyatte, Dean; Herd, Seth; Mingus, Brian; O’Reilly, Randall

2012-01-01

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain’s binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding – it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes. PMID:22719733

Biophoton signal transmission and processing in the brain.

PubMed

Tang, Rendong; Dai, Jiapei

2014-10-05

The transmission and processing of neural information in the nervous system plays a key role in neural functions. It is well accepted that neural communication is mediated by bioelectricity and chemical molecules via the processes called bioelectrical and chemical transmission, respectively. Indeed, the traditional theories seem to give valuable explanations for the basic functions of the nervous system, but difficult to construct general accepted concepts or principles to provide reasonable explanations of higher brain functions and mental activities, such as perception, learning and memory, emotion and consciousness. Therefore, many unanswered questions and debates over the neural encoding and mechanisms of neuronal networks remain. Cell to cell communication by biophotons, also called ultra-weak photon emissions, has been demonstrated in several plants, bacteria and certain animal cells. Recently, both experimental evidence and theoretical speculation have suggested that biophotons may play a potential role in neural signal transmission and processing, contributing to the understanding of the high functions of nervous system. In this paper, we review the relevant experimental findings and discuss the possible underlying mechanisms of biophoton signal transmission and processing in the nervous system. Copyright © 2014 Elsevier B.V. All rights reserved.

Functional Network Architecture of Reading-Related Regions across Development

ERIC Educational Resources Information Center

Vogel, Alecia C.; Church, Jessica A.; Power, Jonathan D.; Miezin, Fran M.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships…

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Neural-scaled entropy predicts the effects of nonlinear frequency compression on speech perception

PubMed Central

Rallapalli, Varsha H.; Alexander, Joshua M.

2015-01-01

The Neural-Scaled Entropy (NSE) model quantifies information in the speech signal that has been altered beyond simple gain adjustments by sensorineural hearing loss (SNHL) and various signal processing. An extension of Cochlear-Scaled Entropy (CSE) [Stilp, Kiefte, Alexander, and Kluender (2010). J. Acoust. Soc. Am. 128(4), 2112–2126], NSE quantifies information as the change in 1-ms neural firing patterns across frequency. To evaluate the model, data from a study that examined nonlinear frequency compression (NFC) in listeners with SNHL were used because NFC can recode the same input information in multiple ways in the output, resulting in different outcomes for different speech classes. Overall, predictions were more accurate for NSE than CSE. The NSE model accurately described the observed degradation in recognition, and lack thereof, for consonants in a vowel-consonant-vowel context that had been processed in different ways by NFC. While NSE accurately predicted recognition of vowel stimuli processed with NFC, it underestimated them relative to a low-pass control condition without NFC. In addition, without modifications, it could not predict the observed improvement in recognition for word final /s/ and /z/. Findings suggest that model modifications that include information from slower modulations might improve predictions across a wider variety of conditions. PMID:26627780

Visual Working Memory Enhances the Neural Response to Matching Visual Input.

PubMed

Gayet, Surya; Guggenmos, Matthias; Christophel, Thomas B; Haynes, John-Dylan; Paffen, Chris L E; Van der Stigchel, Stefan; Sterzer, Philipp

2017-07-12

Visual working memory (VWM) is used to maintain visual information available for subsequent goal-directed behavior. The content of VWM has been shown to affect the behavioral response to concurrent visual input, suggesting that visual representations originating from VWM and from sensory input draw upon a shared neural substrate (i.e., a sensory recruitment stance on VWM storage). Here, we hypothesized that visual information maintained in VWM would enhance the neural response to concurrent visual input that matches the content of VWM. To test this hypothesis, we measured fMRI BOLD responses to task-irrelevant stimuli acquired from 15 human participants (three males) performing a concurrent delayed match-to-sample task. In this task, observers were sequentially presented with two shape stimuli and a retro-cue indicating which of the two shapes should be memorized for subsequent recognition. During the retention interval, a task-irrelevant shape (the probe) was briefly presented in the peripheral visual field, which could either match or mismatch the shape category of the memorized stimulus. We show that this probe stimulus elicited a stronger BOLD response, and allowed for increased shape-classification performance, when it matched rather than mismatched the concurrently memorized content, despite identical visual stimulation. Our results demonstrate that VWM enhances the neural response to concurrent visual input in a content-specific way. This finding is consistent with the view that neural populations involved in sensory processing are recruited for VWM storage, and it provides a common explanation for a plethora of behavioral studies in which VWM-matching visual input elicits a stronger behavioral and perceptual response. SIGNIFICANCE STATEMENT Humans heavily rely on visual information to interact with their environment and frequently must memorize such information for later use. Visual working memory allows for maintaining such visual information in the mind's eye after termination of its retinal input. It is hypothesized that information maintained in visual working memory relies on the same neural populations that process visual input. Accordingly, the content of visual working memory is known to affect our conscious perception of concurrent visual input. Here, we demonstrate for the first time that visual input elicits an enhanced neural response when it matches the content of visual working memory, both in terms of signal strength and information content. Copyright © 2017 the authors 0270-6474/17/376638-10$15.00/0.

An fMRI Study on Conceptual, Grammatical, and Morpho-Phonological Processing

ERIC Educational Resources Information Center

Longoni, F.; Grande, M.; Hendrich, V.; Kastrau, F.; Huber, W.

2005-01-01

The aim of the present study was to determine whether processing of syntactic word information (lemma) is subserved by the same neural substrate as processing of conceptual or word form information (lexeme). We measured BOLD responses in 14 native speakers of German in three different decision tasks, each focussing specifically on one level of…

Cognitive Control Mechanisms, Emotion & Memory: A neural perspective with implications for psychopathology

PubMed Central

Banich, Marie T.; Mackiewicz, Kristen L.; Depue, Brendan E.; Whitmer, Anson; Miller, Gregory A.; Heller, Wendy

2009-01-01

In this paper we provide a focused review of the literature examining neural mechanisms involved in cognitive control over memory processes that can influence, and in turn are influenced, by emotional processes. The review is divided into two parts, the first focusing on working memory and the second on long-term memory. With regard to working memory, we discuss the neural bases of 1) control mechanisms that can select against distracting emotional information, 2) mechanisms that can regulate emotional reactions or responses, 3) how mood state influences cognitive control, and 4) individual differences in control mechanisms. For long-term memory, we briefly review 1) the neural substrates of emotional memory, 2) the cognitive and neural mechanisms that are involved in controlling emotional memories and 3) how these systems are altered in post-traumatic stress disorder. Finally, we consider tentative generalizations that can be drawn from this relatively unexplored conjunction of research endeavors. PMID:18948135

Neural Pathways of Embarrassment and their Modulation by Social Anxiety

PubMed Central

Müller-Pinzler, L; Gazzola, V; Keysers, C; Sommer, J; Jansen, A; Frässle, S; Einhäuser, W

2016-01-01

While being in the center of attention and exposed to other’s evaluations humans are prone to experience embarrassment. To characterize the neural underpinnings of such aversive moments, we induced genuine experiences of embarrassment during person-group interactions in a functional neuroimaging study. Using a mock-up scenario with three confederates, we examined how the presence of an audience affected physiological and neural responses and the reported emotional experiences of failures and achievements. The results indicated that publicity induced activations in mentalizing areas and failures led to activations in arousal processing systems. Mentalizing activity as well as attention towards the audience were increased in socially anxious participants. The converging integration of information from mentalizing areas and arousal processing systems within the ventral anterior insula and amygdala form the neural pathways of embarrassment. Targeting these neural markers of embarrassment in the (para-)limbic system provides new perspectives for developing treatment strategies for social anxiety disorders. PMID:26093329

Emotion processing in words: a test of the neural re-use hypothesis using surface and intracranial EEG.

PubMed

Ponz, Aurélie; Montant, Marie; Liegeois-Chauvel, Catherine; Silva, Catarina; Braun, Mario; Jacobs, Arthur M; Ziegler, Johannes C

2014-05-01

This study investigates the spatiotemporal brain dynamics of emotional information processing during reading using a combination of surface and intracranial electroencephalography (EEG). Two different theoretical views were opposed. According to the standard psycholinguistic perspective, emotional responses to words are generated within the reading network itself subsequent to semantic activation. According to the neural re-use perspective, brain regions that are involved in processing emotional information contained in other stimuli (faces, pictures, smells) might be in charge of the processing of emotional information in words as well. We focused on a specific emotion-disgust-which has a clear locus in the brain, the anterior insula. Surface EEG showed differences between disgust and neutral words as early as 200 ms. Source localization suggested a cortical generator of the emotion effect in the left anterior insula. These findings were corroborated through the intracranial recordings of two epileptic patients with depth electrodes in insular and orbitofrontal areas. Both electrodes showed effects of disgust in reading as early as 200 ms. The early emotion effect in a brain region (insula) that responds to specific emotions in a variety of situations and stimuli clearly challenges classic sequential theories of reading in favor of the neural re-use perspective.

Neural correlates of recognition memory of social information in people with schizophrenia

PubMed Central

Harvey, Philippe-Olivier; Lepage, Martin

2014-01-01

Background Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Methods Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d′. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Results Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Limitations Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Conclusion Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation. PMID:24119792

Neural correlates of recognition memory of social information in people with schizophrenia.

PubMed

Harvey, Philippe-Olivier; Lepage, Martin

2014-03-01

Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d'. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation.

Alpha band event-related desynchronization underlying social situational context processing during irony comprehension: A magnetoencephalography source localization study.

PubMed

Akimoto, Yoritaka; Takahashi, Hidetoshi; Gunji, Atsuko; Kaneko, Yuu; Asano, Michiko; Matsuo, Junko; Ota, Miho; Kunugi, Hiroshi; Hanakawa, Takashi; Mazuka, Reiko; Kamio, Yoko

2017-12-01

Irony comprehension requires integration of social contextual information. Previous studies have investigated temporal aspects of irony processing and its neural substrates using psychological/electroencephalogram or functional magnetic resonance imaging methods, but have not clarified the temporospatial neural mechanisms of irony comprehension. Therefore, we used magnetoencephalography to investigate the neural generators of alpha-band (8-13Hz) event-related desynchronization (ERD) occurring from 600 to 900ms following the onset of a critical sentence at which social situational contexts activated ironic representation. We found that the right anterior temporal lobe, which is involved in processing social knowledge and evaluating others' intentions, exhibited stronger alpha ERD following an ironic statement than following a literal statement. We also found that alpha power in the left anterior temporal lobe correlated with the participants' communication abilities. These results elucidate the temporospatial neural mechanisms of language comprehension in social contexts, including non-literal processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Three-Dimensional Flexible Electronics Enabled by Shape Memory Polymer Substrates for Responsive Neural Interfaces.

PubMed

Ware, Taylor; Simon, Dustin; Hearon, Keith; Liu, Clive; Shah, Sagar; Reeder, Jonathan; Khodaparast, Navid; Kilgard, Michael P; Maitland, Duncan J; Rennaker, Robert L; Voit, Walter E

2012-12-01

Planar electronics processing methods have enabled neural interfaces to become more precise and deliver more information. However, this processing paradigm is inherently 2D and rigid. The resulting mechanical and geometrical mismatch at the biotic-abiotic interface can elicit an immune response that prevents effective stimulation. In this work, a thiol-ene/acrylate shape memory polymer is utilized to create 3D softening substrates for stimulation electrodes. This substrate system is shown to soften in vivo from more than 600 to 6 MPa. A nerve cuff electrode that coils around the vagus nerve in a rat and that drives neural activity is demonstrated.

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Emotion identification and aging: Behavioral and neural age-related changes.

PubMed

Gonçalves, Ana R; Fernandes, Carina; Pasion, Rita; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João

2018-05-01

Aging is known to alter the processing of facial expressions of emotion (FEE), however the impact of this alteration is less clear. Additionally, there is little information about the temporal dynamics of the neural processing of facial affect. We examined behavioral and neural age-related changes in the identification of FEE using event-related potentials. Furthermore, we analyze the relationship between behavioral/neural responses and neuropsychological functioning. To this purpose, 30 younger adults, 29 middle-aged adults and 26 older adults identified FEE. The behavioral results showed a similar performance between groups. The neural results showed no significant differences between groups for the P100 component and an increased N170 amplitude in the older group. Furthermore, a pattern of asymmetric activation was evident in the N170 component. Results also suggest deficits in facial feature decoding abilities, reflected by a reduced N250 amplitude in older adults. Neuropsychological functioning predicts P100 modulation, but does not seem to influence emotion identification ability. The findings suggest the existence of a compensatory function that would explain the age-equivalent performance in emotion identification. The study may help future research addressing behavioral and neural processes involved on processing of FEE in neurodegenerative conditions. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Neural Representations of Location Outside the Hippocampus

ERIC Educational Resources Information Center

Knierim, James J.

2006-01-01

Place cells of the rat hippocampus are a dominant model system for understanding the role of the hippocampus in learning and memory at the level of single-unit and neural ensemble responses. A complete understanding of the information processing and computations performed by the hippocampus requires detailed knowledge about the properties of the…

Human high intelligence is involved in spectral redshift of biophotonic activities in the brain

PubMed Central

Wang, Niting; Li, Zehua; Xiao, Fangyan; Dai, Jiapei

2016-01-01

Human beings hold higher intelligence than other animals on Earth; however, it is still unclear which brain properties might explain the underlying mechanisms. The brain is a major energy-consuming organ compared with other organs. Neural signal communications and information processing in neural circuits play an important role in the realization of various neural functions, whereas improvement in cognitive function is driven by the need for more effective communication that requires less energy. Combining the ultraweak biophoton imaging system (UBIS) with the biophoton spectral analysis device (BSAD), we found that glutamate-induced biophotonic activities and transmission in the brain, which has recently been demonstrated as a novel neural signal communication mechanism, present a spectral redshift from animals (in order of bullfrog, mouse, chicken, pig, and monkey) to humans, even up to a near-infrared wavelength (∼865 nm) in the human brain. This brain property may be a key biophysical basis for explaining high intelligence in humans because biophoton spectral redshift could be a more economical and effective measure of biophotonic signal communications and information processing in the human brain. PMID:27432962

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

PubMed

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Robust information propagation through noisy neural circuits

PubMed Central

Pouget, Alexandre

2017-01-01

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. PMID:28419098

An Algebra-Based Introductory Computational Neuroscience Course with Lab.

PubMed

Fink, Christian G

2017-01-01

A course in computational neuroscience has been developed at Ohio Wesleyan University which requires no previous experience with calculus or computer programming, and which exposes students to theoretical models of neural information processing and techniques for analyzing neural data. The exploration of theoretical models of neural processes is conducted in the classroom portion of the course, while data analysis techniques are covered in lab. Students learn to program in MATLAB and are offered the opportunity to conclude the course with a final project in which they explore a topic of their choice within computational neuroscience. Results from a questionnaire administered at the beginning and end of the course indicate significant gains in student facility with core concepts in computational neuroscience, as well as with analysis techniques applied to neural data.

A plausible neural circuit for decision making and its formation based on reinforcement learning.

PubMed

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

The neural time course of art perception: an ERP study on the processing of style versus content in art.

PubMed

Augustin, M Dorothee; Defranceschi, Birgit; Fuchs, Helene K; Carbon, Claus-Christian; Hutzler, Florian

2011-06-01

A central prerequisite to understand the phenomenon of art in psychological terms is to investigate the nature of the underlying perceptual and cognitive processes. Building on a study by Augustin, Leder, Hutzler, and Carbon (2008) the current ERP study examined the neural time course of two central aspects of representational art, one of which is closely related to object- and scene perception, the other of which is art-specific: content and style. We adapted a paradigm that has repeatedly been employed in psycholinguistics and that allows one to examine the neural time course of two processes in terms of when sufficient information is available to allow successful classification. Twenty-two participants viewed pictures that systematically varied in style and content and conducted a combined go/nogo dual choice task. The dependent variables of interest were the Lateralised Readiness Potential (LRP) and the N200 effect. Analyses of both measures support the notion that in the processing of art style follows content, with style-related information being available at around 224 ms or between 40 and 94 ms later than content-related information. The paradigm used here offers a promising approach to further explore the time course of art perception, thus helping to unravel the perceptual and cognitive processes that underlie the phenomenon of art and the fascination it exerts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum

NASA Astrophysics Data System (ADS)

Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.

2017-07-01

The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

Dynamic information processing states revealed through neurocognitive models of object semantics

PubMed Central

Clarke, Alex

2015-01-01

Recognising objects relies on highly dynamic, interactive brain networks to process multiple aspects of object information. To fully understand how different forms of information about objects are represented and processed in the brain requires a neurocognitive account of visual object recognition that combines a detailed cognitive model of semantic knowledge with a neurobiological model of visual object processing. Here we ask how specific cognitive factors are instantiated in our mental processes and how they dynamically evolve over time. We suggest that coarse semantic information, based on generic shared semantic knowledge, is rapidly extracted from visual inputs and is sufficient to drive rapid category decisions. Subsequent recurrent neural activity between the anterior temporal lobe and posterior fusiform supports the formation of object-specific semantic representations – a conjunctive process primarily driven by the perirhinal cortex. These object-specific representations require the integration of shared and distinguishing object properties and support the unique recognition of objects. We conclude that a valuable way of understanding the cognitive activity of the brain is though testing the relationship between specific cognitive measures and dynamic neural activity. This kind of approach allows us to move towards uncovering the information processing states of the brain and how they evolve over time. PMID:25745632

Schizophrenia and autism as contrasting minds: neural evidence for the hypo-hyper-intentionality hypothesis.

PubMed

Ciaramidaro, Angela; Bölte, Sven; Schlitt, Sabine; Hainz, Daniela; Poustka, Fritz; Weber, Bernhard; Bara, Bruno G; Freitag, Christine; Walter, Henrik

2015-01-01

Both schizophrenia (SCZ) and autism spectrum disorder (ASD) are characterized by mentalizing problems and associated neural dysfunction of the social brain. However, the deficits in mental state attribution are somehow opposed: Whereas patients with SCZ tend to over-attribute intentions to agents and physical events ("hyper-intentionality"), patients with autism treat people as devoid of intentions ("hypo-intentionality"). Here we aimed to investigate whether this hypo-hyper-intentionality hypothesis can be supported by neural evidence during a mentalizing task. Using functional magnetic resonance imaging (fMRI), we investigated the neural responses and functional connectivity during reading others intention. Scanning was performed in 23 individuals with ASD, 18 with paranoid SCZ and 23 gender and IQ matched control subjects. Both clinical groups showed reduced brain activation compared to controls for the contrast intentional vs physical information processing in left posterior superior temporal sulcus (pSTS) and ventral medial prefrontal cortex (vMPFC) for SCZ, and right pSTS in ASD. As predicted, these effects were caused in a group specific way: Relative increased activation for physical information processing in SCZ that was also correlated with positive PANNS score and relative decreased activation for intentional information processing in ASD. Additionally, we could demonstrate opposed connectivity patterns between the right pSTS and vMPFC in the clinical groups, ie, increased for SCZ, decreased for ASD. These findings represent opposed neural signatures in key regions of the social brain as predicted by the hyper-hypo-intentionality hypothesis. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A biologically inspired model of bat echolocation in a cluttered environment with inputs designed from field Recordings

NASA Astrophysics Data System (ADS)

Loncich, Kristen Teczar

Bat echolocation strategies and neural processing of acoustic information, with a focus on cluttered environments, is investigated in this study. How a bat processes the dense field of echoes received while navigating and foraging in the dark is not well understood. While several models have been developed to describe the mechanisms behind bat echolocation, most are based in mathematics rather than biology, and focus on either peripheral or neural processing---not exploring how these two levels of processing are vitally connected. Current echolocation models also do not use habitat specific acoustic input, or account for field observations of echolocation strategies. Here, a new approach to echolocation modeling is described capturing the full picture of echolocation from signal generation to a neural picture of the acoustic scene. A biologically inspired echolocation model is developed using field research measurements of the interpulse interval timing used by a frequency modulating (FM) bat in the wild, with a whole method approach to modeling echolocation including habitat specific acoustic inputs, a biologically accurate peripheral model of sound processing by the outer, middle, and inner ear, and finally a neural model incorporating established auditory pathways and neuron types with echolocation adaptations. Field recordings analyzed underscore bat sonar design differences observed in the laboratory and wild, and suggest a correlation between interpulse interval groupings and increased clutter. The scenario model provides habitat and behavior specific echoes and is a useful tool for both modeling and behavioral studies, and the peripheral and neural model show that spike-time information and echolocation specific neuron types can produce target localization in the midbrain.

Stress affects the neural ensemble for integrating new information and prior knowledge.

PubMed

Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

2018-06-01

Prior knowledge, represented as a schema, facilitates memory encoding. This schema-related learning is assumed to rely on the medial prefrontal cortex (mPFC) that rapidly integrates new information into the schema, whereas schema-incongruent or novel information is encoded by the hippocampus. Stress is a powerful modulator of prefrontal and hippocampal functioning and first studies suggest a stress-induced deficit of schema-related learning. However, the underlying neural mechanism is currently unknown. To investigate the neural basis of a stress-induced schema-related learning impairment, participants first acquired a schema. One day later, they underwent a stress induction or a control procedure before learning schema-related and novel information in the MRI scanner. In line with previous studies, learning schema-related compared to novel information activated the mPFC, angular gyrus, and precuneus. Stress, however, affected the neural ensemble activated during learning. Whereas the control group distinguished between sets of brain regions for related and novel information, stressed individuals engaged the hippocampus even when a relevant schema was present. Additionally, stressed participants displayed aberrant functional connectivity between brain regions involved in schema processing when encoding novel information. The failure to segregate functional connectivity patterns depending on the presence of prior knowledge was linked to impaired performance after stress. Our results show that stress affects the neural ensemble underlying the efficient use of schemas during learning. These findings may have relevant implications for clinical and educational settings. Copyright © 2018 Elsevier Inc. All rights reserved.

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

PubMed Central

Ehrenfeld, Stephan; Herbort, Oliver; Butz, Martin V.

2013-01-01

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control. PMID:24191151

Goal-seeking neural net for recall and recognition

NASA Astrophysics Data System (ADS)

Omidvar, Omid M.

1990-07-01

Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

Neural networks for data compression and invariant image recognition

NASA Technical Reports Server (NTRS)

Gardner, Sheldon

1989-01-01

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

PubMed

Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

2017-05-24

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. Copyright © 2017 the authors 0270-6474/17/375378-15$15.00/0.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex

PubMed Central

2017-01-01

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations (rnoise) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on rnoise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in rnoise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations (rnoise) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on rnoise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. PMID:28432139

How learning to abstract shapes neural sound representations

PubMed Central

Ley, Anke; Vroomen, Jean; Formisano, Elia

2014-01-01

The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes—even in absence of changes in overall signal level—these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations. PMID:24917783

Psychophysiology of dissociated consciousness.

PubMed

Bob, Petr

2014-01-01

Recent study of consciousness provides an evidence that there is a limit of consciousness, which presents a barrier between conscious and unconscious processes. This barrier likely is specifically manifested as a disturbance of neural mechanisms of consciousness that through distributed brain processing, attentional mechanisms and memory processes enable to constitute integrative conscious experience. According to recent findings a level of conscious integration may change during certain conditions related to experimental cognitive manipulations, hypnosis, or stressful experiences that can lead to dissociation of consciousness. In psychopathological research the term dissociation was proposed by Pierre Janet for explanation of processes related to splitting of consciousness due to traumatic events or during hypnosis. According to several recent findings dissociation of consciousness likely is related to deficits in global distribution of information and may lead to heightened levels of "neural complexity" that reflects brain integration or differentiation based on numbers of independent neural processes in the brain that may be specifically related to various mental disorders.

Optical resonators and neural networks

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.

1986-08-01

It may be possible to implement neural network models using continuous field optical architectures. These devices offer the inherent parallelism of propagating waves and an information density in principle dictated by the wavelength of light and the quality of the bulk optical elements. Few components are needed to construct a relatively large equivalent network. Various associative memories based on optical resonators have been demonstrated in the literature, a ring resonator design is discussed in detail here. Information is stored in a holographic medium and recalled through a competitive processes in the gain medium supplying energy to the ring rsonator. The resonator memory is the first realized example of a neural network function implemented with this kind of architecture.

Texture and art with deep neural networks.

PubMed

Gatys, Leon A; Ecker, Alexander S; Bethge, Matthias

2017-10-01

Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated by visual neuroscience have led to a substantial advance in image synthesis and manipulation in computer vision using convolutional neural networks (CNNs). Here, we review these recent advances and discuss how they can in turn inspire new research in visual perception and computational neuroscience. Copyright © 2017. Published by Elsevier Ltd.

Persistent neural activity in auditory cortex is related to auditory working memory in humans and nonhuman primates

PubMed Central

Huang, Ying; Matysiak, Artur; Heil, Peter; König, Reinhard; Brosch, Michael

2016-01-01

Working memory is the cognitive capacity of short-term storage of information for goal-directed behaviors. Where and how this capacity is implemented in the brain are unresolved questions. We show that auditory cortex stores information by persistent changes of neural activity. We separated activity related to working memory from activity related to other mental processes by having humans and monkeys perform different tasks with varying working memory demands on the same sound sequences. Working memory was reflected in the spiking activity of individual neurons in auditory cortex and in the activity of neuronal populations, that is, in local field potentials and magnetic fields. Our results provide direct support for the idea that temporary storage of information recruits the same brain areas that also process the information. Because similar activity was observed in the two species, the cellular bases of some auditory working memory processes in humans can be studied in monkeys. DOI: http://dx.doi.org/10.7554/eLife.15441.001 PMID:27438411

Separable processes before, during, and after the N400 elicited by previously inferred and new information: evidence from time-frequency decompositions.

PubMed

Steele, Vaughn R; Bernat, Edward M; van den Broek, Paul; Collins, Paul F; Patrick, Christopher J; Marsolek, Chad J

2013-01-25

Successful comprehension during reading often requires inferring information not explicitly presented. This information is readily accessible when subsequently encountered, and a neural correlate of this is an attenuation of the N400 event-related potential (ERP). We used ERPs and time-frequency (TF) analysis to investigate neural correlates of processing inferred information after a causal coherence inference had been generated during text comprehension. Participants read short texts, some of which promoted inference generation. After each text, they performed lexical decisions to target words that were unrelated or inference-related to the preceding text. Consistent with previous findings, inference-related words elicited an attenuated N400 relative to unrelated words. TF analyses revealed unique contributions to the N400 from activity occurring at 1-6 Hz (theta) and 0-2 Hz (delta), supporting the view that multiple, sequential processes underlie the N400. Copyright © 2012 Elsevier B.V. All rights reserved.

Sleep Disrupts High-Level Speech Parsing Despite Significant Basic Auditory Processing.

PubMed

Makov, Shiri; Sharon, Omer; Ding, Nai; Ben-Shachar, Michal; Nir, Yuval; Zion Golumbic, Elana

2017-08-09

The extent to which the sleeping brain processes sensory information remains unclear. This is particularly true for continuous and complex stimuli such as speech, in which information is organized into hierarchically embedded structures. Recently, novel metrics for assessing the neural representation of continuous speech have been developed using noninvasive brain recordings that have thus far only been tested during wakefulness. Here we investigated, for the first time, the sleeping brain's capacity to process continuous speech at different hierarchical levels using a newly developed Concurrent Hierarchical Tracking (CHT) approach that allows monitoring the neural representation and processing-depth of continuous speech online. Speech sequences were compiled with syllables, words, phrases, and sentences occurring at fixed time intervals such that different linguistic levels correspond to distinct frequencies. This enabled us to distinguish their neural signatures in brain activity. We compared the neural tracking of intelligible versus unintelligible (scrambled and foreign) speech across states of wakefulness and sleep using high-density EEG in humans. We found that neural tracking of stimulus acoustics was comparable across wakefulness and sleep and similar across all conditions regardless of speech intelligibility. In contrast, neural tracking of higher-order linguistic constructs (words, phrases, and sentences) was only observed for intelligible speech during wakefulness and could not be detected at all during nonrapid eye movement or rapid eye movement sleep. These results suggest that, whereas low-level auditory processing is relatively preserved during sleep, higher-level hierarchical linguistic parsing is severely disrupted, thereby revealing the capacity and limits of language processing during sleep. SIGNIFICANCE STATEMENT Despite the persistence of some sensory processing during sleep, it is unclear whether high-level cognitive processes such as speech parsing are also preserved. We used a novel approach for studying the depth of speech processing across wakefulness and sleep while tracking neuronal activity with EEG. We found that responses to the auditory sound stream remained intact; however, the sleeping brain did not show signs of hierarchical parsing of the continuous stream of syllables into words, phrases, and sentences. The results suggest that sleep imposes a functional barrier between basic sensory processing and high-level cognitive processing. This paradigm also holds promise for studying residual cognitive abilities in a wide array of unresponsive states. Copyright © 2017 the authors 0270-6474/17/377772-10$15.00/0.

Information-geometric measures estimate neural interactions during oscillatory brain states

PubMed Central

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain. PMID:24605089

Information-geometric measures estimate neural interactions during oscillatory brain states.

PubMed

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Przybył, J.; Koszela, K.; Boniecki, P.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.

2014-04-01

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

Who was the agent? The neural correlates of reanalysis processes during sentence comprehension.

PubMed

Hirotani, Masako; Makuuchi, Michiru; Rüschemeyer, Shirley-Ann; Friederici, Angela D

2011-11-01

Sentence comprehension is a complex process. Besides identifying the meaning of each word and processing the syntactic structure of a sentence, it requires the computation of thematic information, that is, information about who did what to whom. The present fMRI study investigated the neural basis for thematic reanalysis (reanalysis of the thematic roles initially assigned to noun phrases in a sentence) and its interplay with syntactic reanalysis (reanalysis of the underlying syntactic structure originally constructed for a sentence). Thematic reanalysis recruited a network consisting of Broca's area, that is, the left pars triangularis (LPT), and the left posterior superior temporal gyrus, whereas only LPT showed greater sensitivity to syntactic reanalysis. These data provide direct evidence for a functional neuroanatomical basis for two linguistically motivated reanalysis processes during sentence comprehension. Copyright © 2010 Wiley-Liss, Inc.

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

A Brain-Machine-Muscle Interface for Restoring Hindlimb Locomotion after Complete Spinal Transection in Rats

PubMed Central

Alam, Monzurul; Chen, Xi; Zhang, Zicong; Li, Yan; He, Jufang

2014-01-01

A brain-machine interface (BMI) is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking) could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics. PMID:25084446

A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

PubMed

Alam, Monzurul; Chen, Xi; Zhang, Zicong; Li, Yan; He, Jufang

2014-01-01

A brain-machine interface (BMI) is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking) could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

Dopamine controls the neural dynamics of memory signals and retrieval accuracy.

PubMed

Apitz, Thore; Bunzeck, Nico

2013-11-01

The human brain is capable of differentiating between new and already stored information rapidly to allow optimal behavior and decision-making. Although the neural mechanisms of novelty discrimination were often described as temporally constant (ie, with specific latencies), recent electrophysiological studies have demonstrated that the onset of neural novelty signals (ie, differences in event-related responses to new and old items) can be accelerated by reward motivation. While the precise physiological mechanisms underlying this acceleration remain unclear, the involvement of the neurotransmitter dopamine in both novelty and reward processing suggests that enhanced dopamine levels in the context of reward prospect may have a role. To investigate this hypothesis, we used magnetoencephalography (MEG) in combination with an old/new recognition memory task in which correct discrimination between old and new items was rewarded. Importantly, before the task, human subjects received either 150 mg of the dopamine precursor levodopa or placebo. For the placebo group, old/new signals peaked at ∼100 ms after stimulus onset over left temporal/occipital sensors. In contrast, after levodopa administration earliest old/new effects only emerged after ∼400 ms and retrieval accuracy was reduced as expressed in lower d' values. As such, our results point towards a previously unreported role of dopamine in controlling the chronometry of neural processes underlying the distinction between old and new information. They also suggest that this relationship follows a nonlinear function whereby slightly enhanced dopamine levels accelerate neural/cognitive processes and excessive dopamine levels impair them.

Processing Of Visual Information In Primate Brains

NASA Technical Reports Server (NTRS)

Anderson, Charles H.; Van Essen, David C.

1991-01-01

Report reviews and analyzes information-processing strategies and pathways in primate retina and visual cortex. Of interest both in biological fields and in such related computational fields as artificial neural networks. Focuses on data from macaque, which has superb visual system similar to that of humans. Authors stress concept of "good engineering" in understanding visual system.

The Importance of Reaction Times for Developmental Science: What a Difference Milliseconds Make

ERIC Educational Resources Information Center

Lange-Küttner, Christiane

2012-01-01

Reaction times are still rarely reported in developmental psychology although they are an indicator of the neural maturity of children's information processing system. Competence and capacity are confounded in development, where children may be able to reason, or remember, but are unable to cope with information processing load. Furthermore, there…

Neural differences in the processing of true and false sentences: insights into the nature of 'truth' in language comprehension.

PubMed

Marques, J Frederico; Canessa, Nicola; Cappa, Stefano

2009-06-01

The inquiry on the nature of truth in language comprehension has a long history of opposite perspectives. These perspectives either consider that there are qualitative differences in the processing of true and false statements, or that these processes are fundamentally the same and only differ in quantitative terms. The present study evaluated the processing nature of true and false statements in terms of patterns of brain activity using event-related functional-Magnetic-Resonance-Imaging (fMRI). We show that when true and false concept-feature statements are controlled for relation strength/ambiguity, their processing is associated to qualitatively different processes. Verifying true statements activates the left inferior parietal cortex and the caudate nucleus, a neural correlate compatible with an extended search and matching process for particular stored information. In contrast, verifying false statements activates the fronto-polar cortex and is compatible with a reasoning process of finding and evaluating a contradiction between the sentence information and stored knowledge.

Neural Differences in Bilingual Children's Arithmetic Processing Depending on Language of Instruction

ERIC Educational Resources Information Center

Mondt, Katrien; Struys, Esli; Van den Noort, Maurits; Baleriaux, Danielle; Metens, Thierry; Paquier, Philippe; Van de Craen, Piet; Bosch, Peggy; Denolin, Vincent

2011-01-01

Many children in bilingual regions follow lessons in a language at school (school-language) that they hardly ever speak at home or in other informal settings. What are the neural effects of this phenomenon? This functional magnetic resonance imaging (fMRI) study investigates the effects of using school-language on brain activity during a high…

Threat Based Risk Assessment for Enterprise Networks

DTIC Science & Technology

2016-02-15

served as the program chair of the Research in Attacks, Intrusions , and Defenses workshop; the Neural Information Processing Systems (NIPS) annual...Threat- Based Risk Assessment for Enterprise Networks Richard P. Lippmann and James F. Riordan Protecting enterprise networks requires...include aids for the hearing impaired, speech recognition, pattern classification, neural networks , and cybersecurity. He has taught three courses

Spatiotemporal Imaging of Glutamate-Induced Biophotonic Activities and Transmission in Neural Circuits

PubMed Central

Tang, Rendong; Dai, Jiapei

2014-01-01

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca2+. We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information. PMID:24454909

Spatiotemporal imaging of glutamate-induced biophotonic activities and transmission in neural circuits.

PubMed

Tang, Rendong; Dai, Jiapei

2014-01-01

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca(2+). We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information.

Real-time EEG-based detection of fatigue driving danger for accident prediction.

PubMed

Wang, Hong; Zhang, Chi; Shi, Tianwei; Wang, Fuwang; Ma, Shujun

2015-03-01

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Automatic Neural Processing of Disorder-Related Stimuli in Social Anxiety Disorder: Faces and More

PubMed Central

Schulz, Claudia; Mothes-Lasch, Martin; Straube, Thomas

2013-01-01

It has been proposed that social anxiety disorder (SAD) is associated with automatic information processing biases resulting in hypersensitivity to signals of social threat such as negative facial expressions. However, the nature and extent of automatic processes in SAD on the behavioral and neural level is not entirely clear yet. The present review summarizes neuroscientific findings on automatic processing of facial threat but also other disorder-related stimuli such as emotional prosody or negative words in SAD. We review initial evidence for automatic activation of the amygdala, insula, and sensory cortices as well as for automatic early electrophysiological components. However, findings vary depending on tasks, stimuli, and neuroscientific methods. Only few studies set out to examine automatic neural processes directly and systematic attempts are as yet lacking. We suggest that future studies should: (1) use different stimulus modalities, (2) examine different emotional expressions, (3) compare findings in SAD with other anxiety disorders, (4) use more sophisticated experimental designs to investigate features of automaticity systematically, and (5) combine different neuroscientific methods (such as functional neuroimaging and electrophysiology). Finally, the understanding of neural automatic processes could also provide hints for therapeutic approaches. PMID:23745116

A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and roadmap for future research

PubMed Central

Phillips, Mary L; Swartz, Holly A.

2014-01-01

Objective This critical review appraises neuroimaging findings in bipolar disorder in emotion processing, emotion regulation, and reward processing neural circuitry, to synthesize current knowledge of the neural underpinnings of bipolar disorder, and provide a neuroimaging research “roadmap” for future studies. Method We examined findings from all major studies in bipolar disorder that used fMRI, volumetric analyses, diffusion imaging, and resting state techniques, to inform current conceptual models of larger-scale neural circuitry abnormalities in bipolar disorder Results Bipolar disorder can be conceptualized in neural circuitry terms as parallel dysfunction in bilateral prefrontal cortical (especially ventrolateral prefrontal cortical)-hippocampal-amygdala emotion processing and emotion regulation neural circuitries, together with an “overactive” left-sided ventral striatal-ventrolateral and orbitofrontal cortical reward processing circuitry, that result in characteristic behavioral abnormalities associated with bipolar disorder: emotional lability, emotional dysregulation and heightened reward sensitivity. A potential structural basis for these functional abnormalities are gray matter decreases in prefrontal and temporal cortices, amygdala and hippocampus, and fractional anisotropy decreases in white matter tracts connecting prefrontal and subcortical regions. Conclusion Neuroimaging studies of bipolar disorder clearly demonstrate abnormalities in neural circuitries supporting emotion processing, emotion regulation and reward processing, although there are several limitations to these studies. Future neuroimaging research in bipolar disorder should include studies adopting dimensional approaches; larger studies examining neurodevelopmental trajectories in bipolar disorder and at-risk youth; multimodal neuroimaging studies using integrated systems approaches; and studies using pattern recognition approaches to provide clinically useful, individual-level data. Such studies will help identify clinically-relevant biomarkers to guide diagnosis and treatment decision-making for individuals with bipolar disorder. PMID:24626773

Temporal and Spatial Patterns of Neural Activity Associated with Information Selection in Open-ended Creativity.

PubMed

Zhou, Siyuan; Chen, Shi; Wang, Shuang; Zhao, Qingbai; Zhou, Zhijin; Lu, Chunming

2018-02-10

Novel information selection is a crucial process in creativity and was found to be associated with frontal-temporal functional connectivity in the right brain in closed-ended creativity. Since it has distinct cognitive processing from closed-ended creativity, the information selection in open-ended creativity might be underlain by different neural activity. To address this issue, a creative generation task of Chinese two-part allegorical sayings was adopted, and the trials were classified into novel and normal solutions according to participants' self-ratings. The results showed that (1) novel solutions induced a higher lower alpha power in the temporal area, which might be associated with the automatic, unconscious mental process of retrieving extensive semantic information, and (2) upper alpha power in both frontal and temporal areas and frontal-temporal alpha coherence were higher in novel solutions than in normal solutions, which might reflect the selective inhibition of semantic information. Furthermore, lower alpha power in the temporal area showed a reduction with time, while the frontal-temporal and temporal-temporal coherence in the upper alpha band appeared to increase from the early to the middle phase. These dynamic changes in neural activity might reflect the transformation from divergent thinking to convergent thinking in the creative progress. The advantage of the right brain in frontal-temporal connectivity was not found in the present work, which might result from the diversity of solutions in open-ended creativity. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural correlates of informational cascades: brain mechanisms of social influence on belief updating

PubMed Central

Klucharev, Vasily; Rieskamp, Jörg

2015-01-01

Informational cascades can occur when rationally acting individuals decide independently of their private information and follow the decisions of preceding decision-makers. In the process of updating beliefs, differences in the weighting of private and publicly available social information may modulate the probability that a cascade starts in a decisive way. By using functional magnetic resonance imaging, we examined neural activity while participants updated their beliefs based on the decisions of two fictitious stock market traders and their own private information, which led to a final decision of buying one of two stocks. Computational modeling of the behavioral data showed that a majority of participants overweighted private information. Overweighting was negatively correlated with the probability of starting an informational cascade in trials especially prone to conformity. Belief updating by private information was related to activity in the inferior frontal gyrus/anterior insula, the dorsolateral prefrontal cortex and the parietal cortex; the more a participant overweighted private information, the higher the activity in the inferior frontal gyrus/anterior insula and the lower in the parietal-temporal cortex. This study explores the neural correlates of overweighting of private information, which underlies the tendency to start an informational cascade. PMID:24974396

Role of temporal processing stages by inferior temporal neurons in facial recognition.

PubMed

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition.

Role of Temporal Processing Stages by Inferior Temporal Neurons in Facial Recognition

PubMed Central

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition. PMID:21734904

Neural mechanisms underlying the integration of situational information into attribution outcomes.

PubMed

Brosch, Tobias; Schiller, Daniela; Mojdehbakhsh, Rachel; Uleman, James S; Phelps, Elizabeth A

2013-08-01

When forming impressions and trying to figure out why other people behave the way they do, we should take into account not only dispositional factors (i.e., personality traits) but also situational constraints as potential causes for a behavior. However, in their attributions, people often ignore the importance of situational factors. To investigate the neural mechanisms underlying the integration of situational information into attributions, we decomposed the attribution process by separately presenting information about behaviors and about the situational circumstances in which they occur. After reading the information, participants judged whether dispositional or situational causes explained the behavior (attribution), and how much they liked the person described in the scenario (affective evaluation). The dorsolateral prefrontal cortex showed increased blood oxygenation-level-dependent activation during the encoding of situational information when the resulting attribution was situational, relative to when the attribution was dispositional, potentially reflecting a controlled process that integrates situational information into attributions. Interestingly, attributions were strongly linked to subsequent affective evaluations, with the dorsomedial prefrontal cortex emerging as potential substrate of the integration of attributions and affective evaluations. Our findings demonstrate how top-down control processes regulate impression formation when situational information is taken into account to understand others.

Neural mechanisms underlying the integration of situational information into attribution outcomes

PubMed Central

Brosch, Tobias; Schiller, Daniela; Mojdehbakhsh, Rachel; Uleman, James S.; Phelps, Elizabeth A.

2013-01-01

When forming impressions and trying to figure out why other people behave the way they do, we should take into account not only dispositional factors (i.e. personality traits) but also situational constraints as potential causes for a behavior. However, in their attributions, people often ignore the importance of situational factors. To investigate the neural mechanisms underlying the integration of situational information into attributions, we decomposed the attribution process by separately presenting information about behaviors and about the situational circumstances in which they occur. After reading the information, participants judged whether dispositional or situational causes explained the behavior (attribution), and how much they liked the person described in the scenario (affective evaluation). The dorsolateral prefrontal cortex showed increased blood oxygenation-level-dependent activation during the encoding of situational information when the resulting attribution was situational, relative to when the attribution was dispositional, potentially reflecting a controlled process that integrates situational information into attributions. Interestingly, attributions were strongly linked to subsequent affective evaluations, with the dorsomedial prefrontal cortex emerging as potential substrate of the integration of attributions and affective evaluations. Our findings demonstrate how top-down control processes regulate impression formation when situational information is taken into account to understand others. PMID:23446840

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Empirical study on neural network based predictive techniques for automatic number plate recognition

NASA Astrophysics Data System (ADS)

Shashidhara, M. S.; Indrakumar, S. S.

2011-10-01

The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

Computing with Neural Synchrony

PubMed Central

Brette, Romain

2012-01-01

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. PMID:22719243

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions.

PubMed

Lueken, Ulrike; Hahn, Tim

2016-01-01

The review provides an update of functional neuroimaging studies that identify neural processes underlying psychotherapy and predict outcomes following psychotherapeutic treatment in anxiety and depressive disorders. Following current developments in this field, studies were classified as 'mechanistic' or 'predictor' studies (i.e., informing neurobiological models about putative mechanisms versus aiming to provide predictive information). Mechanistic evidence points toward a dual-process model of psychotherapy in anxiety disorders with abnormally increased limbic activation being decreased, while prefrontal activity is increased. Partly overlapping findings are reported for depression, albeit with a stronger focus on prefrontal activation following treatment. No studies directly comparing neural pathways of psychotherapy between anxiety and depression were detected. Consensus is accumulating for an overarching role of the anterior cingulate cortex in modulating treatment response across disorders. When aiming to quantify clinical utility, the need for single-subject predictions is increasingly recognized and predictions based on machine learning approaches show high translational potential. Present findings encourage the search for predictors providing clinically meaningful information for single patients. However, independent validation as a crucial prerequisite for clinical use is still needed. Identifying nonresponders a priori creates the need for alternative treatment options that can be developed based on an improved understanding of those neural mechanisms underlying effective interventions.

The Role of Working Memory in the Probabilistic Inference of Future Sensory Events.

PubMed

Cashdollar, Nathan; Ruhnau, Philipp; Weisz, Nathan; Hasson, Uri

2017-05-01

The ability to represent the emerging regularity of sensory information from the external environment has been thought to allow one to probabilistically infer future sensory occurrences and thus optimize behavior. However, the underlying neural implementation of this process is still not comprehensively understood. Through a convergence of behavioral and neurophysiological evidence, we establish that the probabilistic inference of future events is critically linked to people's ability to maintain the recent past in working memory. Magnetoencephalography recordings demonstrated that when visual stimuli occurring over an extended time series had a greater statistical regularity, individuals with higher working-memory capacity (WMC) displayed enhanced slow-wave neural oscillations in the θ frequency band (4-8 Hz.) prior to, but not during stimulus appearance. This prestimulus neural activity was specifically linked to contexts where information could be anticipated and influenced the preferential sensory processing for this visual information after its appearance. A separate behavioral study demonstrated that this process intrinsically emerges during continuous perception and underpins a realistic advantage for efficient behavioral responses. In this way, WMC optimizes the anticipation of higher level semantic concepts expected to occur in the near future. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A cortical neural prosthesis for restoring and enhancing memory

NASA Astrophysics Data System (ADS)

Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

2011-08-01

A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome.

PubMed

Hellyer, Peter J; Scott, Gregory; Shanahan, Murray; Sharp, David J; Leech, Robert

2015-06-17

Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome. Copyright © 2015 the authors 0270-6474/15/359050-14$15.00/0.

Elements of an algorithm for optimizing a parameter-structural neural network

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2016-06-01

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

High level cognitive information processing in neural networks

NASA Technical Reports Server (NTRS)

Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

PubMed

Golosio, Bruno; Cangelosi, Angelo; Gamotina, Olesya; Masala, Giovanni Luca

2015-01-01

Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

Thalamic structures and associated cognitive functions: Relations with age and aging.

PubMed

Fama, Rosemary; Sullivan, Edith V

2015-07-01

The thalamus, with its cortical, subcortical, and cerebellar connections, is a critical node in networks supporting cognitive functions known to decline in normal aging, including component processes of memory and executive functions of attention and information processing. The macrostructure, microstructure, and neural connectivity of the thalamus changes across the adult lifespan. Structural and functional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) have demonstrated, regional thalamic volume shrinkage and microstructural degradation, with anterior regions generally more compromised than posterior regions. The integrity of selective thalamic nuclei and projections decline with advancing age, particularly those in thalamofrontal, thalamoparietal, and thalamolimbic networks. This review presents studies that assess the relations between age and aging and the structure, function, and connectivity of the thalamus and associated neural networks and focuses on their relations with processes of attention, speed of information processing, and working and episodic memory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Differences in the Neural Mechanisms of Selective Attention in Children from Different Socioeconomic Backgrounds: An Event-Related Brain Potential Study

ERIC Educational Resources Information Center

Stevens, Courtney; Lauinger, Brittni; Neville, Helen

2009-01-01

Previous research indicates that children from lower socioeconomic backgrounds show deficits in aspects of attention, including a reduced ability to filter irrelevant information and to suppress prepotent responses. However, less is known about the neural mechanisms of group differences in attention, which could reveal the stages of processing at…

Individual Differences in Neural Mechanisms of Selective Auditory Attention in Preschoolers from Lower Socioeconomic Status Backgrounds: An Event-Related Potentials Study

ERIC Educational Resources Information Center

Isbell, Elif; Wray, Amanda Hampton; Neville, Helen J.

2016-01-01

Selective attention, the ability to enhance the processing of particular input while suppressing the information from other concurrent sources, has been postulated to be a foundational skill for learning and academic achievement. The neural mechanisms of this foundational ability are both vulnerable and enhanceable in children from lower…

Investigation of Dynamic Algorithms for Pattern Recognition Identified in Cerebral Cortex

DTIC Science & Technology

1991-12-02

oscillatory and possibly chaotic activity forin the actual cortical substrate of the diverse sensory, motor, and cognitive operations now studied in...September Neural Information Processing Systems - Natural and Synthetic, Denver, Colo., November 1989 U.C. San Diego, Cognitive Science Dept...Baird. Biologically applied neural networks may foster the co-evolution of neurobiology and cognitive psychology. Brain and Behavioral Sciences, 37

Neural correlates of heart-focused interoception: a functional magnetic resonance imaging meta-analysis

PubMed Central

2016-01-01

Interoception is the ability to perceive one's internal body state including visceral sensations. Heart-focused interoception has received particular attention, in part due to a readily available task for behavioural assessment, but also due to accumulating evidence for a significant role in emotional experience, decision-making and clinical disorders such as anxiety and depression. Improved understanding of the underlying neural correlates is important to promote development of anatomical-functional models and suitable intervention strategies. In the present meta-analysis, nine studies reporting neural activity associated with interoceptive attentiveness (i.e. focused attention to a particular interoceptive signal for a given time interval) to one's heartbeat were submitted to a multilevel kernel density analysis. The findings corroborated an extended network associated with heart-focused interoceptive attentiveness including the posterior right and left insula, right claustrum, precentral gyrus and medial frontal gyrus. Right-hemispheric dominance emphasizes non-verbal information processing with the posterior insula presumably serving as the major gateway for cardioception. Prefrontal neural activity may reflect both top-down attention deployment and processing of feed-forward cardioceptive information, possibly orchestrated via the claustrum. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080975

Neural Processing of What and Who Information in Speech

ERIC Educational Resources Information Center

Chandrasekaran, Bharath; Chan, Alice H. D.; Wong, Patrick C. M.

2011-01-01

Human speech is composed of two types of information, related to content (lexical information, i.e., "what" is being said [e.g., words]) and to the speaker (indexical information, i.e., "who" is talking [e.g., voices]). The extent to which lexical versus indexical information is represented separately or integrally in the brain is unresolved. In…

Social cognitive conflict resolution: Contributions of domain general and domain specific neural systems

PubMed Central

Zaki, Jamil; Hennigan, Kelly; Weber, Jochen; Ochsner, Kevin N.

2010-01-01

Cognitive control mechanisms allow individuals to behave adaptively in the face of complex and sometimes conflicting information. While the neural bases of these control mechanisms have been examined in many contexts, almost no attention has been paid to their role in resolving conflicts between competing social cues, which is surprising, given that cognitive conflicts are part of many social interactions. Evidence about the neural processing of social information suggests that two systems—the mirror neuron system (MNS) and mental state attribution system (MSAS)—are specialized for processing nonverbal and contextual social cues, respectively. This could support a model of social cognitive conflict resolution in which competition between social cues would recruit domain-general cognitive control mechanisms, which in turn would bias processing towards the MNS or MSAS. Such biasing could also alter social behaviors, such as inferences made about the internal states of others. We tested this model by scanning participants using fMRI while they drew inferences about social targets' emotional states based on congruent or incongruent nonverbal and contextual social cues. Conflicts between social cues recruited the anterior cingulate and lateral prefrontal cortex, brain areas associated with domain-general control processes. This activation was accompanied by biasing of neural activity towards areas in the MNS or MSAS, which tracked, respectively, with perceivers' behavioral reliance on nonverbal or contextual cues when drawing inferences about targets' emotions. Together, these data provide evidence about both domain general and domain specific mechanisms involved in resolving social cognitive conflicts. PMID:20573895

Infant Joint Attention, Neural Networks and Social Cognition

PubMed Central

Mundy, Peter; Jarrold, William

2010-01-01

Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). This paper we argue that a neural networks approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one’s own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one’s own attention and the attention of other people. Infant practice with joint attention is both a consequence and organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances to depth of information processing and encoding beginning in the first year of life. We also propose that with development joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. PMID:20884172

What Do You Mean by That?! An Electrophysiological Study of Emotional and Attitudinal Prosody.

PubMed

Wickens, Steven; Perry, Conrad

2015-01-01

The use of prosody during verbal communication is pervasive in everyday language and whilst there is a wealth of research examining the prosodic processing of emotional information, much less is known about the prosodic processing of attitudinal information. The current study investigated the online neural processes underlying the prosodic processing of non-verbal emotional and attitudinal components of speech via the analysis of event-related brain potentials related to the processing of anger and sarcasm. To examine these, sentences with prosodic expectancy violations created by cross-splicing a prosodically neutral head ('he has') and a prosodically neutral, angry, or sarcastic ending (e.g., 'a serious face') were used. Task demands were also manipulated, with participants in one experiment performing prosodic classification and participants in another performing probe-verification. Overall, whilst minor differences were found across the tasks, the results suggest that angry and sarcastic prosodic expectancy violations follow a similar processing time-course underpinned by similar neural resources.

Uniformity and nonuniformity of neural activities correlated to different insight problem solving.

PubMed

Zhao, Q; Li, Y; Shang, X; Zhou, Z; Han, L

2014-06-13

Previous studies on the neural basis of insight reflected weak consistency except for the anterior cingulate cortex. The present work adopted the semantic and homophonic punny riddle to explore the uniformity and nonuniformity of neural activities correlated to different insight problem solving. Results showed that in the early period of insight solving, the semantic and homophonic punny riddles induced a common N350-500 over the central scalp. However, during -400 to 0 ms before the riddles were solved, the semantic punny riddles induced a positive event-related potential (ERP) deflection over the temporal cortex for retrieving the extensive semantic information, while the homophonic punny riddles induced a positive ERP deflection over the temporal cortex and a negative one in the left frontal cortex which might reflect the semantic and phonological information processing respectively. Our study indicated that different insight problem solving should have the same cognitive process of detecting cognitive conflicts, but have different ways to solve the conflicts. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural pathway in the right hemisphere underlies verbal insight problem solving.

PubMed

Zhao, Q; Zhou, Z; Xu, H; Fan, W; Han, L

2014-01-03

Verbal insight problem solving means to break mental sets, to select the novel semantic information and to form novel, task-related associations. Although previous studies have identified the brain regions associated with these key processes, the interaction among these regions during insight is still unclear. In the present study, we explored the functional connectivity between the key regions during solving Chinese 'chengyu' riddles by using event-related functional magnetic resonance imaging. Results showed that both insight and noninsight solutions activated the bilateral inferior frontal gyri, middle temporal gyri and hippocampi, and these regions constituted a frontal to temporal to hippocampal neural pathway. Compared with noninsight solution, insight solution had a stronger functional connectivity between the inferior frontal gyrus and middle temporal gyrus in the right hemisphere. Our study reveals the neural pathway of information processing during verbal insight problem solving, and supports the right-hemisphere advantage theory of insight. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Information-theoretic decomposition of embodied and situated systems.

PubMed

Da Rold, Federico

2018-07-01

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

The role of trauma-related distractors on neural systems for working memory and emotion processing in posttraumatic stress disorder

PubMed Central

Morey, Rajendra A.; Dolcos, Florin; Petty, Christopher M.; Cooper, Debra A.; Hayes, Jasmeet Pannu; LaBar, Kevin S.; McCarthy, Gregory

2009-01-01

The relevance of emotional stimuli to threat and survival confers a privileged role in their processing. In PTSD, the ability of trauma-related information to divert attention is especially pronounced. Information unrelated to the trauma may also be highly distracting when it shares perceptual features with trauma material. Our goal was to study how trauma-related environmental cues modulate working memory networks in PTSD. We examined neural activity in participants performing a visual working memory task while distracted by task-irrelevant trauma and non-trauma material. Recent post-9/11 veterans were divided into a PTSD group (n = 22) and a trauma-exposed control group (n = 20) based on the Davidson trauma scale. Using fMRI, we measured hemodynamic change in response to emotional (trauma-related) and neutral distraction presented during the active maintenance period of a delayed-response working memory task. The goal was to examine differences in functional networks associated with working memory (dorsolateral prefrontal cortex and lateral parietal cortex) and emotion processing (amygdala, ventrolateral prefrontal cortex, and fusiform gyrus). The PTSD group showed markedly different neural activity compared to the trauma-exposed control group in response to task-irrelevant visual distractors. Enhanced activity in ventral emotion processing regions was associated with trauma distractors in the PTSD group, whereas activity in brain regions associated with working memory and attention regions was disrupted by distractor stimuli independent of trauma content. Neural evidence for the impact of distraction on working memory is consistent with PTSD symptoms of hypervigilance and general distractibility during goal-directed cognitive processing. PMID:19091328

A Neuroeconomics Analysis of Investment Process with Money Flow Information: The Error-Related Negativity

PubMed Central

Wang, Cuicui; Vieito, João Paulo; Ma, Qingguo

2015-01-01

This investigation is among the first ones to analyze the neural basis of an investment process with money flow information of financial market, using a simplified task where volunteers had to choose to buy or not to buy stocks based on the display of positive or negative money flow information. After choosing “to buy” or “not to buy,” participants were presented with feedback. At the same time, event-related potentials (ERPs) were used to record investor's brain activity and capture the event-related negativity (ERN) and feedback-related negativity (FRN) components. The results of ERN suggested that there might be a higher risk and more conflict when buying stocks with negative net money flow information than positive net money flow information, and the inverse was also true for the “not to buy” stocks option. The FRN component evoked by the bad outcome of a decision was more negative than that by the good outcome, which reflected the difference between the values of the actual and expected outcome. From the research, we could further understand how investors perceived money flow information of financial market and the neural cognitive effect in investment process. PMID:26557139

A Neuroeconomics Analysis of Investment Process with Money Flow Information: The Error-Related Negativity.

PubMed

Wang, Cuicui; Vieito, João Paulo; Ma, Qingguo

2015-01-01

This investigation is among the first ones to analyze the neural basis of an investment process with money flow information of financial market, using a simplified task where volunteers had to choose to buy or not to buy stocks based on the display of positive or negative money flow information. After choosing "to buy" or "not to buy," participants were presented with feedback. At the same time, event-related potentials (ERPs) were used to record investor's brain activity and capture the event-related negativity (ERN) and feedback-related negativity (FRN) components. The results of ERN suggested that there might be a higher risk and more conflict when buying stocks with negative net money flow information than positive net money flow information, and the inverse was also true for the "not to buy" stocks option. The FRN component evoked by the bad outcome of a decision was more negative than that by the good outcome, which reflected the difference between the values of the actual and expected outcome. From the research, we could further understand how investors perceived money flow information of financial market and the neural cognitive effect in investment process.

Multisite two-photon imaging of neurons on multielectrode arrays

NASA Astrophysics Data System (ADS)

Potter, Steve M.; Lukina, Natalia; Longmuir, Kenneth J.; Wu, Yan

2001-04-01

We wish to understand how neural systems store, recall, and process information. We are using cultured networks of cortical neurons grown on microelectrode arrays as a model system for studying the emergent properties of ensembles of living neurons. We have developed a 2-way communication interface between the cultured network and a computer- generated animal, the Neurally Controlled Animat. Neural activity is used to control the behavior of the Animat, and 2- photon time-lapse imaging is carried out in order to observe the morphological changes that might underlie changes in neural processing. The 2-photon microscope is ideal for repeated imaging over hours or days, with submicron resolution and little photodamage. We have designed a computer-controlled microscope stage that allows imaging several locations in sequence, in order to collect more image data. For the latest progress, see: http://www.caltech.edu/~pinelab/PotterGroup.htm.

Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 1. Theory Track Neural and cognitive Sciences Track

DTIC Science & Technology

1990-11-30

signaI flow , xi. The learning" of such statistics could result from synaptic modification rules similar to those known to exist in the brain 7 " 1 0,1 1...in figure 1 had been established. If the series are appro\\imat.ed by Gaussian process. the information flow from X to Y can be expressed by the...Based on this model. the information flow in different direction were calculated by using eq.(1). RESULTS Figures 2 illustrates the information flow

Neuroanatomic organization of sound memory in humans.

PubMed

Kraut, Michael A; Pitcock, Jeffery A; Calhoun, Vince; Li, Juan; Freeman, Thomas; Hart, John

2006-11-01

The neural interface between sensory perception and memory is a central issue in neuroscience, particularly initial memory organization following perceptual analyses. We used functional magnetic resonance imaging to identify anatomic regions extracting initial auditory semantic memory information related to environmental sounds. Two distinct anatomic foci were detected in the right superior temporal gyrus when subjects identified sounds representing either animals or threatening items. Threatening animal stimuli elicited signal changes in both foci, suggesting a distributed neural representation. Our results demonstrate both category- and feature-specific responses to nonverbal sounds in early stages of extracting semantic memory information from these sounds. This organization allows for these category-feature detection nodes to extract early, semantic memory information for efficient processing of transient sound stimuli. Neural regions selective for threatening sounds are similar to those of nonhuman primates, demonstrating semantic memory organization for basic biological/survival primitives are present across species.

Effects of Oxytocin and Vasopressin on Preferential Brain Responses to Negative Social Feedback.

PubMed

Gozzi, Marta; Dashow, Erica M; Thurm, Audrey; Swedo, Susan E; Zink, Caroline F

2017-06-01

Receiving negative social feedback can be detrimental to emotional, cognitive, and physical well-being, and fear of negative social feedback is a prominent feature of mental illnesses that involve social anxiety. A large body of evidence has implicated the neuropeptides oxytocin and vasopressin in the modulation of human neural activity underlying social cognition, including negative emotion processing; however, the influence of oxytocin and vasopressin on neural activity elicited during negative social evaluation remains unknown. Here 21 healthy men underwent functional magnetic resonance imaging in a double-blind, placebo-controlled, crossover design to determine how intranasally administered oxytocin and vasopressin modulated neural activity when receiving negative feedback on task performance from a study investigator. We found that under placebo, a preferential response to negative social feedback compared with positive social feedback was evoked in brain regions putatively involved in theory of mind (temporoparietal junction), pain processing (anterior insula and supplementary motor area), and identification of emotionally important visual cues in social perception (right fusiform). These activations weakened with oxytocin and vasopressin administration such that neural responses to receiving negative social feedback were not significantly greater than positive social feedback. Our results show effects of both oxytocin and vasopressin on the brain network involved in negative social feedback, informing the possible use of a pharmacological approach targeting these regions in multiple disorders with impairments in social information processing.

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644

Identifying Emotions on the Basis of Neural Activation

PubMed Central

Kassam, Karim S.; Markey, Amanda R.; Cherkassky, Vladimir L.; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing. PMID:23840392

Identifying Emotions on the Basis of Neural Activation.

PubMed

Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Neural Activity When People Solve Verbal Problems with Insight

PubMed Central

Bowden, Edward M; Haberman, Jason; Frymiare, Jennifer L; Arambel-Liu, Stella; Greenblatt, Richard; Reber, Paul J

2004-01-01

People sometimes solve problems with a unique process called insight, accompanied by an “Aha!” experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them. PMID:15094802

Testing of information condensation in a model reverberating spiking neural network.

PubMed

Vidybida, Alexander

2011-06-01

Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

PubMed

Gohel, Bakul; Lee, Peter; Jeong, Yong

2016-08-01

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Pencil-and-Paper Neural Networks: An Undergraduate Laboratory Exercise in Computational Neuroscience

PubMed Central

Crisp, Kevin M.; Sutter, Ellen N.; Westerberg, Jacob A.

2015-01-01

Although it has been more than 70 years since McCulloch and Pitts published their seminal work on artificial neural networks, such models remain primarily in the domain of computer science departments in undergraduate education. This is unfortunate, as simple network models offer undergraduate students a much-needed bridge between cellular neurobiology and processes governing thought and behavior. Here, we present a very simple laboratory exercise in which students constructed, trained and tested artificial neural networks by hand on paper. They explored a variety of concepts, including pattern recognition, pattern completion, noise elimination and stimulus ambiguity. Learning gains were evident in changes in the use of language when writing about information processing in the brain. PMID:26557791

The hippocampus and exploration: dynamically evolving behavior and neural representations

PubMed Central

Johnson, Adam; Varberg, Zachary; Benhardus, James; Maahs, Anthony; Schrater, Paul

2012-01-01

We develop a normative statistical approach to exploratory behavior called information foraging. Information foraging highlights the specific processes that contribute to active, rather than passive, exploration and learning. We hypothesize that the hippocampus plays a critical role in active exploration through directed information foraging by supporting a set of processes that allow an individual to determine where to sample. By examining these processes, we show how information directed information foraging provides a formal theoretical explanation for the common hippocampal substrates of constructive memory, vicarious trial and error behavior, schema-based facilitation of memory performance, and memory consolidation. PMID:22848196

A new perspective on the perceptual selectivity of attention under load.

PubMed

Giesbrecht, Barry; Sy, Jocelyn; Bundesen, Claus; Kyllingsbaek, Søren

2014-05-01

The human attention system helps us cope with a complex environment by supporting the selective processing of information relevant to our current goals. Understanding the perceptual, cognitive, and neural mechanisms that mediate selective attention is a core issue in cognitive neuroscience. One prominent model of selective attention, known as load theory, offers an account of how task demands determine when information is selected and an account of the efficiency of the selection process. However, load theory has several critical weaknesses that suggest that it is time for a new perspective. Here we review the strengths and weaknesses of load theory and offer an alternative biologically plausible computational account that is based on the neural theory of visual attention. We argue that this new perspective provides a detailed computational account of how bottom-up and top-down information is integrated to provide efficient attentional selection and allocation of perceptual processing resources. © 2014 New York Academy of Sciences.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)-regions critical for sensory associations-of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired "top-down" knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Behavior in Oblivion: The Neurobiology of Subliminal Priming

PubMed Central

Jacobs, Christianne; Sack, Alexander T.

2012-01-01

Subliminal priming refers to behavioral modulation by an unconscious stimulus, and can thus be regarded as a form of unconscious visual processing. Theories on recurrent processing have suggested that the neural correlate of consciousness (NCC) comprises of the non-hierarchical transfer of stimulus-related information. According to these models, the neural correlate of subliminal priming (NCSP) corresponds to the visual processing within the feedforward sweep. Research from cognitive neuroscience on these two concepts and the relationship between them is discussed here. Evidence favoring the necessity of recurrent connectivity for visual awareness is accumulating, although some questions, such as the need for global versus local recurrent processing, are not clarified yet. However, this is not to say that recurrent processing is sufficient for consciousness, as a neural definition of consciousness in terms of recurrent connectivity would imply. We argue that the limited interest cognitive neuroscience currently has for the NCSP is undeserved, because the discovery of the NCSP can give insight into why people do (and do not) express certain behavior. PMID:24962773

Population coding and decoding in a neural field: a computational study.

PubMed

Wu, Si; Amari, Shun-Ichi; Nakahara, Hiroyuki

2002-05-01

This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aimone, James Bradley; Betty, Rita

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis, thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

PubMed

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-11-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Song, Yongduan; Xue, Fangzheng

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than themore » SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.« less

The neural signature of emotional memories in serial crimes.

PubMed

Chassy, Philippe

2017-10-01

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Flexing dual-systems models: How variable cognitive control in children informs our understanding of risk-taking across development.

PubMed

Li, Rosa

2017-10-01

Prevailing models of the development of decision-making propose that peak risk-taking occurs in adolescence due to a neural imbalance between two processes: gradual, linearly developing cognitive control and rapid, non-linearly developing reward-processing. Though many studies have found neural evidence supporting this dual-systems imbalance model, its behavioral predictions have been surprisingly difficult to document. Most laboratory studies have not found adolescents to exhibit greater risk-taking than children, and public health data show everyday risk-taking to peak in late adolescence/early adulthood. Moreover, when adolescents are provided detailed information about decision options and consequences, they evince similar behavior to adults. Such findings point to a critical feature of the development of decision-making that is missed by imbalance models. Specifically, the engagement of cognitive control is context dependent, such that cognitive control and therefore advantageous decision-making increases when available information is high and decreases when available information is low. Furthermore, the context dependence of cognitive control varies across development, such that increased information availability benefits children more than adolescents, who benefit more than adults. This review advances a flexible dual-systems model that is only imbalanced under certain conditions; explains disparities between neural, behavioral, and public health findings; and provides testable hypotheses for future research. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Toward Model Building for Visual Aesthetic Perception

PubMed Central

Lughofer, Edwin; Zeng, Xianyi

2017-01-01

Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation. PMID:29270194

An information theory account of cognitive control.

PubMed

Fan, Jin

2014-01-01

Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.

Neural and Behavioral Evidence for an Online Resetting Process in Visual Working Memory.

PubMed

Balaban, Halely; Luria, Roy

2017-02-01

Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence. Visual working memory (VWM) maintains task-relevant information in an online state. Previous studies showed that VWM representations are accessed and modified after changes in the environment. Here, we show that this updating process critically depends on an ongoing mapping between the representations and the objects in the environment. When this mapping breaks, VWM cannot access the old representations and instead resets. The novel resetting process that we introduce removes the existing representations instead of modifying them and this process is accompanied by a unique neural marker. During the resetting process, VWM was blind to salient changes in the object's shape. The resetting process highlights the flexibility of our cognitive system in handling the dynamic environment by abruptly abandoning irrelevant schemas. Copyright © 2017 the authors 0270-6474/17/371225-15$15.00/0.

Electrical and Optical Activation of Mesoscale Neural Circuits with Implications for Coding.

PubMed

Millard, Daniel C; Whitmire, Clarissa J; Gollnick, Clare A; Rozell, Christopher J; Stanley, Garrett B

2015-11-25

Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery of circuit function and for engineered approaches to alleviate various disorders of the nervous system. However, evidence suggests that neural activity generated by artificial stimuli differs dramatically from normal circuit function, in terms of both the local neuronal population activity at the site of activation and the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. Here, we used voltage-sensitive dye imaging of primary somatosensory cortex in the anesthetized rat in response to deflections of the facial vibrissae and electrical or optogenetic stimulation of thalamic neurons that project directly to the somatosensory cortex. Although the different inputs produced responses that were similar in terms of the average cortical activation, the variability of the cortical response was strikingly different for artificial versus sensory inputs. Furthermore, electrical microstimulation resulted in highly unnatural spatial activation of cortex, whereas optical input resulted in spatial cortical activation that was similar to that induced by sensory inputs. A thalamocortical network model suggested that observed differences could be explained by differences in the way in which artificial and natural inputs modulate the magnitude and synchrony of population activity. Finally, the variability structure in the response for each case strongly influenced the optimal inputs for driving the pathway from the perspective of an ideal observer of cortical activation when considered in the context of information transmission. Artificial activation of neural circuitry through electrical microstimulation and optogenetic techniques is important for both scientific discovery and clinical translation. However, neural activity generated by these artificial means differs dramatically from normal circuit function, both locally and in the propagation to downstream brain structures. The precise nature of these differences and the implications for information processing remain unknown. The significance of this work is in quantifying the differences, elucidating likely mechanisms underlying the differences, and determining the implications for information processing. Copyright © 2015 the authors 0270-6474/15/3515702-14$15.00/0.

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Technical Reports Server (NTRS)

Hsu, Ken-Yuh (Editor); Liu, Hua-Kuang (Editor)

1992-01-01

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Astrophysics Data System (ADS)

Hsu, Ken-Yuh; Liu, Hua-Kuang

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

A review on cluster estimation methods and their application to neural spike data.

PubMed

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

A review on cluster estimation methods and their application to neural spike data

NASA Astrophysics Data System (ADS)

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons—‘spike sorting’—is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

A model for integrating elementary neural functions into delayed-response behavior.

PubMed

Gisiger, Thomas; Kerszberg, Michel

2006-04-01

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

A longitudinal study investigating neural processing of speech envelope modulation rates in children with (a family risk for) dyslexia.

PubMed

De Vos, Astrid; Vanvooren, Sophie; Vanderauwera, Jolijn; Ghesquière, Pol; Wouters, Jan

2017-08-01

Recent evidence suggests that a fundamental deficit in the synchronization of neural oscillations to temporal information in speech may underlie phonological processing problems in dyslexia. Since previous studies were performed cross-sectionally in school-aged children or adults, developmental aspects of neural auditory processing in relation to reading acquisition and dyslexia remain to be investigated. The present longitudinal study followed 68 children during development from pre-reader (5 years old) to beginning reader (7 years old) and more advanced reader (9 years old). Thirty-six children had a family risk for dyslexia and 14 children eventually developed dyslexia. EEG recordings of auditory steady-state responses to 4 and 20 Hz modulations, corresponding to syllable and phoneme rates, were collected at each point in time. Our results demonstrate an increase in neural synchronization to phoneme-rate modulations around the onset of reading acquisition. This effect was negatively correlated with later reading and phonological skills, indicating that children who exhibit the largest increase in neural synchronization to phoneme rates, develop the poorest reading and phonological skills. Accordingly, neural synchronization to phoneme-rate modulations was found to be significantly higher in beginning and more advanced readers with dyslexia. We found no developmental effects regarding neural synchronization to syllable rates, nor any effects of a family risk for dyslexia. Altogether, our findings suggest that the onset of reading instruction coincides with an increase in neural responsiveness to phoneme-rate modulations, and that the extent of this increase is related to (the outcome of) reading development. Hereby, dyslexic children persistently demonstrate atypically high neural synchronization to phoneme rates from the beginning of reading acquisition onwards. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sentential Negation Might Share Neurophysiological Mechanisms with Action Inhibition. Evidence from Frontal Theta Rhythm.

PubMed

de Vega, Manuel; Morera, Yurena; León, Inmaculada; Beltrán, David; Casado, Pilar; Martín-Loeches, Manuel

2016-06-01

According to the literature, negations such as "not" or "don't" reduce the accessibility in memory of the concepts under their scope. Moreover, negations applied to action contents (e.g., "don't write the letter") impede the activation of motor processes in the brain, inducing "disembodied" representations. These facts provide important information on the behavioral and neural consequences of negations. However, how negations themselves are processed in the brain is still poorly understood. In two electrophysiological experiments, we explored whether sentential negation shares neural mechanisms with action monitoring or inhibition. Human participants read action-related sentences in affirmative or negative form ("now you will cut the bread" vs "now you will not cut the bread") while performing a simultaneous Go/NoGo task. The analysis of the EEG rhythms revealed that theta oscillations were significantly reduced for NoGo trials in the context of negative sentences compared with affirmative sentences. Given the fact that theta oscillations are often considered as neural markers of response inhibition processes, their modulation by negative sentences strongly suggests that negation uses neural resources of response inhibition. We propose a new approach that views the syntactic operator of negation as relying on the neural machinery of high-order action-monitoring processes. Previous studies have shown that linguistic negation reduces the accessibility of the negated concepts and suppresses the activation of specific brain regions that operate in affirmative statements. Although these studies focus on the consequences of negation on cognitive and neural processes, the proper neural mechanisms of negation have not yet been explored. In the present EEG study, we tested the hypothesis that negation uses the neural network of action inhibition. Using a Go/NoGo task embedded in a sentence comprehension task, we found that negation in the context of NoGo trials modulates frontal theta rhythm, which is usually considered a signature of action inhibition and control mechanisms. Copyright © 2016 the authors 0270-6474/16/366002-09$15.00/0.

Holography and optical information processing; Proceedings of the Soviet-Chinese Joint Seminar, Bishkek, Kyrgyzstan, Sept. 21-26, 1991

NASA Astrophysics Data System (ADS)

Mikaelian, Andrei L.

Attention is given to data storage, devices, architectures, and implementations of optical memory and neural networks; holographic optical elements and computer-generated holograms; holographic display and materials; systems, pattern recognition, interferometry, and applications in optical information processing; and special measurements and devices. Topics discussed include optical immersion as a new way to increase information recording density, systems for data reading from optical disks on the basis of diffractive lenses, a new real-time optical associative memory system, an optical pattern recognition system based on a WTA model of neural networks, phase diffraction grating for the integral transforms of coherent light fields, holographic recording with operated sensitivity and stability in chalcogenide glass layers, a compact optical logic processor, a hybrid optical system for computing invariant moments of images, optical fiber holographic inteferometry, and image transmission through random media in single pass via optical phase conjugation.

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration.

PubMed

Zhao, Zi-Fang; Li, Xue-Zhu; Wan, You

2017-12-01

The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.

Is there "neural efficiency" during the processing of visuo-spatial information in male humans? An EEG study.

PubMed

Capotosto, Paolo; Perrucci, M Gianni; Brunetti, Marcella; Del Gratta, Cosimo; Doppelmayr, Michael; Grabner, Roland H; Klimesch, Wolfgang; Neubauer, Aljoscha; Neuper, Christa; Pfurtscheller, Gert; Romani, Gian Luca; Babiloni, Claudio

2009-12-28

More intelligent persons (high IQ) typically present a higher cortical activity during tasks requiring the encoding of visuo-spatial information, namely higher alpha (about 10 Hz) event-related desynchronization (ERD; Doppelmayr et al., 2005). The opposite is true ("neural efficiency") during the retrieval of the encoded information, as revealed by both lower alpha ERD and/or lower theta (about 5 Hz) event-related synchronization (ERS; Grabner et al., 2004). To reconcile these contrasting results, here we evaluated the working hypothesis that more intelligent male subjects are characterized by a high cortical activity during the encoding phase. This deep encoding would explain the relatively low cortical activity for the retrieval of the encoded information. To test this hypothesis, electroencephalographic (EEG) data were recorded in 22 healthy young male volunteers during visuo-spatial information processing (encoding) and short-term retrieval of the encoded information. Cortical activity was indexed by theta ERS and alpha ERD. It was found that the higher the subjects' total IQ, the stronger the frontal theta ERS during the encoding task. Furthermore, the higher the subjects' total IQ, the lower the frontal high-frequency alpha ERD (about 10-12 Hz) during the retrieval task. This was not true for parietal counterpart of these EEG rhythms. These results reconcile previous contrasting evidence confirming that more intelligent persons do not ever show event-related cortical responses compatible with "neural efficiency" hypothesis. Rather, their cortical activity would depend on flexible and task-adapting features of frontal activation.

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

NASA Technical Reports Server (NTRS)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

A computational model of conditioning inspired by Drosophila olfactory system.

PubMed

Faghihi, Faramarz; Moustafa, Ahmed A; Heinrich, Ralf; Wörgötter, Florentin

2017-03-01

Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots. Copyright © 2016 Elsevier Ltd. All rights reserved.

NPY2-receptor variation modulates iconic memory processes.

PubMed

Arning, Larissa; Stock, Ann-Kathrin; Kloster, Eugen; Epplen, Jörg T; Beste, Christian

2014-08-01

Sensory memory systems are modality-specific buffers that comprise information about external stimuli, which represent the earliest stage of information processing. While these systems have been the subject of cognitive neuroscience research for decades, little is known about the neurobiological basis of sensory memory. However, accumulating evidence suggests that the glutamatergic system and systems influencing glutamatergic neural transmission are important. In the current study we examine if functional promoter variations in neuropeptide Y (NPY) and its receptor gene NPY2R affect iconic memory processes using a partial report paradigm. We found that iconic memory decayed much faster in individuals carrying the rare promoter NPY2R G allele which is associated with increased expression of the Y2 receptor. Possibly this effect is due to altered presynaptic inhibition of glutamate release, known to be modulated by Y2 receptors. Altogether, our results provide evidence that the functionally relevant single nucleotide polymorphism (SNP) in the NPY2R promoter gene affect circumscribed processes of early sensory processing, i.e. only the stability of information in sensory memory buffers. This leads us to suggest that especially the stability of information in sensory memory buffers depends on glutamatergic neural transmission and factors modulating glutamatergic turnover. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

Analysis of fault-tolerant neurocontrol architectures

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1992-01-01

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters.

ID card number detection algorithm based on convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

Molecular Electronic Devices Based On Electrooptical Behavior Of Heme-Like Molecules

NASA Astrophysics Data System (ADS)

Simic-Glavaski, B.

1986-02-01

This paper discusses application of the electrically modulated and unusually strong Raman emitted light produced by an adsorbed monolayer of phthalocyanine molecules on silver electrode or silver bromide substrates and on neural membranes. The analysis of electronic energy levels in semiconducting silver bromide and the adsorbed phthalocyanine molecules suggests a lasing mechanism as a possible origin of the high enhancement factor in surface enhanced Raman scattering. Electrically modulated Raman scattering may be used as a carrier of information which is drawn fran the fast intramolecular electron transfer aN,the multiplicity of quantum wells in phthalocyanine molecules. Fast switching times on the order of 10-13 seconds have been measured at room temperature. Multilevel and multioutput optical signals have also been obtained fran such an electrically modulated adsorbed monolayer of phthalocyanine molecules which can be precisely addressed and interrogated. This may be of practical use to develop Nlecular electronic devices with high density memory and fast parallel processing systems with a typical 1020 gate Hz/cm2 capacity at room temperature for use in optical computers. The paper also discusses the electrooptical modulation of Raman signals obtained from adsorbed bio-compatible phthalocyanine molecules on nerve membranes. This optical probe of neural systems can be used in studies of complex information processing in neural nets and provides a possible method for interfacing natural and man-made information processing devices.

A Cognitive Framework for Understanding and Improving Interference Resolution in the Brain

PubMed Central

Mishra, Jyoti; Anguera, Joaquin A.; Ziegler, David A.; Gazzaley, Adam

2014-01-01

All of us are familiar with the negative impact of interference on achieving our task goals. We are referring to interference by information, which either impinges on our senses from an external environmental source or is internally generated by our thoughts. Informed by more than a decade of research on the cognitive and neural processing of interference, we have developed a framework for understanding how interference impacts our neural systems and especially how it is regulated and suppressed during efficient on-task performance. Importantly, externally and internally generated interferences have distinct neural signatures, and further, distinct neural processing emerges depending on whether individuals must ignore and suppress the interference, as for distractions, or engage with them in a secondary task, as during multitasking. Here, we elaborate on this cognitive framework and how it changes throughout the human lifespan, focusing mostly on research evidence from younger adults and comparing these findings to data from older adults, children, and cognitively impaired populations. With insights gleaned from our growing understanding, we then describe three novel translational efforts in our lab directed at improving distinct aspects of interference resolution using cognitive training. Critically, these training approaches were specifically developed to target improved interference resolution based on neuroplasticity principles and have shown much success in randomized controlled first version evaluations in healthy aging. Our results show not only on-task training improvements but also robust generalization of benefit to other cognitive control abilities. This research showcases how an in-depth understanding of neural mechanisms can then inform the development of effective deficit-targeted interventions, which can in turn benefit both healthy and cognitively impaired populations. PMID:24309262

Integration and segregation in auditory streaming

NASA Astrophysics Data System (ADS)

Almonte, Felix; Jirsa, Viktor K.; Large, Edward W.; Tuller, Betty

2005-12-01

We aim to capture the perceptual dynamics of auditory streaming using a neurally inspired model of auditory processing. Traditional approaches view streaming as a competition of streams, realized within a tonotopically organized neural network. In contrast, we view streaming to be a dynamic integration process which resides at locations other than the sensory specific neural subsystems. This process finds its realization in the synchronization of neural ensembles or in the existence of informational convergence zones. Our approach uses two interacting dynamical systems, in which the first system responds to incoming acoustic stimuli and transforms them into a spatiotemporal neural field dynamics. The second system is a classification system coupled to the neural field and evolves to a stationary state. These states are identified with a single perceptual stream or multiple streams. Several results in human perception are modelled including temporal coherence and fission boundaries [L.P.A.S. van Noorden, Temporal coherence in the perception of tone sequences, Ph.D. Thesis, Eindhoven University of Technology, The Netherlands, 1975], and crossing of motions [A.S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound, MIT Press, 1990]. Our model predicts phenomena such as the existence of two streams with the same pitch, which cannot be explained by the traditional stream competition models. An experimental study is performed to provide proof of existence of this phenomenon. The model elucidates possible mechanisms that may underlie perceptual phenomena.

Concentration: The Neural Underpinnings of How Cognitive Load Shields Against Distraction.

PubMed

Sörqvist, Patrik; Dahlström, Örjan; Karlsson, Thomas; Rönnberg, Jerker

2016-01-01

Whether cognitive load-and other aspects of task difficulty-increases or decreases distractibility is subject of much debate in contemporary psychology. One camp argues that cognitive load usurps executive resources, which otherwise could be used for attentional control, and therefore cognitive load increases distraction. The other camp argues that cognitive load demands high levels of concentration (focal-task engagement), which suppresses peripheral processing and therefore decreases distraction. In this article, we employed an functional magnetic resonance imaging (fMRI) protocol to explore whether higher cognitive load in a visually-presented task suppresses task-irrelevant auditory processing in cortical and subcortical areas. The results show that selectively attending to an auditory stimulus facilitates its neural processing in the auditory cortex, and switching the locus-of-attention to the visual modality decreases the neural response in the auditory cortex. When the cognitive load of the task presented in the visual modality increases, the neural response to the auditory stimulus is further suppressed, along with increased activity in networks related to effortful attention. Taken together, the results suggest that higher cognitive load decreases peripheral processing of task-irrelevant information-which decreases distractibility-as a side effect of the increased activity in a focused-attention network.

A dynamic neural field model of temporal order judgments.

PubMed

Hecht, Lauren N; Spencer, John P; Vecera, Shaun P

2015-12-01

Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

PubMed Central

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces. PMID:29046625

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation.

PubMed

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

A Symbolic Model of the Nonconscious Acquisition of Information.

ERIC Educational Resources Information Center

Ling, Charles X.; Marinov, Marin

1994-01-01

Challenges Smolensky's theory that human intuitive/nonconscious cognitive processes can only be accurately explained in terms of subsymbolic computations in artificial neural networks. Symbolic learning models of two cognitive tasks involving nonconscious acquisition of information are presented: learning production rules and artificial finite…

Experimental Machine Learning of Quantum States

NASA Astrophysics Data System (ADS)

Gao, Jun; Qiao, Lu-Feng; Jiao, Zhi-Qiang; Ma, Yue-Chi; Hu, Cheng-Qiu; Ren, Ruo-Jing; Yang, Ai-Lin; Tang, Hao; Yung, Man-Hong; Jin, Xian-Min

2018-06-01

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

Cognitive processing in the primary visual cortex: from perception to memory.

PubMed

Supèr, Hans

2002-01-01

The primary visual cortex is the first cortical area of the visual system that receives information from the external visual world. Based on the receptive field characteristics of the neurons in this area, it has been assumed that the primary visual cortex is a pure sensory area extracting basic elements of the visual scene. This information is then subsequently further processed upstream in the higher-order visual areas and provides us with perception and storage of the visual environment. However, recent findings show that such neural implementations are observed in the primary visual cortex. These neural correlates are expressed by the modulated activity of the late response of a neuron to a stimulus, and most likely depend on recurrent interactions between several areas of the visual system. This favors the concept of a distributed nature of visual processing in perceptual organization.

Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving Mammals

PubMed Central

Jercog, Pablo; Rogerson, Thomas; Schnitzer, Mark J.

2016-01-01

During long-term memory formation, cellular and molecular processes reshape how individual neurons respond to specific patterns of synaptic input. It remains poorly understood how such changes impact information processing across networks of mammalian neurons. To observe how networks encode, store, and retrieve information, neuroscientists must track the dynamics of large ensembles of individual cells in behaving animals, over timescales commensurate with long-term memory. Fluorescence Ca2+-imaging techniques can monitor hundreds of neurons in behaving mice, opening exciting avenues for studies of learning and memory at the network level. Genetically encoded Ca2+ indicators allow neurons to be targeted by genetic type or connectivity. Chronic animal preparations permit repeated imaging of neural Ca2+ dynamics over multiple weeks. Together, these capabilities should enable unprecedented analyses of how ensemble neural codes evolve throughout memory processing and provide new insights into how memories are organized in the brain. PMID:27048190

Feature-selective Attention in Frontoparietal Cortex: Multivoxel Codes Adjust to Prioritize Task-relevant Information.

PubMed

Jackson, Jade; Rich, Anina N; Williams, Mark A; Woolgar, Alexandra

2017-02-01

Human cognition is characterized by astounding flexibility, enabling us to select appropriate information according to the objectives of our current task. A circuit of frontal and parietal brain regions, often referred to as the frontoparietal attention network or multiple-demand (MD) regions, are believed to play a fundamental role in this flexibility. There is evidence that these regions dynamically adjust their responses to selectively process information that is currently relevant for behavior, as proposed by the "adaptive coding hypothesis" [Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820-829, 2001]. Could this provide a neural mechanism for feature-selective attention, the process by which we preferentially process one feature of a stimulus over another? We used multivariate pattern analysis of fMRI data during a perceptually challenging categorization task to investigate whether the representation of visual object features in the MD regions flexibly adjusts according to task relevance. Participants were trained to categorize visually similar novel objects along two orthogonal stimulus dimensions (length/orientation) and performed short alternating blocks in which only one of these dimensions was relevant. We found that multivoxel patterns of activation in the MD regions encoded the task-relevant distinctions more strongly than the task-irrelevant distinctions: The MD regions discriminated between stimuli of different lengths when length was relevant and between the same objects according to orientation when orientation was relevant. The data suggest a flexible neural system that adjusts its representation of visual objects to preferentially encode stimulus features that are currently relevant for behavior, providing a neural mechanism for feature-selective attention.

Infant joint attention, neural networks and social cognition.

PubMed

Mundy, Peter; Jarrold, William

2010-01-01

Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). In this paper we argue that a neural network approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one's own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one's own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. We also propose that with development, joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. Copyright © 2010 Elsevier Ltd. All rights reserved.

Source Reconstruction of Brain Potentials Using Bayesian Model Averaging to Analyze Face Intra-Domain vs. Face-Occupation Cross-Domain Processing.

PubMed

Olivares, Ela I; Lage-Castellanos, Agustín; Bobes, María A; Iglesias, Jaime

2018-01-01

We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs) in both intra-domain (face-feature) and cross-domain (face-occupation) matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively. A novel Bayesian source reconstruction approach plus conjunction analysis of group effects revealed that in both cases the generated N170s were of similar amplitude but had different neural origin. Thus, whereas the N170 of faces was associated predominantly to right fusiform and occipital regions (the so-called "Fusiform Face Area", "FFA" and "Occipital Face Area", "OFA", respectively), the N170 of occupations was associated to a bilateral very posterior activity, suggestive of basic perceptual processes. Importantly, the right-sided perceptual P200 and the face-related N250 were evoked exclusively in the intra-domain task, with sources in OFA and extensively in the fusiform region, respectively. Regarding later latencies, the intra-domain N400 seemed to be generated in right posterior brain regions encompassing mainly OFA and, to some extent, the FFA, likely reflecting neural operations triggered by structural incongruities. In turn, the cross-domain N400 was related to more anterior left-sided fusiform and temporal inferior sources, paralleling those described previously for the classic verbal N400. These results support the existence of differentiated neural streams for face structure and person-related verbal processing triggered by faces, which can be activated differentially according to specific task demands.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

PubMed Central

Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire

2015-01-01

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269

Dopamine modulation of emotional processing in cortical and subcortical neural circuits: evidence for a final common pathway in schizophrenia?

PubMed

Laviolette, Steven R

2007-07-01

The neural regulation of emotional perception, learning, and memory is essential for normal behavioral and cognitive functioning. Many of the symptoms displayed by individuals with schizophrenia may arise from fundamental disturbances in the ability to accurately process emotionally salient sensory information. The neurotransmitter dopamine (DA) and its ability to modulate neural regions involved in emotional learning, perception, and memory formation has received considerable research attention as a potential final common pathway to account for the aberrant emotional regulation and psychosis present in the schizophrenic syndrome. Evidence from both human neuroimaging studies and animal-based research using neurodevelopmental, behavioral, and electrophysiological techniques have implicated the mesocorticolimbic DA circuit as a crucial system for the encoding and expression of emotionally salient learning and memory formation. While many theories have examined the cortical-subcortical interactions between prefrontal cortical regions and subcortical DA substrates, many questions remain as to how DA may control emotional perception and learning and how disturbances linked to DA abnormalities may underlie the disturbed emotional processing in schizophrenia. Beyond the mesolimbic DA system, increasing evidence points to the amygdala-prefrontal cortical circuit as an important processor of emotionally salient information and how neurodevelopmental perturbances within this circuitry may lead to dysregulation of DAergic modulation of emotional processing and learning along this cortical-subcortical emotional processing circuit.

Maladaptive social information processing in childhood predicts young men’s atypical amygdala reactivity to threat

PubMed Central

Choe, Daniel Ewon; Shaw, Daniel S.; Forbes, Erika E.

2014-01-01

Background Maladaptive social information processing, such as hostile attributional bias and aggressive response generation, is associated with childhood maladjustment. Although social information processing problems are correlated with heightened physiological responses to social threat, few studies have examined their associations with neural threat circuitry, specifically amygdala activation to social threat. Methods A cohort of 310 boys participated in an ongoing longitudinal study and completed questionnaires and laboratory tasks assessing their social and cognitive characteristics between 10- and 12-years-old. At age 20, 178 of these young men underwent functional magnetic resonance imaging and a social threat task. At age 22, adult criminal arrest records and self-reports of impulsiveness were obtained. Results Path models indicated that maladaptive social information processing at ages 10 and 11 predicted increased left amygdala reactivity to fear faces, an ambiguous threat, at age 20 while accounting for childhood antisocial behavior, empathy, IQ, and socioeconomic status. Exploratory analyses indicated that aggressive response generation—the tendency to respond to threat with reactive aggression—predicted left amygdala reactivity to fear faces and was concurrently associated with empathy, antisocial behavior, and hostile attributional bias, whereas hostile attributional bias correlated with IQ. Although unrelated to social information processing problems, bilateral amygdala reactivity to anger faces at age 20 was unexpectedly predicted by low IQ at age 11. Amygdala activation did not mediate associations between social information processing and number of criminal arrests, but both impulsiveness at age 22 and arrests were correlated with right amygdala reactivity to anger facial expressions at age 20. Conclusions Childhood social information processing and IQ predicted young men’s amygdala response to threat a decade later, which suggests that childhood social-cognitive characteristics are associated with the development of neural threat processing and adult adjustment. PMID:25142952

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Neural coding of time-varying interaural time differences and time-varying amplitude in the inferior colliculus

PubMed Central

2017-01-01

Binaural cues occurring in natural environments are frequently time varying, either from the motion of a sound source or through interactions between the cues produced by multiple sources. Yet, a broad understanding of how the auditory system processes dynamic binaural cues is still lacking. In the current study, we directly compared neural responses in the inferior colliculus (IC) of unanesthetized rabbits to broadband noise with time-varying interaural time differences (ITD) with responses to noise with sinusoidal amplitude modulation (SAM) over a wide range of modulation frequencies. On the basis of prior research, we hypothesized that the IC, one of the first stages to exhibit tuning of firing rate to modulation frequency, might use a common mechanism to encode time-varying information in general. Instead, we found weaker temporal coding for dynamic ITD compared with amplitude modulation and stronger effects of adaptation for amplitude modulation. The differences in temporal coding of dynamic ITD compared with SAM at the single-neuron level could be a neural correlate of “binaural sluggishness,” the inability to perceive fluctuations in time-varying binaural cues at high modulation frequencies, for which a physiological explanation has so far remained elusive. At ITD-variation frequencies of 64 Hz and above, where a temporal code was less effective, noise with a dynamic ITD could still be distinguished from noise with a constant ITD through differences in average firing rate in many neurons, suggesting a frequency-dependent tradeoff between rate and temporal coding of time-varying binaural information. NEW & NOTEWORTHY Humans use time-varying binaural cues to parse auditory scenes comprising multiple sound sources and reverberation. However, the neural mechanisms for doing so are poorly understood. Our results demonstrate a potential neural correlate for the reduced detectability of fluctuations in time-varying binaural information at high speeds, as occurs in reverberation. The results also suggest that the neural mechanisms for processing time-varying binaural and monaural cues are largely distinct. PMID:28381487

Enabling the First Ever Measurement of Coherent Neutrino Scattering Through Background Neutron Measurements.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyna, David; Betty, Rita

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis,thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities. The purpose of this project was to computationally model the impact of neural population dynamics within the neurobiological memory system in order to examine how subareas in the brain enable pattern separation and completion of information in memory across time as associated experiences.

Amygdala neural activity reflects spatial attention towards stimuli promising reward or threatening punishment

PubMed Central

Peck, Christopher J; Salzman, C Daniel

2014-01-01

Humans and other animals routinely identify and attend to sensory stimuli so as to rapidly acquire rewards or avoid aversive experiences. Emotional arousal, a process mediated by the amygdala, can enhance attention to stimuli in a non-spatial manner. However, amygdala neural activity was recently shown to encode spatial information about reward-predictive stimuli, and to correlate with spatial attention allocation. If representing the motivational significance of sensory stimuli within a spatial framework reflects a general principle of amygdala function, then spatially selective neural responses should also be elicited by sensory stimuli threatening aversive events. Recordings from amygdala neurons were therefore obtained while monkeys directed spatial attention towards stimuli promising reward or threatening punishment. Neural responses encoded spatial information similarly for stimuli associated with both valences of reinforcement, and responses reflected spatial attention allocation. The amygdala therefore may act to enhance spatial attention to sensory stimuli associated with rewarding or aversive experiences. DOI: http://dx.doi.org/10.7554/eLife.04478.001 PMID:25358090

Neural correlates of risk perception during real-life risk communication.

PubMed

Schmälzle, Ralf; Häcker, Frank; Renner, Britta; Honey, Christopher J; Schupp, Harald T

2013-06-19

During global health crises, such as the recent H1N1 pandemic, the mass media provide the public with timely information regarding risk. To obtain new insights into how these messages are received, we measured neural data while participants, who differed in their preexisting H1N1 risk perceptions, viewed a TV report about H1N1. Intersubject correlation (ISC) of neural time courses was used to assess how similarly the brains of viewers responded to the TV report. We found enhanced intersubject correlations among viewers with high-risk perception in the anterior cingulate, a region which classical fMRI studies associated with the appraisal of threatening information. By contrast, neural coupling in sensory-perceptual regions was similar for the high and low H1N1-risk perception groups. These results demonstrate a novel methodology for understanding how real-life health messages are processed in the human brain, with particular emphasis on the role of emotion and differences in risk perceptions.

Neural Correlates of Risk Perception during Real-Life Risk Communication

PubMed Central

Häcker, Frank; Renner, Britta; Honey, Christopher J.; Schupp, Harald T.

2013-01-01

During global health crises, such as the recent H1N1 pandemic, the mass media provide the public with timely information regarding risk. To obtain new insights into how these messages are received, we measured neural data while participants, who differed in their preexisting H1N1 risk perceptions, viewed a TV report about H1N1. Intersubject correlation (ISC) of neural time courses was used to assess how similarly the brains of viewers responded to the TV report. We found enhanced intersubject correlations among viewers with high-risk perception in the anterior cingulate, a region which classical fMRI studies associated with the appraisal of threatening information. By contrast, neural coupling in sensory-perceptual regions was similar for the high and low H1N1-risk perception groups. These results demonstrate a novel methodology for understanding how real-life health messages are processed in the human brain, with particular emphasis on the role of emotion and differences in risk perceptions. PMID:23785147

The auditory representation of speech sounds in human motor cortex

PubMed Central

Cheung, Connie; Hamilton, Liberty S; Johnson, Keith; Chang, Edward F

2016-01-01

In humans, listening to speech evokes neural responses in the motor cortex. This has been controversially interpreted as evidence that speech sounds are processed as articulatory gestures. However, it is unclear what information is actually encoded by such neural activity. We used high-density direct human cortical recordings while participants spoke and listened to speech sounds. Motor cortex neural patterns during listening were substantially different than during articulation of the same sounds. During listening, we observed neural activity in the superior and inferior regions of ventral motor cortex. During speaking, responses were distributed throughout somatotopic representations of speech articulators in motor cortex. The structure of responses in motor cortex during listening was organized along acoustic features similar to auditory cortex, rather than along articulatory features as during speaking. Motor cortex does not contain articulatory representations of perceived actions in speech, but rather, represents auditory vocal information. DOI: http://dx.doi.org/10.7554/eLife.12577.001 PMID:26943778

Active Control of Complex Systems via Dynamic (Recurrent) Neural Networks

DTIC Science & Technology

1992-05-30

course, to on-going changes brought about by learning processes. As research in neurodynamics proceeded, the concept of reverberatory information flows...Microstructure of Cognition . Vol. 1: Foundations, M.I.T. Press, Cambridge, Massachusetts, pp. 354-361, 1986. 100 I Schwarz, G., "Estimating the dimension of a...Continually Running Fully Recurrent Neural Networks, ICS Report 8805, Institute of Cognitive Science, University of California at San Diego, 1988. 10 II

Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

PubMed Central

Jiang, Jiefeng; Egner, Tobias

2014-01-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

Neural systems for social cognition in Klinefelter syndrome (47,XXY): evidence from fMRI.

PubMed

van Rijn, Sophie; Swaab, Hanna; Baas, Daan; de Haan, Edward; Kahn, René S; Aleman, André

2012-08-01

Klinefelter syndrome (KS) is a chromosomal condition (47, XXY) that may help us to unravel gene-brain behavior pathways to psychopathology. The phenotype includes social cognitive impairments and increased risk for autism traits. We used functional MRI to study neural mechanisms underlying social information processing. Eighteen nonclinical controls and thirteen men with XXY were scanned during judgments of faces with regard to trustworthiness and age. While judging faces as untrustworthy in comparison to trustworthy, men with XXY displayed less activation than controls in (i) the amygdala, which plays a key role in screening information for socio-emotional significance, (ii) the insula, which plays a role in subjective emotional experience, as well as (iii) the fusiform gyrus and (iv) the superior temporal sulcus, which are both involved in the perceptual processing of faces and which were also less involved during age judgments in men with XXY. This is the first study showing that KS can be associated with reduced involvement of the neural network subserving social cognition. Studying KS may increase our understanding of the genetic and hormonal basis of neural dysfunctions contributing to abnormalities in social cognition and behavior, which are considered core abnormalities in psychiatric disorders such as autism and schizophrenia.

Culture Wires the Brain: A Cognitive Neuroscience Perspective.

PubMed

Park, Denise C; Huang, Chih-Mao

2010-07-01

There is clear evidence that sustained experiences may affect both brain structure and function. Thus, it is quite reasonable to posit that sustained exposure to a set of cultural experiences and behavioral practices will affect neural structure and function. The burgeoning field of cultural psychology has often demonstrated the subtle differences in the way individuals process information-differences that appear to be a product of cultural experiences. We review evidence that the collectivistic and individualistic biases of East Asian and Western cultures, respectively, affect neural structure and function. We conclude that there is limited evidence that cultural experiences affect brain structure and considerably more evidence that neural function is affected by culture, particularly activations in ventral visual cortex-areas associated with perceptual processing. © The Author(s) 2010.

Valence and magnitude ambiguity in feedback processing.

PubMed

Gu, Ruolei; Feng, Xue; Broster, Lucas S; Yuan, Lu; Xu, Pengfei; Luo, Yue-Jia

2017-05-01

Outcome feedback which indicates behavioral consequences are crucial for reinforcement learning and environmental adaptation. Nevertheless, outcome information in daily life is often totally or partially ambiguous. Studying how people interpret this kind of information would provide important knowledge about the human evaluative system. This study concentrates on the neural processing of partially ambiguous feedback, that is, either its valence or magnitude is unknown to participants. To address this topic, we sequentially presented valence and magnitude information; electroencephalography (EEG) response to each kind of presentation was recorded and analyzed. The event-related potential components feedback-related negativity (FRN) and P3 were used as indices of neural activity. Consistent with previous literature, the FRN elicited by ambiguous valence was not significantly different from that elicited by negative valence. On the other hand, the FRN elicited by ambiguous magnitude was larger than both the large and small magnitude, indicating the motivation to seek unambiguous magnitude information. The P3 elicited by ambiguous valence and ambiguous magnitude was not significantly different from that elicited by negative valence and small magnitude, respectively, indicating the emotional significance of feedback ambiguity. Finally, the aforementioned effects also manifested in the stage of information integration. These findings indicate both similarities and discrepancies between the processing of valence ambiguity and that of magnitude ambiguity, which may help understand the mechanisms of ambiguous information processing.

Neurocognitive mechanisms underlying deceptive hazard evaluation: An event-related potentials investigation.

PubMed

Fu, Huijian; Qiu, Wenwei; Ma, Haiying; Ma, Qingguo

2017-01-01

Deceptive behavior is common in human social interactions. Researchers have been trying to uncover the cognitive process and neural basis underlying deception due to its theoretical and practical significance. We used Event-related potentials (ERPs) to investigate the neural correlates of deception when the participants completed a hazard judgment task. Pictures conveying or not conveying hazard information were presented to the participants who were then requested to discriminate the hazard content (safe or hazardous) and make a response corresponding to the cues (truthful or deceptive). Behavioral and electrophysiological data were recorded during the entire experiment. Results showed that deceptive responses, compared to truthful responses, were associated with longer reaction time (RT), lower accuracy, increased N2 and reduced late positive potential (LPP), suggesting a cognitively more demanding process to respond deceptively. The decrement in LPP correlated negatively with the increment in RT for deceptive relative to truthful responses, regardless of hazard content. In addition, hazardous information evoked larger N1 and P300 than safe information, reflecting an early processing bias and a later evaluative categorization process based on motivational significance, respectively. Finally, the interaction between honesty (truthful/deceptive) and safety (safe/hazardous) on accuracy and LPP indicated that deceptive responses towards safe information required more effort than deceptive responses towards hazardous information. Overall, these results demonstrate the neurocognitive substrates underlying deception about hazard information.

Neurocognitive mechanisms underlying deceptive hazard evaluation: An event-related potentials investigation

PubMed Central

Qiu, Wenwei; Ma, Haiying; Ma, Qingguo

2017-01-01

Deceptive behavior is common in human social interactions. Researchers have been trying to uncover the cognitive process and neural basis underlying deception due to its theoretical and practical significance. We used Event-related potentials (ERPs) to investigate the neural correlates of deception when the participants completed a hazard judgment task. Pictures conveying or not conveying hazard information were presented to the participants who were then requested to discriminate the hazard content (safe or hazardous) and make a response corresponding to the cues (truthful or deceptive). Behavioral and electrophysiological data were recorded during the entire experiment. Results showed that deceptive responses, compared to truthful responses, were associated with longer reaction time (RT), lower accuracy, increased N2 and reduced late positive potential (LPP), suggesting a cognitively more demanding process to respond deceptively. The decrement in LPP correlated negatively with the increment in RT for deceptive relative to truthful responses, regardless of hazard content. In addition, hazardous information evoked larger N1 and P300 than safe information, reflecting an early processing bias and a later evaluative categorization process based on motivational significance, respectively. Finally, the interaction between honesty (truthful/deceptive) and safety (safe/hazardous) on accuracy and LPP indicated that deceptive responses towards safe information required more effort than deceptive responses towards hazardous information. Overall, these results demonstrate the neurocognitive substrates underlying deception about hazard information. PMID:28793344

Processing counterfactual and hypothetical conditionals: an fMRI investigation.

PubMed

Kulakova, Eugenia; Aichhorn, Markus; Schurz, Matthias; Kronbichler, Martin; Perner, Josef

2013-05-15

Counterfactual thinking is ubiquitous in everyday life and an important aspect of cognition and emotion. Although counterfactual thought has been argued to differ from processing factual or hypothetical information, imaging data which elucidate these differences on a neural level are still scarce. We investigated the neural correlates of processing counterfactual sentences under visual and aural presentation. We compared conditionals in subjunctive mood which explicitly contradicted previously presented facts (i.e. counterfactuals) to conditionals framed in indicative mood which did not contradict factual world knowledge and thus conveyed a hypothetical supposition. Our results show activation in right occipital cortex (cuneus) and right basal ganglia (caudate nucleus) during counterfactual sentence processing. Importantly the occipital activation is not only present under visual presentation but also with purely auditory stimulus presentation, precluding a visual processing artifact. Thus our results can be interpreted as reflecting the fact that counterfactual conditionals pragmatically imply the relevance of keeping in mind both factual and supposed information whereas the hypothetical conditionals imply that real world information is irrelevant for processing the conditional and can be omitted. The need to sustain representations of factual and suppositional events during counterfactual sentence processing requires increased mental imagery and integration efforts. Our findings are compatible with predictions based on mental model theory. Copyright © 2013 Elsevier Inc. All rights reserved.

Coding of cognitive magnitude: compressed scaling of numerical information in the primate prefrontal cortex.

PubMed

Nieder, Andreas; Miller, Earl K

2003-01-09

Whether cognitive representations are better conceived as language-based, symbolic representations or perceptually related, analog representations is a subject of debate. If cognitive processes parallel perceptual processes, then fundamental psychophysical laws should hold for each. To test this, we analyzed both behavioral and neuronal representations of numerosity in the prefrontal cortex of rhesus monkeys. The data were best described by a nonlinearly compressed scaling of numerical information, as postulated by the Weber-Fechner law or Stevens' law for psychophysical/sensory magnitudes. This nonlinear compression was observed on the neural level during the acquisition phase of the task and maintained through the memory phase with no further compression. These results suggest that certain cognitive and perceptual/sensory representations share the same fundamental mechanisms and neural coding schemes.

Image processing and analysis using neural networks for optometry area

NASA Astrophysics Data System (ADS)

Netto, Antonio V.; Ferreira de Oliveira, Maria C.

2002-11-01

In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.

A neutron spectrum unfolding code based on generalized regression artificial neural networks.

PubMed

Del Rosario Martinez-Blanco, Ma; Ornelas-Vargas, Gerardo; Castañeda-Miranda, Celina Lizeth; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, Jose M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

2016-11-01

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Double Dissociation between Anterior and Posterior Superior Temporal Gyrus for Processing Audiovisual Speech Demonstrated by Electrocorticography.

PubMed

Ozker, Muge; Schepers, Inga M; Magnotti, John F; Yoshor, Daniel; Beauchamp, Michael S

2017-06-01

Human speech can be comprehended using only auditory information from the talker's voice. However, comprehension is improved if the talker's face is visible, especially if the auditory information is degraded as occurs in noisy environments or with hearing loss. We explored the neural substrates of audiovisual speech perception using electrocorticography, direct recording of neural activity using electrodes implanted on the cortical surface. We observed a double dissociation in the responses to audiovisual speech with clear and noisy auditory component within the superior temporal gyrus (STG), a region long known to be important for speech perception. Anterior STG showed greater neural activity to audiovisual speech with clear auditory component, whereas posterior STG showed similar or greater neural activity to audiovisual speech in which the speech was replaced with speech-like noise. A distinct border between the two response patterns was observed, demarcated by a landmark corresponding to the posterior margin of Heschl's gyrus. To further investigate the computational roles of both regions, we considered Bayesian models of multisensory integration, which predict that combining the independent sources of information available from different modalities should reduce variability in the neural responses. We tested this prediction by measuring the variability of the neural responses to single audiovisual words. Posterior STG showed smaller variability than anterior STG during presentation of audiovisual speech with noisy auditory component. Taken together, these results suggest that posterior STG but not anterior STG is important for multisensory integration of noisy auditory and visual speech.

Computational neural learning formalisms for manipulator inverse kinematics

NASA Technical Reports Server (NTRS)

Gulati, Sandeep; Barhen, Jacob; Iyengar, S. Sitharama

1989-01-01

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

NASA Astrophysics Data System (ADS)

Aoyagi, Toshio; Nomura, Masaki

1999-08-01

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

NASA Astrophysics Data System (ADS)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Neural classifier in the estimation process of maturity of selected varieties of apples

NASA Astrophysics Data System (ADS)

Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Wojcieszak, D.; Zbytek, Z.; Ludwiczak, A.; Przybylak, A.; Lewicki, A.

2015-07-01

This paper seeks to present methods of neural image analysis aimed at estimating the maturity state of selected varieties of apples which are popular in Poland. An identification of the degree of maturity of selected varieties of apples has been conducted on the basis of information encoded in graphical form, presented in the digital photos. The above process involves the application of the BBCH scale, used to determine the maturity of apples. The aforementioned scale is widely used in the EU and has been developed for many species of monocotyledonous plants and dicotyledonous plants. It is also worth noticing that the given scale enables detailed determinations of development stage of a given plant. The purpose of this work is to identify maturity level of selected varieties of apples, which is supported by the use of image analysis methods and classification techniques represented by artificial neural networks. The analysis of graphical representative features based on image analysis method enabled the assessment of the maturity of apples. For the utilitarian purpose the "JabVis 1.1" neural IT system was created, in accordance with requirements of the software engineering dedicated to support the decision-making processes occurring in broadly understood production process and processing of apples.

Combined contributions of feedforward and feedback inputs to bottom-up attention

PubMed Central

Khorsand, Peyman; Moore, Tirin; Soltani, Alireza

2015-01-01

In order to deal with a large amount of information carried by visual inputs entering the brain at any given point in time, the brain swiftly uses the same inputs to enhance processing in one part of visual field at the expense of the others. These processes, collectively called bottom-up attentional selection, are assumed to solely rely on feedforward processing of the external inputs, as it is implied by the nomenclature. Nevertheless, evidence from recent experimental and modeling studies points to the role of feedback in bottom-up attention. Here, we review behavioral and neural evidence that feedback inputs are important for the formation of signals that could guide attentional selection based on exogenous inputs. Moreover, we review results from a modeling study elucidating mechanisms underlying the emergence of these signals in successive layers of neural populations and how they depend on feedback from higher visual areas. We use these results to interpret and discuss more recent findings that can further unravel feedforward and feedback neural mechanisms underlying bottom-up attention. We argue that while it is descriptively useful to separate feedforward and feedback processes underlying bottom-up attention, these processes cannot be mechanistically separated into two successive stages as they occur at almost the same time and affect neural activity within the same brain areas using similar neural mechanisms. Therefore, understanding the interaction and integration of feedforward and feedback inputs is crucial for better understanding of bottom-up attention. PMID:25784883

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

PubMed Central

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding. We believe that our study shed lights on the mechanism underlying the efficient neural information processing via adaptation. PMID:29636675

Scary symptoms? Functional magnetic resonance imaging evidence for symptom interpretation bias in pathological health anxiety.

PubMed

Yan, Zhimin; Witthöft, Michael; Bailer, Josef; Diener, Carsten; Mier, Daniela

2017-08-12

Patients with pathological health anxiety (PHA) tend to automatically interpret bodily sensations as sign of a severe illness. To elucidate the neural correlates of this cognitive bias, we applied an functional magnetic resonance imaging adaption of a body-symptom implicit association test with symptom words in patients with PHA (n = 32) in comparison to patients with depression (n = 29) and healthy participants (n = 35). On the behavioral level, patients with PHA did not significantly differ from the control groups. However, on the neural-level patients with PHA in comparison to the control groups showed hyperactivation independent of condition in bilateral amygdala, right parietal lobe, and left nucleus accumbens. Moreover, patients with PHA, again in comparison to the control groups, showed hyperactivation in bilateral posterior parietal cortex and left dorsolateral prefrontal cortex during incongruent (i.e., harmless) versus congruent (i.e., dangerous) categorizations of body symptoms. Thus, body-symptom cues seem to trigger hyperactivity in salience and emotion processing brain regions in PHA. In addition, hyperactivity in brain regions involved in cognitive control and conflict resolution during incongruent categorization emphasizes enhanced neural effort to cope with negative implicit associations to body-symptom-related information in PHA. These results suggest increased neural responding in key structures for the processing of both emotional and cognitive aspects of body-symptom information in PHA, reflecting potential neural correlates of a negative somatic symptom interpretation bias.

Delayed excitatory and inhibitory feedback shape neural information transmission

NASA Astrophysics Data System (ADS)

Chacron, Maurice J.; Longtin, André; Maler, Leonard

2005-11-01

Feedback circuitry with conduction and synaptic delays is ubiquitous in the nervous system. Yet the effects of delayed feedback on sensory processing of natural signals are poorly understood. This study explores the consequences of delayed excitatory and inhibitory feedback inputs on the processing of sensory information. We show, through numerical simulations and theory, that excitatory and inhibitory feedback can alter the firing frequency response of stochastic neurons in opposite ways by creating dynamical resonances, which in turn lead to information resonances (i.e., increased information transfer for specific ranges of input frequencies). The resonances are created at the expense of decreased information transfer in other frequency ranges. Using linear response theory for stochastically firing neurons, we explain how feedback signals shape the neural transfer function for a single neuron as a function of network size. We also find that balanced excitatory and inhibitory feedback can further enhance information tuning while maintaining a constant mean firing rate. Finally, we apply this theory to in vivo experimental data from weakly electric fish in which the feedback loop can be opened. We show that it qualitatively predicts the observed effects of inhibitory feedback. Our study of feedback excitation and inhibition reveals a possible mechanism by which optimal processing may be achieved over selected frequency ranges.

Is the Brain a Quantum Computer?

ERIC Educational Resources Information Center

Litt, Abninder; Eliasmith, Chris; Kroon, Frederick W.; Weinstein, Steven; Thagard, Paul

2006-01-01

We argue that computation via quantum mechanical processes is irrelevant to explaining how brains produce thought, contrary to the ongoing speculations of many theorists. First, quantum effects do not have the temporal properties required for neural information processing. Second, there are substantial physical obstacles to any organic…

More than Skin Deep: Body Representation beyond Primary Somatosensory Cortex

ERIC Educational Resources Information Center

Longo, Matthew R.; Azanon, Elena; Haggard, Patrick

2010-01-01

The neural circuits underlying initial sensory processing of somatic information are relatively well understood. In contrast, the processes that go beyond primary somatosensation to create more abstract representations related to the body are less clear. In this review, we focus on two classes of higher-order processing beyond Somatosensation.…

Sleep, Off-Line Processing, and Vocal Learning

ERIC Educational Resources Information Center

Margoliash, Daniel; Schmidt, Marc F.

2010-01-01

The study of song learning and the neural song system has provided an important comparative model system for the study of speech and language acquisition. We describe some recent advances in the bird song system, focusing on the role of off-line processing including sleep in processing sensory information and in guiding developmental song…

An information theory account of cognitive control

PubMed Central

Fan, Jin

2014-01-01

Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory. PMID:25228875

Love withdrawal predicts electrocortical responses to emotional faces with performance feedback: a follow-up and extension.

PubMed

Huffmeijer, Renske; Bakermans-Kranenburg, Marian J; Alink, Lenneke R A; van IJzendoorn, Marinus H

2014-06-02

Parental use of love withdrawal is thought to affect children's later psychological functioning because it creates a link between children's performance and relational consequences. In addition, recent studies have begun to show that experiences of love withdrawal also relate to the neural processing of socio-emotional information relevant to a performance-relational consequence link, and can moderate effects of oxytocin on social information processing and behavior. The current study follows-up on our previous results by attempting to confirm and extend previous findings indicating that experiences of maternal love withdrawal are related to electrocortical responses to emotional faces presented with performance feedback. More maternal love withdrawal was related to enhanced early processing of facial feedback stimuli (reflected in more positive VPP amplitudes, and confirming previous findings). However, attentional engagement with and processing of the stimuli at a later stage were diminished in those reporting higher maternal love withdrawal (reflected in less positive LPP amplitudes, and diverging from previous findings). Maternal love withdrawal affects the processing of emotional faces presented with performance feedback differently in different stages of neural processing.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

NASA Astrophysics Data System (ADS)

Oby, Emily R.; Perel, Sagi; Sadtler, Patrick T.; Ruff, Douglas A.; Mischel, Jessica L.; Montez, David F.; Cohen, Marlene R.; Batista, Aaron P.; Chase, Steven M.

2016-06-01

Objective. A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). Approach. We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. Main Results. The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. Significance. How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

PubMed Central

Oby, Emily R; Perel, Sagi; Sadtler, Patrick T; Ruff, Douglas A; Mischel, Jessica L; Montez, David F; Cohen, Marlene R; Batista, Aaron P; Chase, Steven M

2018-01-01

Objective A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain–computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). Approach We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. Main Results The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. Significance How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue. PMID:27097901

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.

PubMed

Oby, Emily R; Perel, Sagi; Sadtler, Patrick T; Ruff, Douglas A; Mischel, Jessica L; Montez, David F; Cohen, Marlene R; Batista, Aaron P; Chase, Steven M

2016-06-01

A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.

Network Modeling of Adult Neurogenesis: Shifting Rates of Neuronal Turnover Optimally Gears Network Learning according to Novelty Gradient

PubMed Central

Chambers, R. Andrew; Conroy, Susan K.

2010-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In “adult” networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis–neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders. PMID:17214558

Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient.

PubMed

Chambers, R Andrew; Conroy, Susan K

2007-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In "adult" networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.

Sex, lies and fMRI--gender differences in neural basis of deception.

PubMed

Marchewka, Artur; Jednorog, Katarzyna; Falkiewicz, Marcel; Szeszkowski, Wojciech; Grabowska, Anna; Szatkowska, Iwona

2012-01-01

Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants' subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG). Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization.

Sex, Lies and fMRI—Gender Differences in Neural Basis of Deception

PubMed Central

Falkiewicz, Marcel; Szeszkowski, Wojciech; Grabowska, Anna; Szatkowska, Iwona

2012-01-01

Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants’ subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG). Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization. PMID:22952631

Altered Topology in Information Processing of a Narrated Story in Older Adults with Mild Cognitive Impairment.

PubMed

Yogev-Seligmann, Galit; Oren, Noga; Ash, Elissa L; Hendler, Talma; Giladi, Nir; Lerner, Yulia

2016-05-03

The ability to store, integrate, and manipulate information declines with aging. These changes occur earlier, faster, and to a greater degree as a result of neurodegeneration. One of the most common and early characteristics of cognitive decline is difficulty with comprehension of information. The neural mechanisms underlying this breakdown of information processing are poorly understood. Using functional MRI and natural stimuli (e.g., stories), we mapped the neural mechanisms by which the human brain accumulates and processes information with increasing duration and complexity in participants with amnestic mild cognitive impairment (aMCI) and healthy older adults. To explore the mechanisms of information processing, we measured the reliability of brain responses elicited by listening to different versions of a narrated story created by segmenting the story into words, sentences, and paragraphs and then scrambling the segments. Comparing healthy older adults and participants with aMCI revealed that in both groups, all types of stimuli similarly recruited primary auditory areas. However, prominent differences between groups were found at the level of processing long and complex stimuli. In healthy older adults, parietal and frontal regions demonstrated highly synchronized responses in both the paragraph and full story conditions, as has been previously reported in young adults. Participants with aMCI, however, exhibited a robust functional shift of long time scale processing to the pre- and post-central sulci. Our results suggest that participants with aMCI experienced a functional shift of higher order auditory information processing, possibly reflecting a functional response to concurrent or impending neuronal or synaptic loss. This observation might assist in understanding mechanisms of cognitive decline in aMCI.

The problem with value

PubMed Central

O’Doherty, John P.

2015-01-01

Neural correlates of value have been extensively reported in a diverse set of brain regions. However, in many cases it is difficult to determine whether a particular neural response pattern corresponds to a value-signal per se as opposed to an array of alternative non-value related processes, such as outcome-identity coding, informational coding, encoding of autonomic and skeletomotor consequences, alongside previously described “salience” or “attentional” effects. Here, I review a number of experimental manipulations that can be used to test for value, and I identify the challenges in ascertaining whether a particular neural response is or is not a value signal. Finally, I emphasize that some non-value related signals may be especially informative as a means of providing insight into the nature of the decision-making related computations that are being implemented in a particular brain region. PMID:24726573

Arm injury produces long-term behavioral and neural hypersensitivity in octopus.

PubMed

Alupay, Jean S; Hadjisolomou, Stavros P; Crook, Robyn J

2014-01-13

Cephalopod molluscs are the most neurally and behaviorally complex invertebrates, with brains rivaling those of some vertebrates in size and complexity. This has fostered the opinion that cephalopods, particularly octopuses, may experience vertebrate-like pain when injured. However, it is not known whether octopuses possess nociceptors or if their somatic sensory neurons exhibit sensitization after injury. Here we show that the octopus Abdopus aculeatus expresses nocifensive behaviors including arm autotomy, and displays marked neural hyperexcitability both in injured and uninjured arms for at least 24h after injury. These findings do not demonstrate that octopuses experience pain-like states; instead they add to the minimal existing literature on how cephalopods receive, process, and integrate noxious sensory information, potentially informing and refining regulations governing use of cephalopods in scientific research. Published by Elsevier Ireland Ltd.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Information processing in the CNS: a supramolecular chemistry?

PubMed

Tozzi, Arturo

2015-10-01

How does central nervous system process information? Current theories are based on two tenets: (a) information is transmitted by action potentials, the language by which neurons communicate with each other-and (b) homogeneous neuronal assemblies of cortical circuits operate on these neuronal messages where the operations are characterized by the intrinsic connectivity among neuronal populations. In this view, the size and time course of any spike is stereotypic and the information is restricted to the temporal sequence of the spikes; namely, the "neural code". However, an increasing amount of novel data point towards an alternative hypothesis: (a) the role of neural code in information processing is overemphasized. Instead of simply passing messages, action potentials play a role in dynamic coordination at multiple spatial and temporal scales, establishing network interactions across several levels of a hierarchical modular architecture, modulating and regulating the propagation of neuronal messages. (b) Information is processed at all levels of neuronal infrastructure from macromolecules to population dynamics. For example, intra-neuronal (changes in protein conformation, concentration and synthesis) and extra-neuronal factors (extracellular proteolysis, substrate patterning, myelin plasticity, microbes, metabolic status) can have a profound effect on neuronal computations. This means molecular message passing may have cognitive connotations. This essay introduces the concept of "supramolecular chemistry", involving the storage of information at the molecular level and its retrieval, transfer and processing at the supramolecular level, through transitory non-covalent molecular processes that are self-organized, self-assembled and dynamic. Finally, we note that the cortex comprises extremely heterogeneous cells, with distinct regional variations, macromolecular assembly, receptor repertoire and intrinsic microcircuitry. This suggests that every neuron (or group of neurons) embodies different molecular information that hands an operational effect on neuronal computation.

Neurovision processor for designing intelligent sensors

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1992-03-01

A programmable multi-task neuro-vision processor, called the Positive-Negative (PN) neural processor, is proposed as a plausible hardware mechanism for constructing robust multi-task vision sensors. The computational operations performed by the PN neural processor are loosely based on the neural activity fields exhibited by certain nervous tissue layers situated in the brain. The neuro-vision processor can be programmed to generate diverse dynamic behavior that may be used for spatio-temporal stabilization (STS), short-term visual memory (STVM), spatio-temporal filtering (STF) and pulse frequency modulation (PFM). A multi- functional vision sensor that performs a variety of information processing operations on time- varying two-dimensional sensory images can be constructed from a parallel and hierarchical structure of numerous individually programmed PN neural processors.

Neural Global Pattern Similarity Underlies True and False Memories.

PubMed

Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Neural correlates of the processing of self-referent emotional information in bulimia nervosa.

PubMed

Pringle, A; Ashworth, F; Harmer, C J; Norbury, R; Cooper, M J

2011-10-01

There is increasing interest in understanding the roles of distorted beliefs about the self, ostensibly unrelated to eating, weight and shape, in eating disorders (EDs), but little is known about their neural correlates. We therefore used functional magnetic resonance imaging to investigate the neural correlates of self-referent emotional processing in EDs. During the scan, unmedicated patients with bulimia nervosa (n=11) and healthy controls (n=16) responded to personality words previously found to be related to negative self beliefs in EDs and depression. Rating of the negative personality descriptors resulted in reduced activation in patients compared to controls in parietal, occipital and limbic areas including the amygdala. There was no evidence that reduced activity in patients was secondary to increased cognitive control. Different patterns of neural activation between patients and controls may be the result of either habituation to personally relevant negative self beliefs or of emotional blunting in patients. Copyright © 2011 Elsevier Ltd. All rights reserved.

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Functional model of biological neural networks.

PubMed

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data.

PubMed

Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

2018-05-01

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain. © 2018 Wiley Periodicals, Inc.

Toward a Neural Basis of Music Perception – A Review and Updated Model

PubMed Central

Koelsch, Stefan

2011-01-01

Music perception involves acoustic analysis, auditory memory, auditory scene analysis, processing of interval relations, of musical syntax and semantics, and activation of (pre)motor representations of actions. Moreover, music perception potentially elicits emotions, thus giving rise to the modulation of emotional effector systems such as the subjective feeling system, the autonomic nervous system, the hormonal, and the immune system. Building on a previous article (Koelsch and Siebel, 2005), this review presents an updated model of music perception and its neural correlates. The article describes processes involved in music perception, and reports EEG and fMRI studies that inform about the time course of these processes, as well as about where in the brain these processes might be located. PMID:21713060

Working Memory in the Prefrontal Cortex

PubMed Central

Funahashi, Shintaro

2017-01-01

The prefrontal cortex participates in a variety of higher cognitive functions. The concept of working memory is now widely used to understand prefrontal functions. Neurophysiological studies have revealed that stimulus-selective delay-period activity is a neural correlate of the mechanism for temporarily maintaining information in working memory processes. The central executive, which is the master component of Baddeley’s working memory model and is thought to be a function of the prefrontal cortex, controls the performance of other components by allocating a limited capacity of memory resource to each component based on its demand. Recent neurophysiological studies have attempted to reveal how prefrontal neurons achieve the functions of the central executive. For example, the neural mechanisms of memory control have been examined using the interference effect in a dual-task paradigm. It has been shown that this interference effect is caused by the competitive and overloaded recruitment of overlapping neural populations in the prefrontal cortex by two concurrent tasks and that the information-processing capacity of a single neuron is limited to a fixed level, can be flexibly allocated or reallocated between two concurrent tasks based on their needs, and enhances behavioral performance when its allocation to one task is increased. Further, a metamemory task requiring spatial information has been used to understand the neural mechanism for monitoring its own operations, and it has been shown that monitoring the quality of spatial information represented by prefrontal activity is an important factor in the subject's choice and that the strength of spatially selective delay-period activity reflects confidence in decision-making. Although further studies are needed to elucidate how the prefrontal cortex controls memory resource and supervises other systems, some important mechanisms related to the central executive have been identified. PMID:28448453

Dynamic Encoding of Acoustic Features in Neural Responses to Continuous Speech.

PubMed

Khalighinejad, Bahar; Cruzatto da Silva, Guilherme; Mesgarani, Nima

2017-02-22

Humans are unique in their ability to communicate using spoken language. However, it remains unclear how the speech signal is transformed and represented in the brain at different stages of the auditory pathway. In this study, we characterized electroencephalography responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). We showed that responses to different phoneme categories are organized by phonetic features. We found that each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations revealed that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders. SIGNIFICANCE STATEMENT We characterized the properties of evoked neural responses to phoneme instances in continuous speech. We show that each instance of a phoneme in continuous speech produces several observable neural responses at different times occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Each temporal event explicitly encodes the acoustic similarity of phonemes, and linguistic and nonlinguistic information are best represented at different time intervals. Finally, we show a joint encoding of phonetic and speaker information, where the neural representation of speakers is dependent on phoneme category. These findings provide compelling new evidence for dynamic processing of speech sounds in the auditory pathway. Copyright © 2017 Khalighinejad et al.

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1999-01-01

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

Synaptic unreliability facilitates information transmission in balanced cortical populations

NASA Astrophysics Data System (ADS)

Gatys, Leon A.; Ecker, Alexander S.; Tchumatchenko, Tatjana; Bethge, Matthias

2015-06-01

Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Prediction of collision events: an EEG coherence analysis.

PubMed

Spapé, Michiel M; Serrien, Deborah J

2011-05-01

A common daily-life task is the interaction with moving objects for which prediction of collision events is required. To evaluate the sources of information used in this process, this EEG study required participants to judge whether two moving objects would collide with one another or not. In addition, the effect of a distractor object is evaluated. The measurements included the behavioural decision time and accuracy, eye movement fixation times, and the neural dynamics which was determined by means of EEG coherence, expressing functional connectivity between brain areas. Collision judgment involved widespread information processing across both hemispheres. When a distractor object was present, task-related activity was increased whereas distractor activity induced modulation of local sensory processing. Also relevant were the parietal regions communicating with bilateral occipital and midline areas and a left-sided sensorimotor circuit. Besides visual cues, cognitive and strategic strategies are used to establish a decision of events in time. When distracting information is introduced into the collision judgment process, it is managed at different processing levels and supported by distinct neural correlates. These data shed light on the processing mechanisms that support judgment of collision events; an ability that implicates higher-order decision-making. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The role of fluid intelligence and learning in analogical reasoning: How to become neurally efficient?

PubMed

Dix, Annika; Wartenburger, Isabell; van der Meer, Elke

2016-10-01

This study on analogical reasoning evaluates the impact of fluid intelligence on adaptive changes in neural efficiency over the course of an experiment and specifies the underlying cognitive processes. Grade 10 students (N=80) solved unfamiliar geometric analogy tasks of varying difficulty. Neural efficiency was measured by the event-related desynchronization (ERD) in the alpha band, an indicator of cortical activity. Neural efficiency was defined as a low amount of cortical activity accompanying high performance during problem-solving. Students solved the tasks faster and more accurately the higher their FI was. Moreover, while high FI led to greater cortical activity in the first half of the experiment, high FI was associated with a neurally more efficient processing (i.e., better performance but same amount of cortical activity) in the second half of the experiment. Performance in difficult tasks improved over the course of the experiment for all students while neural efficiency increased for students with higher but decreased for students with lower fluid intelligence. Based on analyses of the alpha sub-bands, we argue that high fluid intelligence was associated with a stronger investment of attentional resource in the integration of information and the encoding of relations in this unfamiliar task in the first half of the experiment (lower-2 alpha band). Students with lower fluid intelligence seem to adapt their applied strategies over the course of the experiment (i.e., focusing on task-relevant information; lower-1 alpha band). Thus, the initially lower cortical activity and its increase in students with lower fluid intelligence might reflect the overcoming of mental overload that was present in the first half of the experiment. Copyright © 2016 Elsevier Inc. All rights reserved.

Different Neural Processes Accompany Self-Recognition in Photographs Across the Lifespan: An ERP Study Using Dizygotic Twins

PubMed Central

Butler, David L.; Mattingley, Jason B.; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5–15, 16–25, and 26–45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time. PMID:24069151

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

PubMed

Butler, David L; Mattingley, Jason B; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

Neural correlates of three cognitive processes involved in theory of mind and discourse comprehension.

PubMed

Lin, Nan; Yang, Xiaohong; Li, Jing; Wang, Shaonan; Hua, Huimin; Ma, Yujun; Li, Xingshan

2018-04-01

Neuroimaging studies have found that theory of mind (ToM) and discourse comprehension involve similar brain regions. These brain regions may be associated with three cognitive components that are necessarily or frequently involved in ToM and discourse comprehension, including social concept representation and retrieval, domain-general semantic integration, and domain-specific integration of social semantic contents. Using fMRI, we investigated the neural correlates of these three cognitive components by exploring how discourse topic (social/nonsocial) and discourse processing period (ending/beginning) modulate brain activation in a discourse comprehension (and also ToM) task. Different sets of brain areas showed sensitivity to discourse topic, discourse processing period, and the interaction between them, respectively. The most novel finding was that the right temporoparietal junction and middle temporal gyrus showed sensitivity to discourse processing period only during social discourse comprehension, indicating that they selectively contribute to domain-specific semantic integration. Our finding indicates how different domains of semantic information are processed and integrated in the brain and provides new insights into the neural correlates of ToM and discourse comprehension.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Double-Labeled Metabolic Maps of Memory.

ERIC Educational Resources Information Center

John, E. R.; And Others

1986-01-01

Reviews a study which sought to obtain a quantitative metabolic map of the neurons mediating a specific memory. Research results support notions of cooperative processes in which nonrandom behavior of high ensembles of neural elements mediates the integration and processing of information and the retrieval of memory. (ML)

Executive Semantic Processing Is Underpinned by a Large-scale Neural Network: Revealing the Contribution of Left Prefrontal, Posterior Temporal, and Parietal Cortex to Controlled Retrieval and Selection Using TMS

ERIC Educational Resources Information Center

Whitney, Carin; Kirk, Marie; O'Sullivan, Jamie; Ralph, Matthew A. Lambon; Jefferies, Elizabeth

2012-01-01

To understand the meanings of words and objects, we need to have knowledge about these items themselves plus executive mechanisms that compute and manipulate semantic information in a task-appropriate way. The neural basis for semantic control remains controversial. Neuroimaging studies have focused on the role of the left inferior frontal gyrus…

Crossmodal association of auditory and visual material properties in infants.

PubMed

Ujiie, Yuta; Yamashita, Wakayo; Fujisaki, Waka; Kanazawa, So; Yamaguchi, Masami K

2018-06-18

The human perceptual system enables us to extract visual properties of an object's material from auditory information. In monkeys, the neural basis underlying such multisensory association develops through experience of exposure to a material; material information could be processed in the posterior inferior temporal cortex, progressively from the high-order visual areas. In humans, however, the development of this neural representation remains poorly understood. Here, we demonstrated for the first time the presence of a mapping of the auditory material property with visual material ("Metal" and "Wood") in the right temporal region in preverbal 4- to 8-month-old infants, using near-infrared spectroscopy (NIRS). Furthermore, we found that infants acquired the audio-visual mapping for a property of the "Metal" material later than for the "Wood" material, since infants form the visual property of "Metal" material after approximately 6 months of age. These findings indicate that multisensory processing of material information induces the activation of brain areas related to sound symbolism. Our findings also indicate that the material's familiarity might facilitate the development of multisensory processing during the first year of life.

Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Patrick I.

2003-09-23

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neuralmore » networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing information [2]. Each one of these cells acts as a simple processor. When individual cells interact with one another, the complex abilities of the brain are made possible. In neural networks, the input or data are processed by a propagation function that adds up the values of all the incoming data. The ending value is then compared with a threshold or specific value. The resulting value must exceed the activation function value in order to become output. The activation function is a mathematical function that a neuron uses to produce an output referring to its input value. [8] Figure 1 depicts this process. Neural networks usually have three components an input, a hidden, and an output. These layers create the end result of the neural network. A real world example is a child associating the word dog with a picture. The child says dog and simultaneously looks a picture of a dog. The input is the spoken word ''dog'', the hidden is the brain processing, and the output will be the category of the word dog based on the picture. This illustration describes how a neural network functions.« less

What can individual differences reveal about face processing?

PubMed Central

Yovel, Galit; Wilmer, Jeremy B.; Duchaine, Brad

2014-01-01

Faces are probably the most widely studied visual stimulus. Most research on face processing has used a group-mean approach that averages behavioral or neural responses to faces across individuals and treats variance between individuals as noise. However, individual differences in face processing can provide valuable information that complements and extends findings from group-mean studies. Here we demonstrate that studies employing an individual differences approach—examining associations and dissociations across individuals—can answer fundamental questions about the way face processing operates. In particular these studies allow us to associate and dissociate the mechanisms involved in face processing, tie behavioral face processing mechanisms to neural mechanisms, link face processing to broader capacities and quantify developmental influences on face processing. The individual differences approach we illustrate here is a powerful method that should be further explored within the domain of face processing as well as fruitfully applied across the cognitive sciences. PMID:25191241

The human body odor compound androstadienone increases neural conflict coupled to higher behavioral costs during an emotional Stroop task.

PubMed

Hornung, Jonas; Kogler, Lydia; Erb, Michael; Freiherr, Jessica; Derntl, Birgit

2018-05-01

The androgen derivative androstadienone (AND) is a substance found in human sweat and thus may act as human chemosignal. With the current experiment, we aimed to explore in which way AND affects interference processing during an emotional Stroop task which used human faces as target and emotional words as distractor stimuli. This was complemented by functional magnetic resonance imaging (fMRI) to unravel the neural mechanism of AND-action. Based on previous accounts we expected AND to increase neural activation in areas commonly implicated in evaluation of emotional face processing and to change neural activation in brain regions linked to interference processing. For this aim, a total of 80 healthy individuals (oral contraceptive users, luteal women, men) were tested twice on two consecutive days with an emotional Stroop task using fMRI. Our results suggest that AND increases interference processing in brain areas that are heavily recruited during emotional conflict. At the same time, correlation analyses revealed that this neural interference processing was paralleled by higher behavioral costs (response times) with higher interference related brain activation under AND. Furthermore, AND elicited higher activation in regions implicated in emotional face processing including right fusiform gyrus, inferior frontal gyrus and dorsomedial cortex. In this connection, neural activation was not coupled to behavioral outcome. Furthermore, despite previous accounts of increased hypothalamic activation under AND, we were not able to replicate this finding and discuss possible reasons for this discrepancy. To conclude, AND increased interference processing in regions heavily recruited during emotional conflict which was coupled to higher costs in resolving emotional conflicts with stronger interference-related brain activation under AND. At the moment it remains unclear whether these effects are due to changes in conflict detection or resolution. However, evidence most consistently suggests that AND does not draw attention to the most potent socio-emotional information (human faces) but rather highlights representations of emotional words. Copyright © 2018 Elsevier Inc. All rights reserved.

Functional connectivity mapping of regions associated with self- and other-processing.

PubMed

Murray, Ryan J; Debbané, Martin; Fox, Peter T; Bzdok, Danilo; Eickhoff, Simon B

2015-04-01

Neuroscience literature increasingly suggests a conceptual self composed of interacting neural regions, rather than independent local activations, yet such claims have yet to be investigated. We, thus, combined task-dependent meta-analytic connectivity modeling (MACM) with task-independent resting-state (RS) connectivity analysis to delineate the neural network of the self, across both states. Given psychological evidence implicating the self's interdependence on social information, we also delineated the neural network underlying conceptual other-processing. To elucidate the relation between the self-/other-networks and their function, we mined the MACM metadata to generate a cognitive-behavioral profile for an empirically identified region specific to conceptual self, the pregenual anterior cingulate (pACC), and conceptual other, posterior cingulate/precuneus (PCC/PC). Mining of 7,200 published, task-dependent, neuroimaging studies, using healthy human subjects, yielded 193 studies activating the self-related seed and were conjoined with RS connectivity analysis to delineate a differentiated self-network composed of the pACC (seed) and anterior insula, relative to other functional connectivity. Additionally, 106 studies activating the other-related seed were conjoined with RS connectivity analysis to delineate a differentiated other-network of PCC/PC (seed) and angular gyrus/temporoparietal junction, relative to self-functional connectivity. The self-network seed related to emotional conflict resolution and motivational processing, whereas the other-network seed related to socially oriented processing and contextual information integration. Notably, our findings revealed shared RS connectivity between ensuing self-/other-networks within the ventromedial prefrontal cortex and medial orbitofrontal cortex, suggesting self-updating via integration of self-relevant social information. We, therefore, present initial neurobiological evidence corroborating the increasing claims of an intricate self-network, the architecture of which may promote social value processing. © 2014 Wiley Periodicals, Inc.

Amplification of local changes along the timescale processing hierarchy.

PubMed

Yeshurun, Yaara; Nguyen, Mai; Hasson, Uri

2017-08-29

Small changes in word choice can lead to dramatically different interpretations of narratives. How does the brain accumulate and integrate such local changes to construct unique neural representations for different stories? In this study, we created two distinct narratives by changing only a few words in each sentence (e.g., "he" to "she" or "sobbing" to "laughing") while preserving the grammatical structure across stories. We then measured changes in neural responses between the two stories. We found that differences in neural responses between the two stories gradually increased along the hierarchy of processing timescales. For areas with short integration windows, such as early auditory cortex, the differences in neural responses between the two stories were relatively small. In contrast, in areas with the longest integration windows at the top of the hierarchy, such as the precuneus, temporal parietal junction, and medial frontal cortices, there were large differences in neural responses between stories. Furthermore, this gradual increase in neural differences between the stories was highly correlated with an area's ability to integrate information over time. Amplification of neural differences did not occur when changes in words did not alter the interpretation of the story (e.g., sobbing to "crying"). Our results demonstrate how subtle differences in words are gradually accumulated and amplified along the cortical hierarchy as the brain constructs a narrative over time.

The role of the episodic buffer in working memory for language processing.

PubMed

Rudner, Mary; Rönnberg, Jerker

2008-03-01

A body of work has accumulated to show that the cognitive process of binding information from different mnemonic and sensory sources as well as in different linguistic modalities can be fractionated from general executive functions in working memory both functionally and neurally. This process has been defined in terms of the episodic buffer (Baddeley in Trends Cogn Sci 4(11):417-423, 2000). This paper considers behavioural, neuropsychological and neuroimaging data that elucidate the role of the episodic buffer in language processing. We argue that the episodic buffer seems to be truly multimodal in function and that while formation of unitary multidimensional representations in the episodic buffer seems to engage posterior neural networks, maintenance of such representations is supported by frontal networks. Although, the episodic buffer is not necessarily supported by executive processes and seems to be supported by different neural networks, it may operate in tandem with the central executive during effortful language processing. There is also evidence to suggest engagement of the phonological loop during buffer processing. The hippocampus seems to play a role in formation but not maintenance of representations in the episodic buffer of working memory.

A structural and a functional aspect of stable information processing by the brain

PubMed Central

2007-01-01

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space. PMID:19003500

Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

DTIC Science & Technology

1987-10-01

include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

Neural Systems for Cognitive and Emotional Processing in Posttraumatic Stress Disorder

PubMed Central

Brown, Vanessa M.; Morey, Rajendra A.

2012-01-01

Individuals with posttraumatic stress disorder (PTSD) show altered cognition when trauma-related material is present. PTSD may lead to enhanced processing of trauma-related material, or it may cause impaired processing of trauma-unrelated information. However, other forms of emotional information may also alter cognition in PTSD. In this review, we discuss the behavioral and neural effects of emotion processing on cognition in PTSD, with a focus on neuroimaging results. We propose a model of emotion-cognition interaction based on evidence of two network models of altered brain activation in PTSD. The first is a trauma-disrupted network made up of ventrolateral PFC, dorsal anterior cingulate cortex (ACC), hippocampus, insula, and dorsomedial PFC that are differentially modulated by trauma content relative to emotional trauma-unrelated information. The trauma-disrupted network forms a subnetwork of regions within a larger, widely recognized network organized into ventral and dorsal streams for processing emotional and cognitive information that converge in the medial PFC and cingulate cortex. Models of fear learning, while not a cognitive process in the conventional sense, provide important insights into the maintenance of the core symptom clusters of PTSD such as re-experiencing and hypervigilance. Fear processing takes place within the limbic corticostriatal loop composed of threat-alerting and threat-assessing components. Understanding the disruptions in these two networks, and their effect on individuals with PTSD, will lead to an improved knowledge of the etiopathogenesis of PTSD and potential targets for both psychotherapeutic and pharmacotherapeutic interventions. PMID:23162499

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Efficient self-organizing multilayer neural network for nonlinear system modeling.

PubMed

Han, Hong-Gui; Wang, Li-Dan; Qiao, Jun-Fei

2013-07-01

It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

PubMed Central

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning’s dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions. PMID:22746951

Electrophysiological evidence for biased competition in V1 for fear expressions.

PubMed

West, Greg L; Anderson, Adam A K; Ferber, Susanne; Pratt, Jay

2011-11-01

When multiple stimuli are concurrently displayed in the visual field, they must compete for neural representation at the processing expense of their contemporaries. This biased competition is thought to begin as early as primary visual cortex, and can be driven by salient low-level stimulus features. Stimuli important for an organism's survival, such as facial expressions signaling environmental threat, might be similarly prioritized at this early stage of visual processing. In the present study, we used ERP recordings from striate cortex to examine whether fear expressions can bias the competition for neural representation at the earliest stage of retinotopic visuo-cortical processing when in direct competition with concurrently presented visual information of neutral valence. We found that within 50 msec after stimulus onset, information processing in primary visual cortex is biased in favor of perceptual representations of fear at the expense of competing visual information (Experiment 1). Additional experiments confirmed that the facial display's emotional content rather than low-level features is responsible for this prioritization in V1 (Experiment 2), and that this competition is reliant on a face's upright canonical orientation (Experiment 3). These results suggest that complex stimuli important for an organism's survival can indeed be prioritized at the earliest stage of cortical processing at the expense of competing information, with competition possibly beginning before encoding in V1.

Two-Stage Processing of Aesthetic Information in the Human Brain Revealed by Neural Adaptation Paradigm.

PubMed

Iwasaki, Miho; Noguchi, Yasuki; Kakigi, Ryusuke

2018-06-07

Some researchers in aesthetics assume visual features related to aesthetic perception (e.g. golden ratio and symmetry) commonly embedded in masterpieces. If this is true, an intriguing hypothesis is that the human brain has neural circuitry specialized for the processing of visual beauty. We presently tested this hypothesis by combining a neuroimaging technique with the repetition suppression (RS) paradigm. Subjects (non-experts in art) viewed two images of sculptures sequentially presented. Some sculptures obeyed the golden ratio (canonical images), while the golden proportion were impaired in other sculptures (deformed images). We found that the occipito-temporal cortex in the right hemisphere showed the RS when a canonical sculpture (e.g. Venus de Milo) was repeatedly presented, but not when its deformed version was repeated. Furthermore, the right parietal cortex showed the RS to the canonical proportion even when two sculptures had different identities (e.g. Venus de Milo as the first stimulus and David di Michelangelo as the second), indicating that this region encodes the golden ratio as an abstract rule shared by different sculptures. Those results suggest two separate stages of neural processing for aesthetic information (one in the occipito-temporal and another in the parietal regions) that are hierarchically arranged in the human brain.

Neural changes associated with appetite information processing in schizophrenic patients after 16 weeks of olanzapine treatment

PubMed Central

Stip, E; Lungu, O V; Anselmo, K; Letourneau, G; Mendrek, A; Stip, B; Lipp, O; Lalonde, P; Bentaleb, L A

2012-01-01

There is evidence that some atypical antipsychotics, including olanzapine, can produce unwanted metabolic side effects, weight gain and diabetes. However, neuronal correlates of change related to food information processing have not been investigated with these medications. We studied the effect of a pharmacological manipulation with an antipsychotic known to cause weight gain on metabolites, cognitive tasks and neural correlates related to food regulation. We used functional magnetic resonance imaging in conjunction with a task requiring visual processing of appetitive stimuli in schizophrenic patients and healthy controls before and after 16 weeks of antipsychotic medication with olanzapine. In patients, the psychological and neuronal changes associated following the treatment correlated with appetite control measures and metabolite levels in fasting blood samples. After 16 weeks of olanzapine treatment, the patients gained weight, increased their waist circumference, had fewer positive schizophrenia symptoms, a reduced ghrelin plasma concentration and an increased concentration of triglycerides, insulin and leptin. In premotor area, somatosensory cortices as well as bilaterally in the fusiform gyri, the olanzapine treatment increased the neural activity related to appetitive information in schizophrenic patients to similar levels relative to healthy individuals. However, a higher increase in sensitivity to appetitive stimuli after the treatment was observed in insular cortices, amygdala and cerebellum in schizophrenic patients as compared with healthy controls. Furthermore, these changes in neuronal activity correlated with changes in some metabolites and cognitive measurements related to appetite regulation. PMID:22714121

Attention affects visual perceptual processing near the hand.

PubMed

Cosman, Joshua D; Vecera, Shaun P

2010-09-01

Specialized, bimodal neural systems integrate visual and tactile information in the space near the hand. Here, we show that visuo-tactile representations allow attention to influence early perceptual processing, namely, figure-ground assignment. Regions that were reached toward were more likely than other regions to be assigned as foreground figures, and hand position competed with image-based information to bias figure-ground assignment. Our findings suggest that hand position allows attention to influence visual perceptual processing and that visual processes typically viewed as unimodal can be influenced by bimodal visuo-tactile representations.

[Neural Mechanisms That Facilitate Adaptive Behavior Based on Acquired Stimulus-Outcome Information].

PubMed

Ogawa, Masaaki

2017-11-01

In response to changing internal and external situations, we always need to adapt our behavior based on previous experiences, particularly, acquired stimulus-outcome information. The orbitofrontal cortex (OFC), a prefrontal cortical region, is critical for this type of decision-making. The current understanding of the fundamental functions of the OFC has been reviewed by introducing, as an example, how the OFC contributes to the processing of uncertain rewards. Furthermore, the importance of revealing context and temporally specific causal roles of neural circuits including the OFC in decision-making, as well as the techniques to achieve the goal, have been discussed.

Item Memory, Context Memory and the Hippocampus: fMRI Evidence

ERIC Educational Resources Information Center

Rugg, Michael D.; Vilberg, Kaia L.; Mattson, Julia T.; Yu, Sarah S.; Johnson, Jeffrey D.; Suzuki, Maki

2012-01-01

Dual-process models of recognition memory distinguish between the retrieval of qualitative information about a prior event (recollection), and judgments of prior occurrence based on an acontextual sense of familiarity. fMRI studies investigating the neural correlates of memory encoding and retrieval conducted within the dual-process framework have…

The neural subjective frame: from bodily signals to perceptual consciousness

PubMed Central

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-01-01

The report ‘I saw the stimulus’ operationally defines visual consciousness, but where does the ‘I’ come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness. PMID:24639580

The neural subjective frame: from bodily signals to perceptual consciousness.

PubMed

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Anthropic Correction of Information Estimates and Its Application to Neural Coding

PubMed Central

Gastpar, Michael C.; Gill, Patrick R.; Huth, Alexander G.; Theunissen, Frédéric E.

2015-01-01

Information theory has been used as an organizing principle in neuroscience for several decades. Estimates of the mutual information (MI) between signals acquired in neurophysiological experiments are believed to yield insights into the structure of the underlying information processing architectures. With the pervasive availability of recordings from many neurons, several information and redundancy measures have been proposed in the recent literature. A typical scenario is that only a small number of stimuli can be tested, while ample response data may be available for each of the tested stimuli. The resulting asymmetric information estimation problem is considered. It is shown that the direct plug-in information estimate has a negative bias. An anthropic correction is introduced that has a positive bias. These two complementary estimators and their combinations are natural candidates for information estimation in neuroscience. Tail and variance bounds are given for both estimates. The proposed information estimates are applied to the analysis of neural discrimination and redundancy in the avian auditory system. PMID:26900172

Anthropic Correction of Information Estimates and Its Application to Neural Coding.

PubMed

Gastpar, Michael C; Gill, Patrick R; Huth, Alexander G; Theunissen, Frédéric E

2010-02-01

Information theory has been used as an organizing principle in neuroscience for several decades. Estimates of the mutual information (MI) between signals acquired in neurophysiological experiments are believed to yield insights into the structure of the underlying information processing architectures. With the pervasive availability of recordings from many neurons, several information and redundancy measures have been proposed in the recent literature. A typical scenario is that only a small number of stimuli can be tested, while ample response data may be available for each of the tested stimuli. The resulting asymmetric information estimation problem is considered. It is shown that the direct plug-in information estimate has a negative bias. An anthropic correction is introduced that has a positive bias. These two complementary estimators and their combinations are natural candidates for information estimation in neuroscience. Tail and variance bounds are given for both estimates. The proposed information estimates are applied to the analysis of neural discrimination and redundancy in the avian auditory system.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

PubMed

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Machine Vision Within The Framework Of Collective Neural Assemblies

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1990-03-01

The proposed mechanism for designing a robust machine vision system is based on the dynamic activity generated by the various neural populations embedded in nervous tissue. It is postulated that a hierarchy of anatomically distinct tissue regions are involved in visual sensory information processing. Each region may be represented as a planar sheet of densely interconnected neural circuits. Spatially localized aggregates of these circuits represent collective neural assemblies. Four dynamically coupled neural populations are assumed to exist within each assembly. In this paper we present a state-variable model for a tissue sheet derived from empirical studies of population dynamics. Each population is modelled as a nonlinear second-order system. It is possible to emulate certain observed physiological and psychophysiological phenomena of biological vision by properly programming the interconnective gains . Important early visual phenomena such as temporal and spatial noise insensitivity, contrast sensitivity and edge enhancement will be discussed for a one-dimensional tissue model.

A Double Dissociation between Anterior and Posterior Superior Temporal Gyrus for Processing Audiovisual Speech Demonstrated by Electrocorticography

PubMed Central

Ozker, Muge; Schepers, Inga M.; Magnotti, John F.; Yoshor, Daniel; Beauchamp, Michael S.

2017-01-01

Human speech can be comprehended using only auditory information from the talker’s voice. However, comprehension is improved if the talker’s face is visible, especially if the auditory information is degraded as occurs in noisy environments or with hearing loss. We explored the neural substrates of audiovisual speech perception using electrocorticography, direct recording of neural activity using electrodes implanted on the cortical surface. We observed a double dissociation in the responses to audiovisual speech with clear and noisy auditory component within the superior temporal gyrus (STG), a region long known to be important for speech perception. Anterior STG showed greater neural activity to audiovisual speech with clear auditory component, whereas posterior STG showed similar or greater neural activity to audiovisual speech in which the speech was replaced with speech-like noise. A distinct border between the two response patterns was observed, demarcated by a landmark corresponding to the posterior margin of Heschl’s gyrus. To further investigate the computational roles of both regions, we considered Bayesian models of multisensory integration, which predict that combining the independent sources of information available from different modalities should reduce variability in the neural responses. We tested this prediction by measuring the variability of the neural responses to single audiovisual words. Posterior STG showed smaller variability than anterior STG during presentation of audiovisual speech with noisy auditory component. Taken together, these results suggest that posterior STG but not anterior STG is important for multisensory integration of noisy auditory and visual speech. PMID:28253074

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

2015-03-01

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

Altered behavioral and neural responsiveness to counterfactual gains in the elderly.

PubMed

Tobia, Michael J; Guo, Rong; Gläscher, Jan; Schwarze, Ulrike; Brassen, Stefanie; Büchel, Christian; Obermayer, Klaus; Sommer, Tobias

2016-06-01

Counterfactual information processing refers to the consideration of events that did not occur in comparison to those actually experienced, in order to determine optimal actions, and can be formulated as computational learning signals, referred to as fictive prediction errors. Decision making and the neural circuitry for counterfactual processing are altered in healthy elderly adults. This experiment investigated age differences in neural systems for decision making with knowledge of counterfactual outcomes. Two groups of healthy adult participants, young (N = 30; ages 19-30 years) and elderly (N = 19; ages 65-80 years), were scanned with fMRI during 240 trials of a strategic sequential investment task in which a particular strategy of differentially weighting counterfactual gains and losses during valuation is associated with more optimal performance. Elderly participants earned significantly less than young adults, differently weighted counterfactual consequences and exploited task knowledge, and exhibited altered activity in a fronto-striatal circuit while making choices, compared to young adults. The degree to which task knowledge was exploited was positively correlated with modulation of neural activity by expected value in the vmPFC for young adults, but not in the elderly. These findings demonstrate that elderly participants' poor task performance may be related to different counterfactual processing.

Neural Correlates of Verb Argument Structure Processing

PubMed Central

Thompson, Cynthia K.; Bonakdarpour, Borna; Fix, Stephen C.; Blumenfeld, Henrike K.; Parrish, Todd B.; Gitelman, Darren R.; Mesulam, M.-Marsel

2008-01-01

Neuroimaging and lesion studies suggest that processing of word classes, such as verbs and nouns, is associated with distinct neural mechanisms. Such studies also suggest that subcategories within these broad word class categories are differentially processed in the brain. Within the class of verbs, argument structure provides one linguistic dimension that distinguishes among verb exemplars, with some requiring more complex argument structure entries than others. This study examined the neural instantiation of verbs by argument structure complexity: one-, two-, and three-argument verbs. Stimuli of each type, along with nouns and pseudowords, were presented for lexical decision using an event-related functional magnetic resonance imaging design. Results for 14 young normal participants indicated largely overlapping activation maps for verbs and nouns, with no areas of significant activation for verbs compared to nouns, or vice versa. Pseudowords also engaged neural tissue overlapping with that for both word classes, with more widespread activation noted in visual, motor, and peri-sylvian regions. Examination of verbs by argument structure revealed activation of the supramarginal and angular gyri, limited to the left hemisphere only when verbs with two obligatory arguments were compared to verbs with a single argument. However, bilateral activation was noted when both two- and three-argument verbs were compared to one-argument verbs. These findings suggest that posterior peri-sylvian regions are engaged for processing argument structure information associated with verbs, with increasing neural tissue in the inferior parietal region associated with increasing argument structure complexity. These findings are consistent with processing accounts, which suggest that these regions are crucial for semantic integration. PMID:17958479

Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method

NASA Astrophysics Data System (ADS)

Tselentis, G.-A.; Sokos, E.

2012-01-01

In this paper we suggest the use of diffusion-neural-networks, (neural networks with intrinsic fuzzy logic abilities) to assess the relationship between isoseismal area and earthquake magnitude for the region of Greece. It is of particular importance to study historical earthquakes for which we often have macroseismic information in the form of isoseisms but it is statistically incomplete to assess magnitudes from an isoseismal area or to train conventional artificial neural networks for magnitude estimation. Fuzzy relationships are developed and used to train a feed forward neural network with a back propagation algorithm to obtain the final relationships. Seismic intensity data from 24 earthquakes in Greece have been used. Special attention is being paid to the incompleteness and contradictory patterns in scanty historical earthquake records. The results show that the proposed processing model is very effective, better than applying classical artificial neural networks since the magnitude macroseismic intensity target function has a strong nonlinearity and in most cases the macroseismic datasets are very small.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

PubMed Central

2018-01-01

Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. PMID:29537963

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

PubMed

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

ANNarchy: a code generation approach to neural simulations on parallel hardware

PubMed Central

Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

2015-01-01

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

PubMed

Bast, Tobias; Pezze, Marie; McGarrity, Stephanie

2017-10-01

We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition contributes to clinically relevant cognitive deficits, and we consider pharmacological strategies for ameliorating cognitive deficits by rebalancing disinhibition-induced aberrant neural activity. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc. © 2017 The British Pharmacological Society.

A unified 3D default space consciousness model combining neurological and physiological processes that underlie conscious experience

PubMed Central

Jerath, Ravinder; Crawford, Molly W.; Barnes, Vernon A.

2015-01-01

The Global Workspace Theory and Information Integration Theory are two of the most currently accepted consciousness models; however, these models do not address many aspects of conscious experience. We compare these models to our previously proposed consciousness model in which the thalamus fills-in processed sensory information from corticothalamic feedback loops within a proposed 3D default space, resulting in the recreation of the internal and external worlds within the mind. This 3D default space is composed of all cells of the body, which communicate via gap junctions and electrical potentials to create this unified space. We use 3D illustrations to explain how both visual and non-visual sensory information may be filled-in within this dynamic space, creating a unified seamless conscious experience. This neural sensory memory space is likely generated by baseline neural oscillatory activity from the default mode network, other salient networks, brainstem, and reticular activating system. PMID:26379573

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

PubMed Central

2014-01-01

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility. PMID:25276860

The Predictive Brain State: Asynchrony in Disorders of Attention?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

It is postulated that a key function of attention in goal-oriented behavior is to reduce performance variability by generating anticipatory neural activity that can be synchronized with expected sensory information. A network encompassing the prefrontal cortex, parietal lobe, and cerebellum may be critical in the maintenance and timing of such predictive neural activity. Dysfunction of this temporal process may constitute a fundamental defect in attention, causing working memory problems, distractibility, and decreased awareness. PMID:19074688

Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

PubMed

Jiang, Jiefeng; Egner, Tobias

2014-07-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

A positron emission tomography study of the neural basis of informational and energetic masking effects in speech perception

NASA Astrophysics Data System (ADS)

Scott, Sophie K.; Rosen, Stuart; Wickham, Lindsay; Wise, Richard J. S.

2004-02-01

Positron emission tomography (PET) was used to investigate the neural basis of the comprehension of speech in unmodulated noise (``energetic'' masking, dominated by effects at the auditory periphery), and when presented with another speaker (``informational'' masking, dominated by more central effects). Each type of signal was presented at four different signal-to-noise ratios (SNRs) (+3, 0, -3, -6 dB for the speech-in-speech, +6, +3, 0, -3 dB for the speech-in-noise), with listeners instructed to listen for meaning to the target speaker. Consistent with behavioral studies, there was SNR-dependent activation associated with the comprehension of speech in noise, with no SNR-dependent activity for the comprehension of speech-in-speech (at low or negative SNRs). There was, in addition, activation in bilateral superior temporal gyri which was associated with the informational masking condition. The extent to which this activation of classical ``speech'' areas of the temporal lobes might delineate the neural basis of the informational masking is considered, as is the relationship of these findings to the interfering effects of unattended speech and sound on more explicit working memory tasks. This study is a novel demonstration of candidate neural systems involved in the perception of speech in noisy environments, and of the processing of multiple speakers in the dorso-lateral temporal lobes.

Mapping soil landscape as spatial continua: The Neural Network Approach

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing

2000-03-01

A neural network approach was developed to populate a soil similarity model that was designed to represent soil landscape as spatial continua for hydroecological modeling at watersheds of mesoscale size. The approach employs multilayer feed forward neural networks. The input to the network was data on a set of soil formative environmental factors; the output from the network was a set of similarity values to a set of prescribed soil classes. The network was trained using a conjugate gradient algorithm in combination with a simulated annealing technique to learn the relationships between a set of prescribed soils and their environmental factors. Once trained, the network was used to compute for every location in an area the similarity values of the soil to the set of prescribed soil classes. The similarity values were then used to produce detailed soil spatial information. The approach also included a Geographic Information System procedure for selecting representative training and testing samples and a process of determining the network internal structure. The approach was applied to soil mapping in a watershed, the Lubrecht Experimental Forest, in western Montana. The case study showed that the soil spatial information derived using the neural network approach reveals much greater spatial detail and has a higher quality than that derived from the conventional soil map. Implications of this detailed soil spatial information for hydroecological modeling at the watershed scale are also discussed.

Step to improve neural cryptography against flipping attacks.

PubMed

Zhou, Jiantao; Xu, Qinzhen; Pei, Wenjiang; He, Zhenya; Szu, Harold

2004-12-01

Synchronization of neural networks by mutual learning has been demonstrated to be possible for constructing key exchange protocol over public channel. However, the neural cryptography schemes presented so far are not the securest under regular flipping attack (RFA) and are completely insecure under majority flipping attack (MFA). We propose a scheme by splitting the mutual information and the training process to improve the security of neural cryptosystem against flipping attacks. Both analytical and simulation results show that the success probability of RFA on the proposed scheme can be decreased to the level of brute force attack (BFA) and the success probability of MFA still decays exponentially with the weights' level L. The synchronization time of the parties also remains polynomial with L. Moreover, we analyze the security under an advanced flipping attack.

Prior perceptual processing enhances the effect of emotional arousal on the neural correlates of memory retrieval

PubMed Central

Dew, Ilana T. Z.; Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto

2014-01-01

A fundamental idea in memory research is that items are more likely to be remembered if encoded with a semantic, rather than perceptual, processing strategy. Interestingly, this effect has been shown to reverse for emotionally arousing materials, such that perceptual processing enhances memory for emotional information or events. The current fMRI study investigated the neural mechanisms of this effect by testing how neural activations during emotional memory retrieval are influenced by the prior encoding strategy. Participants incidentally encoded emotional and neutral pictures under instructions to attend to either semantic or perceptual properties of each picture. Recognition memory was tested two days later. fMRI analyses yielded three main findings. First, right amygdalar activity associated with emotional memory strength was enhanced by prior perceptual processing. Second, prior perceptual processing of emotional pictures produced a stronger effect on recollection- than familiarity-related activations in the right amygdala and left hippocampus. Finally, prior perceptual processing enhanced amygdalar connectivity with regions strongly associated with retrieval success, including hippocampal/parahippocampal regions, visual cortex, and ventral parietal cortex. Taken together, the results specify how encoding orientations yield alterations in brain systems that retrieve emotional memories. PMID:24380867

How the amygdala affects emotional memory by altering brain network properties.

PubMed

Hermans, Erno J; Battaglia, Francesco P; Atsak, Piray; de Voogd, Lycia D; Fernández, Guillén; Roozendaal, Benno

2014-07-01

The amygdala has long been known to play a key role in supporting memory for emotionally arousing experiences. For example, classical fear conditioning depends on neural plasticity within this anterior medial temporal lobe region. Beneficial effects of emotional arousal on memory, however, are not restricted to simple associative learning. Our recollection of emotional experiences often includes rich representations of, e.g., spatiotemporal context, visceral states, and stimulus-response associations. Critically, such memory features are known to bear heavily on regions elsewhere in the brain. These observations led to the modulation account of amygdala function, which postulates that amygdala activation enhances memory consolidation by facilitating neural plasticity and information storage processes in its target regions. Rodent work in past decades has identified the most important brain regions and neurochemical processes involved in these modulatory actions, and neuropsychological and neuroimaging work in humans has produced a large body of convergent data. Importantly, recent methodological developments make it increasingly realistic to monitor neural interactions underlying such modulatory effects as they unfold. For instance, functional connectivity network modeling in humans has demonstrated how information exchanges between the amygdala and specific target regions occur within the context of large-scale neural network interactions. Furthermore, electrophysiological and optogenetic techniques in rodents are beginning to make it possible to quantify and even manipulate such interactions with millisecond precision. In this paper we will discuss that these developments will likely lead to an updated view of the amygdala as a critical nexus within large-scale networks supporting different aspects of memory processing for emotionally arousing experiences. Copyright © 2014 Elsevier Inc. All rights reserved.

Congenital prosopagnosia: face-blind from birth.

PubMed

Behrmann, Marlene; Avidan, Galia

2005-04-01

Congenital prosopagnosia refers to the deficit in face processing that is apparent from early childhood in the absence of any underlying neurological basis and in the presence of intact sensory and intellectual function. Several such cases have been described recently and elucidating the mechanisms giving rise to this impairment should aid our understanding of the psychological and neural mechanisms mediating face processing. Fundamental questions include: What is the nature and extent of the face-processing deficit in congenital prosopagnosia? Is the deficit related to a more general perceptual deficit such as the failure to process configural information? Are any neural alterations detectable using fMRI, ERP or structural analyses of the anatomy of the ventral visual cortex? We discuss these issues in relation to the existing literature and suggest directions for future research.

LSTM-CRF | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

LSTM-CRF uses Natural Language Processing methods for detecting Adverse Drug Events, Drugname, Indication and other medically relevant information from Electronic Health Records. It implements Recurrent Neural Networks using several CRF based inference methods.

High-Density Stretchable Electrode Grids for Chronic Neural Recording

PubMed Central

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F.; Buzsáki, György; Vörös, János

2018-01-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. PMID:29488263

Top-Down Inhibition of BMP Signaling Enables Robust Induction of hPSCs Into Neural Crest in Fully Defined, Xeno-free Conditions.

PubMed

Hackland, James O S; Frith, Tom J R; Thompson, Oliver; Marin Navarro, Ana; Garcia-Castro, Martin I; Unger, Christian; Andrews, Peter W

2017-10-10

Defects in neural crest development have been implicated in many human disorders, but information about human neural crest formation mostly depends on extrapolation from model organisms. Human pluripotent stem cells (hPSCs) can be differentiated into in vitro counterparts of the neural crest, and some of the signals known to induce neural crest formation in vivo are required during this process. However, the protocols in current use tend to produce variable results, and there is no consensus as to the precise signals required for optimal neural crest differentiation. Using a fully defined culture system, we have now found that the efficient differentiation of hPSCs to neural crest depends on precise levels of BMP signaling, which are vulnerable to fluctuations in endogenous BMP production. We present a method that controls for this phenomenon and could be applied to other systems where endogenous signaling can also affect the outcome of differentiation protocols. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing.

PubMed

Hu, Bin; Yue, Shigang; Zhang, Zhuhong

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

About turn: how object orientation affects categorisation and mental rotation.

PubMed

Milivojevic, Branka; Hamm, Jeff P; Corballis, Michael C

2011-11-01

High-density ERPs evoked by rotated alphanumeric characters were examined to determine how neural processing is affected by stimulus orientation during letter/digit classifications and during mirror/normal discriminations. The former task typically produces response times that are unaffected by stimulus orientation while the latter is thought to require mental rotation. Sensitivity to orientation was first observed around 100-140 ms and this effect was attributed to differences in low-level features between vertical and oblique orientations. Subsequently, character misorientation amplified the N170, a neural marker of object classification, between 160 and 220 ms. Top-down processing is reflected in the ERPs beginning at 280-320 ms and this time range may reflect binding of ventral and dorsal stream information. In the case of mirror-normal discrimination these top-down processes can lead to mental rotation between 340 and 700 ms. Therefore, although neural processing reflects object orientation, these effects do not translate into increases in reaction-times or impaired accuracy for categorisation, and precede those that do in the mental-rotation task. Copyright © 2011 Elsevier Ltd. All rights reserved.

Perceptions as Hypotheses

NASA Astrophysics Data System (ADS)

Gregory, R. L.

1980-07-01

Perceptions may be compared with hypotheses in science. The methods of acquiring scientific knowledge provide a working paradigm for investigating processes of perception. Much as the information channels of instruments, such as radio telescopes, transmit signals which are processed according to various assumptions to give useful data, so neural signals are processed to give data for perception. To understand perception, the signal codes and the stored knowledge or assumptions used for deriving perceptual hypotheses must be discovered. Systematic perceptual errors are important clues for appreciating signal channel limitations, and for discovering hypothesis-generating procedures. Although this distinction between `physiological' and `cognitive' aspects of perception may be logically clear, it is in practice surprisingly difficult to establish which are responsible even for clearly established phenomena such as the classical distortion illusions. Experimental results are presented, aimed at distinguishing between and discovering what happens when there is mismatch with the neural signal channel, and when neural signals are processed inappropriately for the current situation. This leads us to make some distinctions between perceptual and scientific hypotheses, which raise in a new form the problem: What are `objects'?

From emotion resonance to empathic understanding: a social developmental neuroscience account.

PubMed

Decety, Jean; Meyer, Meghan

2008-01-01

The psychological construct of empathy refers to an intersubjective induction process by which positive and negative emotions are shared, without losing sight of whose feelings belong to whom. Empathy can lead to personal distress or to empathic concern (sympathy). The goal of this paper is to address the underlying cognitive processes and their neural underpinnings that constitute empathy within a developmental neuroscience perspective. In addition, we focus on how these processes go awry in developmental disorders marked by impairments in social cognition, such as autism spectrum disorder, and conduct disorder. We argue that empathy involves both bottom-up and top-down information processing, underpinned by specific and interacting neural systems. We discuss data from developmental psychology as well as cognitive neuroscience in support of such a model, and highlight the impact of neural dysfunctions on social cognitive developmental behavior. Altogether, bridging developmental science and cognitive neuroscience helps approach a more complete understanding of social cognition. Synthesizing these two domains also contributes to a better characterization of developmental psychopathologies that impacts the development of effective treatment strategies.

Age-Related Neural Oscillation Patterns During the Processing of Temporally Manipulated Speech.

PubMed

Rufener, Katharina S; Oechslin, Mathias S; Wöstmann, Malte; Dellwo, Volker; Meyer, Martin

2016-05-01

This EEG-study aims to investigate age-related differences in the neural oscillation patterns during the processing of temporally modulated speech. Viewing from a lifespan perspective, we recorded the electroencephalogram (EEG) data of three age samples: young adults, middle-aged adults and older adults. Stimuli consisted of temporally degraded sentences in Swedish-a language unfamiliar to all participants. We found age-related differences in phonetic pattern matching when participants were presented with envelope-degraded sentences, whereas no such age-effect was observed in the processing of fine-structure-degraded sentences. Irrespective of age, during speech processing the EEG data revealed a relationship between envelope information and the theta band (4-8 Hz) activity. Additionally, an association between fine-structure information and the gamma band (30-48 Hz) activity was found. No interaction, however, was found between acoustic manipulation of stimuli and age. Importantly, our main finding was paralleled by an overall enhanced power in older adults in high frequencies (gamma: 30-48 Hz). This occurred irrespective of condition. For the most part, this result is in line with the Asymmetric Sampling in Time framework (Poeppel in Speech Commun 41:245-255, 2003), which assumes an isomorphic correspondence between frequency modulations in neurophysiological patterns and acoustic oscillations in spoken language. We conclude that speech-specific neural networks show strong stability over adulthood, despite initial processes of cortical degeneration indicated by enhanced gamma power. The results of our study therefore confirm the concept that sensory and cognitive processes undergo multidirectional trajectories within the context of healthy aging.

Neuro-inspired smart image sensor: analog Hmax implementation

NASA Astrophysics Data System (ADS)

Paindavoine, Michel; Dubois, Jérôme; Musa, Purnawarman

2015-03-01

Neuro-Inspired Vision approach, based on models from biology, allows to reduce the computational complexity. One of these models - The Hmax model - shows that the recognition of an object in the visual cortex mobilizes V1, V2 and V4 areas. From the computational point of view, V1 corresponds to the area of the directional filters (for example Sobel filters, Gabor filters or wavelet filters). This information is then processed in the area V2 in order to obtain local maxima. This new information is then sent to an artificial neural network. This neural processing module corresponds to area V4 of the visual cortex and is intended to categorize objects present in the scene. In order to realize autonomous vision systems (consumption of a few milliwatts) with such treatments inside, we studied and realized in 0.35μm CMOS technology prototypes of two image sensors in order to achieve the V1 and V2 processing of Hmax model.

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ericson, Milton Nance; McKnight, Timothy E; Melechko, Anatoli Vasilievich

2012-01-01

Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information ofmore » neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.« less

How Awareness Changes the Relative Weights of Evidence During Human Decision-Making

PubMed Central

Lamme, Victor A. F.; Dehaene, Stanislas

2011-01-01

Human decisions are based on accumulating evidence over time for different options. Here we ask a simple question: How is the accumulation of evidence affected by the level of awareness of the information? We examined the influence of awareness on decision-making using combined behavioral methods and magneto-encephalography (MEG). Participants were required to make decisions by accumulating evidence over a series of visually presented arrow stimuli whose visibility was modulated by masking. Behavioral results showed that participants could accumulate evidence under both high and low visibility. However, a top-down strategic modulation of the flow of incoming evidence was only present for stimuli with high visibility: once enough evidence had been accrued, participants strategically reduced the impact of new incoming stimuli. Also, decision-making speed and confidence were strongly modulated by the strength of the evidence for high-visible but not low-visible evidence, even though direct priming effects were identical for both types of stimuli. Neural recordings revealed that, while initial perceptual processing was independent of visibility, there was stronger top-down amplification for stimuli with high visibility than low visibility. Furthermore, neural markers of evidence accumulation over occipito-parietal cortex showed a strategic bias only for highly visible sensory information, speeding up processing and reducing neural computations related to the decision process. Our results indicate that the level of awareness of information changes decision-making: while accumulation of evidence already exists under low visibility conditions, high visibility allows evidence to be accumulated up to a higher level, leading to important strategical top-down changes in decision-making. Our results therefore suggest a potential role of awareness in deploying flexible strategies for biasing information acquisition in line with one's expectations and goals. PMID:22131904

How awareness changes the relative weights of evidence during human decision-making.

PubMed

de Lange, Floris P; van Gaal, Simon; Lamme, Victor A F; Dehaene, Stanislas

2011-11-01

Human decisions are based on accumulating evidence over time for different options. Here we ask a simple question: How is the accumulation of evidence affected by the level of awareness of the information? We examined the influence of awareness on decision-making using combined behavioral methods and magneto-encephalography (MEG). Participants were required to make decisions by accumulating evidence over a series of visually presented arrow stimuli whose visibility was modulated by masking. Behavioral results showed that participants could accumulate evidence under both high and low visibility. However, a top-down strategic modulation of the flow of incoming evidence was only present for stimuli with high visibility: once enough evidence had been accrued, participants strategically reduced the impact of new incoming stimuli. Also, decision-making speed and confidence were strongly modulated by the strength of the evidence for high-visible but not low-visible evidence, even though direct priming effects were identical for both types of stimuli. Neural recordings revealed that, while initial perceptual processing was independent of visibility, there was stronger top-down amplification for stimuli with high visibility than low visibility. Furthermore, neural markers of evidence accumulation over occipito-parietal cortex showed a strategic bias only for highly visible sensory information, speeding up processing and reducing neural computations related to the decision process. Our results indicate that the level of awareness of information changes decision-making: while accumulation of evidence already exists under low visibility conditions, high visibility allows evidence to be accumulated up to a higher level, leading to important strategical top-down changes in decision-making. Our results therefore suggest a potential role of awareness in deploying flexible strategies for biasing information acquisition in line with one's expectations and goals.

Towards a neural basis of music perception.

PubMed

Koelsch, Stefan; Siebel, Walter A

2005-12-01

Music perception involves complex brain functions underlying acoustic analysis, auditory memory, auditory scene analysis, and processing of musical syntax and semantics. Moreover, music perception potentially affects emotion, influences the autonomic nervous system, the hormonal and immune systems, and activates (pre)motor representations. During the past few years, research activities on different aspects of music processing and their neural correlates have rapidly progressed. This article provides an overview of recent developments and a framework for the perceptual side of music processing. This framework lays out a model of the cognitive modules involved in music perception, and incorporates information about the time course of activity of some of these modules, as well as research findings about where in the brain these modules might be located.

A Goal Direction Signal in the Human Entorhinal/Subicular Region

PubMed Central

Chadwick, Martin J.; Jolly, Amy E.J.; Amos, Doran P.; Hassabis, Demis; Spiers, Hugo J.

2015-01-01

Summary Navigating to a safe place, such as a home or nest, is a fundamental behavior for all complex animals. Determining the direction to such goals is a crucial first step in navigation. Surprisingly, little is known about how or where in the brain this “goal direction signal” is represented. In mammals, “head-direction cells” are thought to support this process, but despite 30 years of research, no evidence for a goal direction representation has been reported [1, 2]. Here, we used fMRI to record neural activity while participants made goal direction judgments based on a previously learned virtual environment. We applied multivoxel pattern analysis [3–5] to these data and found that the human entorhinal/subicular region contains a neural representation of intended goal direction. Furthermore, the neural pattern expressed for a given goal direction matched the pattern expressed when simply facing that same direction. This suggests the existence of a shared neural representation of both goal and facing direction. We argue that this reflects a mechanism based on head-direction populations that simulate future goal directions during route planning [6]. Our data further revealed that the strength of direction information predicts performance. Finally, we found a dissociation between this geocentric information in the entorhinal/subicular region and egocentric direction information in the precuneus. PMID:25532898

Memorable Audiovisual Narratives Synchronize Sensory and Supramodal Neural Responses

PubMed Central

2016-01-01

Abstract Our brains integrate information across sensory modalities to generate perceptual experiences and form memories. However, it is difficult to determine the conditions under which multisensory stimulation will benefit or hinder the retrieval of everyday experiences. We hypothesized that the determining factor is the reliability of information processing during stimulus presentation, which can be measured through intersubject correlation of stimulus-evoked activity. We therefore presented biographical auditory narratives and visual animations to 72 human subjects visually, auditorily, or combined, while neural activity was recorded using electroencephalography. Memory for the narrated information, contained in the auditory stream, was tested 3 weeks later. While the visual stimulus alone led to no meaningful retrieval, this related stimulus improved memory when it was combined with the story, even when it was temporally incongruent with the audio. Further, individuals with better subsequent memory elicited neural responses during encoding that were more correlated with their peers. Surprisingly, portions of this predictive synchronized activity were present regardless of the sensory modality of the stimulus. These data suggest that the strength of sensory and supramodal activity is predictive of memory performance after 3 weeks, and that neural synchrony may explain the mnemonic benefit of the functionally uninformative visual context observed for these real-world stimuli. PMID:27844062

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.

PubMed

Yu, Zhe; McKnight, Timothy E; Ericson, M Nance; Melechko, Anatoli V; Simpson, Michael L; Morrison, Barclay

2012-05-01

Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell. The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities. Copyright © 2012 Elsevier Inc. All rights reserved.

Facial expressions perceived by the adolescent brain: Towards the proficient use of low spatial frequency information.

PubMed

Peters, Judith C; Kemner, Chantal

2017-10-01

Rapid decoding of emotional expressions is essential for social communication. Fast processing of facial expressions depends on the adequate (subcortical) processing of important global face cues in the low spatial frequency (LSF) ranges. However, children below 9 years of age extract fearful expression information from local details represented by high SF (HSF) image content. Our ERP study investigated at which developmental stage this ineffective HSF-driven processing is replaced by the proficient and rapid LSF-driven perception of fearful faces, in which adults are highly skilled. We examined behavioral and neural responses to high- and low-pass filtered faces with a fearful or neutral expression in groups of children on the verge of pre-adolescence (9-10 years), adolescents (14-15 years), and young adults (20-28 years). Our results suggest that the neural emotional face processing network has a protracted maturational course into adolescence, which is related to changes in SF processing. In mid-adolescence, increased sensitivity to emotional LSF cues is developed, which aids the fast and adequate processing of fearful expressions that might signal impending danger. Copyright © 2017 Elsevier B.V. All rights reserved.

Musical intervention enhances infants’ neural processing of temporal structure in music and speech

PubMed Central

Zhao, T. Christina; Kuhl, Patricia K.

2016-01-01

Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants’ neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants’ neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants’ neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants’ ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing. PMID:27114512

Musical intervention enhances infants' neural processing of temporal structure in music and speech.

PubMed

Zhao, T Christina; Kuhl, Patricia K

2016-05-10

Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants' neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants' neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants' neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants' ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing.

Image texture segmentation using a neural network

NASA Astrophysics Data System (ADS)

Sayeh, Mohammed R.; Athinarayanan, Ragu; Dhali, Pushpuak

1992-09-01

In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.

Vicarious Neural Processing of Outcomes during Observational Learning

PubMed Central

Monfardini, Elisabetta; Gazzola, Valeria; Boussaoud, Driss

2013-01-01

Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others’ incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS). PMID:24040104

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity

PubMed Central

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities–0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking. PMID:28337137

Neural Correlates of Audiovisual Integration of Semantic Category Information

ERIC Educational Resources Information Center

Hu, Zhonghua; Zhang, Ruiling; Zhang, Qinglin; Liu, Qiang; Li, Hong

2012-01-01

Previous studies have found a late frontal-central audiovisual interaction during the time period about 150-220 ms post-stimulus. However, it is unclear to which process is this audiovisual interaction related: to processing of acoustical features or to classification of stimuli? To investigate this question, event-related potentials were recorded…

Stuttering and Natural Speech Processing of Semantic and Syntactic Constraints on Verbs

ERIC Educational Resources Information Center

Weber-Fox, Christine; Hampton, Amanda

2008-01-01

Purpose: Previous findings from event-related brain potentials (ERPs) indicate that adults who stutter (AWS) exhibit processing differences for visually presented linguistic information. This study explores how neural activations for AWS may differ for a linguistic task that does not require preparation for overt articulation or engage the…

Prefrontal neural correlates of memory for sequences.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2007-02-28

The sequence of actions appropriate to solve a problem often needs to be discovered by trial and error and recalled in the future when faced with the same problem. Here, we show that when monkeys had to discover and then remember a sequence of decisions across trials, ensembles of prefrontal cortex neurons reflected the sequence of decisions the animal would make throughout the interval between trials. This signal could reflect either an explicit memory process or a sequence-planning process that begins far in advance of the actual sequence execution. This finding extended to error trials such that, when the neural activity during the intertrial interval specified the wrong sequence, the animal also attempted to execute an incorrect sequence. More specifically, we used a decoding analysis to predict the sequence the monkey was planning to execute at the end of the fore-period, just before sequence execution. When this analysis was applied to error trials, we were able to predict where in the sequence the error would occur, up to three movements into the future. This suggests that prefrontal neural activity can retain information about sequences between trials, and that regardless of whether information is remembered correctly or incorrectly, the prefrontal activity veridically reflects the animal's action plan.

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Reward-related neural responses are dependent on the beneficiary

PubMed Central

Güroğlu, Berna; de Water, Erik; Meuwese, Rosa; Koolschijn, P. Cédric; Peper, Jiska S.; Crone, Eveline A.

2014-01-01

Prior studies have suggested that positive social interactions are experienced as rewarding. Yet, it is not well understood how social relationships influence neural responses to other persons’ gains. In this study, we investigated neural responses during a gambling task in which healthy participants (N = 31; 18 females) could win or lose money for themselves, their best friend or a disliked other (antagonist). At the moment of receiving outcome, person-related activity was observed in the dorsal medial prefrontal cortex (dmPFC), precuneus and temporal parietal junction (TPJ), showing higher activity for friends and antagonists than for self, and this activity was independent of outcome. The only region showing an interaction between the person-participants played for and outcome was the ventral striatum. Specifically, the striatum was more active following gains than losses for self and friends, whereas for the antagonist this pattern was reversed. Together, these results show that, in a context with social and reward information, social aspects are processed in brain regions associated with social cognition (mPFC, TPJ), and reward aspects are processed in primary reward areas (striatum). Furthermore, there is an interaction of social and reward information in the striatum, such that reward-related activity was dependent on social relationship. PMID:23720575

Near-instant automatic access to visually presented words in the human neocortex: neuromagnetic evidence.

PubMed

Shtyrov, Yury; MacGregor, Lucy J

2016-05-24

Rapid and efficient processing of external information by the brain is vital to survival in a highly dynamic environment. The key channel humans use to exchange information is language, but the neural underpinnings of its processing are still not fully understood. We investigated the spatio-temporal dynamics of neural access to word representations in the brain by scrutinising the brain's activity elicited in response to psycholinguistically, visually and phonologically matched groups of familiar words and meaningless pseudowords. Stimuli were briefly presented on the visual-field periphery to experimental participants whose attention was occupied with a non-linguistic visual feature-detection task. The neural activation elicited by these unattended orthographic stimuli was recorded using multi-channel whole-head magnetoencephalography, and the timecourse of lexically-specific neuromagnetic responses was assessed in sensor space as well as at the level of cortical sources, estimated using individual MR-based distributed source reconstruction. Our results demonstrate a neocortical signature of automatic near-instant access to word representations in the brain: activity in the perisylvian language network characterised by specific activation enhancement for familiar words, starting as early as ~70 ms after the onset of unattended word stimuli and underpinned by temporal and inferior-frontal cortices.

Cognitive and neural consequences of memory suppression in major depressive disorder.

PubMed

Sacchet, Matthew D; Levy, Benjamin J; Hamilton, J Paul; Maksimovskiy, Arkadiy; Hertel, Paula T; Joormann, Jutta; Anderson, Michael C; Wagner, Anthony D; Gotlib, Ian H

2017-02-01

Negative biases in cognition have been documented consistently in major depressive disorder (MDD), including difficulties in the ability to control the processing of negative material. Although negative information-processing biases have been studied using both behavioral and neuroimaging paradigms, relatively little research has been conducted examining the difficulties of depressed persons with inhibiting the retrieval of negative information from long-term memory. In this study, we used the think/no-think paradigm and functional magnetic resonance imaging to assess the cognitive and neural consequences of memory suppression in individuals diagnosed with depression and in healthy controls. The participants showed typical behavioral forgetting effects, but contrary to our hypotheses, there were no differences between the depressed and nondepressed participants or between neutral and negative memories. Relative to controls, depressed individuals exhibited greater activity in right middle frontal gyrus during memory suppression, regardless of the valence of the suppressed stimuli, and differential activity in the amygdala and hippocampus during memory suppression involving negatively valenced stimuli. These findings indicate that depressed individuals are characterized by neural anomalies during the suppression of long-term memories, increasing our understanding of the brain bases of negative cognitive biases in MDD.

Targeted neural network interventions for auditory hallucinations: Can TMS inform DBS?

PubMed

Taylor, Joseph J; Krystal, John H; D'Souza, Deepak C; Gerrard, Jason Lee; Corlett, Philip R

2018-05-01

The debilitating and refractory nature of auditory hallucinations (AH) in schizophrenia and other psychiatric disorders has stimulated investigations into neuromodulatory interventions that target the aberrant neural networks associated with them. Internal or invasive forms of brain stimulation such as deep brain stimulation (DBS) are currently being explored for treatment-refractory schizophrenia. The process of developing and implementing DBS is limited by symptom clustering within psychiatric constructs as well as a scarcity of causal tools with which to predict response, refine targeting or guide clinical decisions. Transcranial magnetic stimulation (TMS), an external or non-invasive form of brain stimulation, has shown some promise as a therapeutic intervention for AH but remains relatively underutilized as an investigational probe of clinically relevant neural networks. In this editorial, we propose that TMS has the potential to inform DBS by adding individualized causal evidence to an evaluation processes otherwise devoid of it in patients. Although there are significant limitations and safety concerns regarding DBS, the combination of TMS with computational modeling of neuroimaging and neurophysiological data could provide critical insights into more robust and adaptable network modulation. Copyright © 2017 Elsevier B.V. All rights reserved.

The neural circuits of innate fear: detection, integration, action, and memorization

PubMed Central

Silva, Bianca A.; Gross, Cornelius T.

2016-01-01

How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

Maladaptive social information processing in childhood predicts young men's atypical amygdala reactivity to threat.

PubMed

Choe, Daniel Ewon; Shaw, Daniel S; Forbes, Erika E

2015-05-01

Maladaptive social information processing, such as hostile attributional bias and aggressive response generation, is associated with childhood maladjustment. Although social information processing problems are correlated with heightened physiological responses to social threat, few studies have examined their associations with neural threat circuitry, specifically amygdala activation to social threat. A cohort of 310 boys participated in an ongoing longitudinal study and completed questionnaires and laboratory tasks assessing their social and cognitive characteristics the boys were between 10 and 12 years of age. At age 20, 178 of these young men underwent functional magnetic resonance imaging and a social threat task. At age 22, adult criminal arrest records and self-reports of impulsiveness were obtained. Path models indicated that maladaptive social information-processing at ages 10 and 11 predicted increased left amygdala reactivity to fear faces, an ambiguous threat, at age 20 while accounting for childhood antisocial behavior, empathy, IQ, and socioeconomic status. Exploratory analyses indicated that aggressive response generation - the tendency to respond to threat with reactive aggression - predicted left amygdala reactivity to fear faces and was concurrently associated with empathy, antisocial behavior, and hostile attributional bias, whereas hostile attributional bias correlated with IQ. Although unrelated to social information-processing problems, bilateral amygdala reactivity to anger faces at age 20 was unexpectedly predicted by low IQ at age 11. Amygdala activation did not mediate associations between social information processing and number of criminal arrests, but both impulsiveness at age 22 and arrests were correlated with right amygdala reactivity to anger facial expressions at age 20. Childhood social information processing and IQ predicted young men's amygdala response to threat a decade later, which suggests that childhood social-cognitive characteristics are associated with the development of neural threat processing and adult adjustment. © 2014 Association for Child and Adolescent Mental Health.

Windows to the soul: vision science as a tool for studying biological mechanisms of information processing deficits in schizophrenia.

PubMed

Yoon, Jong H; Sheremata, Summer L; Rokem, Ariel; Silver, Michael A

2013-10-31

Cognitive and information processing deficits are core features and important sources of disability in schizophrenia. Our understanding of the neural substrates of these deficits remains incomplete, in large part because the complexity of impairments in schizophrenia makes the identification of specific deficits very challenging. Vision science presents unique opportunities in this regard: many years of basic research have led to detailed characterization of relationships between structure and function in the early visual system and have produced sophisticated methods to quantify visual perception and characterize its neural substrates. We present a selective review of research that illustrates the opportunities for discovery provided by visual studies in schizophrenia. We highlight work that has been particularly effective in applying vision science methods to identify specific neural abnormalities underlying information processing deficits in schizophrenia. In addition, we describe studies that have utilized psychophysical experimental designs that mitigate generalized deficit confounds, thereby revealing specific visual impairments in schizophrenia. These studies contribute to accumulating evidence that early visual cortex is a useful experimental system for the study of local cortical circuit abnormalities in schizophrenia. The high degree of similarity across neocortical areas of neuronal subtypes and their patterns of connectivity suggests that insights obtained from the study of early visual cortex may be applicable to other brain regions. We conclude with a discussion of future studies that combine vision science and neuroimaging methods. These studies have the potential to address pressing questions in schizophrenia, including the dissociation of local circuit deficits vs. impairments in feedback modulation by cognitive processes such as spatial attention and working memory, and the relative contributions of glutamatergic and GABAergic deficits.

Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

PubMed

Orbán, Levente L; Chartier, Sylvain

2015-01-01

Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

The neural basis of episodic memory: evidence from functional neuroimaging.

PubMed Central

Rugg, Michael D; Otten, Leun J; Henson, Richard N A

2002-01-01

We review some of our recent research using functional neuroimaging to investigate neural activity supporting the encoding and retrieval of episodic memories, that is, memories for unique events. Findings from studies of encoding indicate that, at the cortical level, the regions responsible for the effective encoding of a stimulus event as an episodic memory include some of the regions that are also engaged to process the event 'online'. Thus, it appears that there is no single cortical site or circuit responsible for episodic encoding. The results of retrieval studies indicate that successful recollection of episodic information is associated with activation of lateral parietal cortex, along with more variable patterns of activity in dorsolateral and anterior prefrontal cortex. Whereas parietal regions may play a part in the representation of retrieved information, prefrontal areas appear to support processes that act on the products of retrieval to align behaviour with the demands of the retrieval task. PMID:12217177

A Framework of Hyperspectral Image Compression using Neural Networks

DOE PAGES

Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; ...

2015-01-01

Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows usmore » to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.« less

3D hybrid electrode structure as implantable interface for a vestibular neural prosthesis in humans.

PubMed

Hoffmann, Klaus-P; Poppendieck, Wigand; Tätzner, Simon; DiGiovanna, Jack; Kos, Maria Izabel; Guinand, Nils; Guyot, Jean-P; Micera, Silvestro

2011-01-01

Implantable interfaces are essential components of vestibular neural prostheses. They interface the biological system with electrical stimulation that is used to restore transfer of vestibular information. Regarding the anatomical situation special 3D structures are required. In this paper, the design and the manufacturing process of a novel 3D hybrid microelectrode structure as interface to the human vestibular system are described. Photolithography techniques, assembling technology and rapid prototyping are used for manufacturing.

Clues to the Foundations of Numerical Cognitive Impairments: Evidence From Genetic Disorders

PubMed Central

Simon, Tony J.

2011-01-01

Several neurodevelopmental disorders of known genetic etiology generate phenotypes that share the characteristic of numerical and mathematical cognitive impairments. This article reviews some of the main findings that suggest a possible key role that spatial and temporal information processing impairments may play in the atypical development of numerical cognitive competence. The question of what neural substrate might underlie these impairments is also addressed, as are the challenges for interpreting neural structure/cognitive function mapping in atypically developing populations. PMID:21761998

Neural network for interpretation of multi-meaning Chinese words

NASA Astrophysics Data System (ADS)

He, Qianhua; Xu, Bingzheng

1994-03-01

We proposed a neural network that can interpret multi-meaning Chinese words correctly by using context information. The self-organized network, designed for translating Chinese to English, builds a context according to key words of the processed text and utilizes it to interpret multi-meaning words correctly. The network is generated automatically basing on a Chinese-English dictionary and a knowledge-base of weights, and can adapt to the change of contexts. Simulation experiments have proved that the network worked as expected.

Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series

PubMed Central

Vicente, Raul; Díaz-Pernas, Francisco J.; Wibral, Michael

2014-01-01

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems. PMID:25068489

Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

PubMed Central

Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Churchland, Mark M.; Cunningham, John P.; Shenoy, Krishna V.

2015-01-01

Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. PMID:26220660

Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.

PubMed

Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp

2017-04-01

According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Age-induced differences in brain neural activation elicited by visual emotional stimuli: A high-density EEG study.

PubMed

Tsolaki, Anthoula C; Kosmidou, Vasiliki E; Kompatsiaris, Ioannis Yiannis; Papadaniil, Chrysa; Hadjileontiadis, Leontios; Tsolaki, Magda

2017-01-06

Identifying the brain sources of neural activation during processing of emotional information remains a very challenging task. In this work, we investigated the response to different emotional stimuli and the effect of age on the neuronal activation. Two negative emotion conditions, i.e., 'anger' and 'fear' faces were presented to 22 adult female participants (11 young and 11 elderly) while acquiring high-density electroencephalogram (EEG) data of 256 channels. Brain source localization was utilized to study the modulations in the early N170 event-related-potential component. The results revealed alterations in the amplitude of N170 and the localization of areas with maximum neural activation. Furthermore, age-induced differences are shown in the topographic maps and the neural activation for both emotional stimuli. Overall, aging appeared to affect the limbic area and its implication to emotional processing. These findings can serve as a step toward the understanding of the way the brain functions and evolves with age which is a significant element in the design of assistive environments. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Comparison of RF spectrum prediction methods for dynamic spectrum access

NASA Astrophysics Data System (ADS)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

Source Reconstruction of Brain Potentials Using Bayesian Model Averaging to Analyze Face Intra-Domain vs. Face-Occupation Cross-Domain Processing

PubMed Central

Olivares, Ela I.; Lage-Castellanos, Agustín; Bobes, María A.; Iglesias, Jaime

2018-01-01

We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs) in both intra-domain (face-feature) and cross-domain (face-occupation) matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively. A novel Bayesian source reconstruction approach plus conjunction analysis of group effects revealed that in both cases the generated N170s were of similar amplitude but had different neural origin. Thus, whereas the N170 of faces was associated predominantly to right fusiform and occipital regions (the so-called “Fusiform Face Area”, “FFA” and “Occipital Face Area”, “OFA”, respectively), the N170 of occupations was associated to a bilateral very posterior activity, suggestive of basic perceptual processes. Importantly, the right-sided perceptual P200 and the face-related N250 were evoked exclusively in the intra-domain task, with sources in OFA and extensively in the fusiform region, respectively. Regarding later latencies, the intra-domain N400 seemed to be generated in right posterior brain regions encompassing mainly OFA and, to some extent, the FFA, likely reflecting neural operations triggered by structural incongruities. In turn, the cross-domain N400 was related to more anterior left-sided fusiform and temporal inferior sources, paralleling those described previously for the classic verbal N400. These results support the existence of differentiated neural streams for face structure and person-related verbal processing triggered by faces, which can be activated differentially according to specific task demands. PMID:29628877

Closing the Loop for Memory Prostheses: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

PubMed Central

Hampson, Robert E.; Song, Dong; Chan, Rosa H.M.; Sweatt, Andrew J.; Riley, Mitchell R.; Goonawardena, Anushka V.; Marmarelis, Vasilis Z.; Gerhardt, Greg A.; Berger, Theodore W.; Deadwyler, Sam A.

2012-01-01

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry. PMID:22498704

Modification of a neuronal network direction using stepwise photo-thermal etching of an agarose architecture.

PubMed

Suzuki, Ikurou; Sugio, Yoshihiro; Moriguchi, Hiroyuki; Jimbo, Yasuhiko; Yasuda, Kenji

2004-07-01

Control over spatial distribution of individual neurons and the pattern of neural network provides an important tool for studying information processing pathways during neural network formation. Moreover, the knowledge of the direction of synaptic connections between cells in each neural network can provide detailed information on the relationship between the forward and feedback signaling. We have developed a method for topographical control of the direction of synaptic connections within a living neuronal network using a new type of individual-cell-based on-chip cell-cultivation system with an agarose microchamber array (AMCA). The advantages of this system include the possibility to control positions and number of cultured cells as well as flexible control of the direction of elongation of axons through stepwise melting of narrow grooves. Such micrometer-order microchannels are obtained by photo-thermal etching of agarose where a portion of the gel is melted with a 1064-nm infrared laser beam. Using this system, we created neural network from individual Rat hippocampal cells. We were able to control elongation of individual axons during cultivation (from cells contained within the AMCA) by non-destructive stepwise photo-thermal etching. We have demonstrated the potential of our on-chip AMCA cell cultivation system for the controlled development of individual cell-based neural networks.

ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

PubMed Central

Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

2012-01-01

Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans.

PubMed

Farzmahdi, Amirhossein; Rajaei, Karim; Ghodrati, Masoud; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2016-04-26

Converging reports indicate that face images are processed through specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches.